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Deep Learning Use Cases

Deep Learning (DL) has become more than just a buzzword in the Artificial Intelligence (AI) community – it is reshaping global business through the prolific use of autonomous, self-teaching systems, which can build models by directly studying images, text, audio, or video data. Such systems can use that data for future pattern recognition. According to […]

According to many technical professionals, businesses can reap the full benefits of AI only when the appropriate levels of competency is developed in advanced data technologies such as Machine Learning (ML) and Deep Learning for extracting reliable business insights. This unilateral opinion among professionals implies that the skill gaps have to be identified and training must be in place to make the best use of available technologies and tools.

The Gartner article Artificial Intelligence and the Enterprise indicates an urgent need to develop teams of highly skilled Data Science expert  “who can manage the complexity of data, analytical methods and machine learning associated with AI, and help apply it with workers, customers and constituents.”

AI Analytics Solutions: How Much is Really Deep?

In How to Tell When Vendors Are Hyping AI Capabilities , the author discusses how an increasing number of technology vendors in the market continue to claim that they have value-added AI capabilities in their solutions. But, the real value of AI in the enterprise is getting side-tracked by market hype.

The purchase decision for business consumers gets tougher when they have to compare and choose the most suitable AI solution (whether based on ML or DL ) for their specific Analytics or BI needs. Customers need to be more focused and ask questions about managing risks, monitoring performance, and extracting the right business benefits from a given solution.

The Deep Learning revolution began from a need to build “high-accuracy predictive models” from unstructured data such as images, voice, and natural language. The ultimate push came from the four giants in the IT industry (Facebook, Google, Microsoft, and IBM) who were all out to win the AI in the enterprise race by leveraging their Deep Learning technology development strategy.

Now the revolution has reached the proportion of a gigantic disruptive wave, which has also created further development opportunities for solution vendors. The Forrester Report Deep Learning: an AI Revolution Started for Courageous Enterprises shows how the Deep Learning storm in the market inspired enterprises to embrace advanced AI research for their Analytics and BI ever-growing requirements.

According to recent Tractica report on Deep Learning , the DL software market will expand from “$655 million in 2016 to $34.9 billion worldwide by 2025.” Moreover, this report suggested that the top 10 Deep Learning use cases in terms of potential for revenue generation are:

“(1) Static image recognition, classification, and tagging; (2) Machine/vehicular object detection/identification/avoidance; (3) Patient data processing; (4) Algorithmic trading strategy performance improvement; (5) Converting paperwork into digital data; (6) Medical image analysis; (7) Localization and mapping; (8) Sentiment analysis; (9) Social media publishing and management; (10) Intelligent recruitment and HR systems.”

Factors that Differentiate Deep Learning from Other AI Technologies

One of the primary drivers of Deep Learning is that it can crunch much more data at very high speeds. DL techniques have become necessary for successful pattern recognition in large unstructured data. So, two major factors that differentiate Deep Learning from other AI technologies: Largeness of training data and direct analysis of unstructured data.

In traditional Data Modeling, a “labeled data set” is used to train a model with an algorithm, and then the model is expected to accurately predict new data sets in future, based on that learning. Deep Learning takes this learning process one step ahead by directly working with images, audio, or video data without the data going through any kind of initial preparation.

The Data Scientist just tells the DL algorithm what to look for and then, the algorithm does it all. This unique capability of DL is known as “feature engineering,” which helps the DL algorithm to directly focus on the right features or distinguishing elements of particular data without any intervention from a technical expert.

Thus, in case of DL, Data Science staff members are not required for training data models to recognize pattern in images, audio, or video data. However, the major drawback in DL is going through an unlimited number of permutations to make the feature engineering process accurate.

Another drawback is that for Deep Learning processes to work, very high-end supercomputers handling billions of high-speed mathematical computations are required. One answer to this processing need has been provided by NVIDIA’s GPU system and open source Deep Learning libraries. NDVIDIA has helped to make DL research and development affordable for enterprises.

Applications in Businesses: Deep Learning Use Cases

Deep Learning algorithms are becoming more widely used in every industry sector from online retail to photography; some use cases are more popular and have attracted extra attention of global media than others. Some widely publicized Deep Learning applications include:

• Speech recognition used by Amazon Alexa, Google, Apple Siri, or Microsoft Cortana.
• Image recognition used for analyzing documents and pictures residing on large databases.
• Natural Language Processing (NLP) used for negative sampling, sentiment analysis, machine translation, or contextual entity linking.
• Automated drug discovery and toxicology used for drug design and development work, as well as for predictive diagnosis of diseases.
• CRM activities used for automated marketing practices.
• Recommendation engines used in a variety of applications.
• Predictions in gene ontology and gene-function relationships.
• Health predictions based on data collected from wearables and EMRs.

The Computer World article Deep Learning Use Cases for ASEAN describes how DL algorithms can be used to aid traffic management in ASEAN member countries.

Deep Learning Success in the Enterprise

Deep Learning use cases have been widely used for knowledge discovery and Predictive Analytics . For example, Google uses DL to build powerful voice- and image-recognition algorithms. Netflix and Amazon use DL in their recommendation engines, and MIT researchers use DL for Predictive Analytics.

According to the NVIDIA article Deep Learning Success Stories , Deep Learning case studies are easily found in many enterprises. Soon, DL solutions will be used in ways no one thought was possible. Right now, we can witness successful DL applications behind self-driving cars, automated web services like recommendation engines, and smart assistants.

Here are some business-specific, Deep Learning use cases:

• Canary : a NY-based DL startup has their vision set on the world’s first smart home security device, which comprises of an HD video camera and sensors for tracking temperature, sound, vibration, air quality, and movement. This device can be controlled by a smartphone. A product video is available here .
• Atomwise : Another startup applies Deep Learning technology to drug discovery. This solution uses DL networks to help discover new medicines and to explore avenues for repurposing known and tested drugs for use against new diseases.
• ViSenze : Provides tools to simplify image search and categorization through an API. The solution uses DL networks to power image recognition and categorization.
• Bay Labs : A startup devoted to medical imaging technology has used DL for medical diagnosis and disease management. They have their eyes on both the developed and developing nations with a firm vision to improve the quality of global healthcare.
• Knit Health : Promises to help people with sleeping disorders. They have combined computer vision and DL technologies to provide “personalized insights and suggestions” related to sleep management.
• CarePredict : Provides a senior care platform to enable timely intervention and monitoring of medical conditions that may have been overlooked by close family members or friends. The ultimate goal of this solution is to provide timely detection of preventable health conditions. A video of CarePredict is available here .
• BenchSci : Is an Machine Learning research tool, which aids biomedical researchers to locate the best biological compounds for their experiments. This solution grew out of a highly publicized need for finding a tool to quickly glean millions of research publications for locating the most suitable antibodies for particular experiments. A video is available here .

You will find more examples of Deep Learning use cases in Deep Learning Startups, Use Cases, and Books ,

Deep Learning has pervaded the global business landscape, capturing the undivided attention of industry giants like IBM, Facebook, Google, Microsoft, Twitter, PayPal, or Yahoo, among others. It’s quite clear that large or small companies alike are making heavy investments into Deep Learning technologies , as they all think such advances will be core drivers of enterprise growth far into the future.

Photo Credit: hanss/Shutterstock.com

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What is Deep Learning? Use Cases, Examples, Benefits in 2024

Deep learning is a state-of-the-art field in machine learning domain. Deep learning models can learn from examples and they need to be trained with sufficient data. The predictions of deep learning algorithms can boost the performance of businesses. However, they have challenges such as being data hungry, hard to interpret and can be expensive due to the cost of collecting and labelling data.

What is deep learning?

Deep learning, also called deep structured learning or hierarchical learning, is a set of machine learning methods which is part of the broader family of artificial neural network based machine learning methods. Like other machine learning methods, deep learning allows businesses to predict outcomes. A simple example is to predict which customers are likely to buy if they receive discounted offers. Improved models allow businesses to save costs and increase sales.

Deep learning is one of the most popular machine learning methods in commercial applications and interest in deep learning has exploded since 2013 as you can see below.

Why is deep learning relevant now?

While  Geoffrey Hinton and other researchers started to demonstrate deep learning’s potential in 1980s effectiveness, several elements were missing:

• Cheap computing power  is required by deep learning. Enough amounts of economical computing power for deep learning applications only became available around 2010s.
• Training data : Researchers used to rely on hand-labeled data for machine learning. However, data generation has increased significantly with new data per year  doubling  every 2 years since 2010s. Currently, new data generation and storage is expected to grow with a CAGR of 23% until 2025.
• Better algorithms : Years of research also led to more optimized algorithms, further enabling deep learning.

Modern companies armed with an abundance of data, cheap computing power and modern deep learning algorithms are set to take advantage of deep learning models.

How does deep learning work?

Based on training dataset, an Artificial Neural Network (ANN) based model is built and tested against a test dataset to make predictions on your business’ data. Let’s explain each term:

Training data : As its name implies, machine learning is all about learning from previous examples. Training data includes both data that is and will be known, as well as the outcome that needs to be predicted. For example, let’s assume that we are trying to predict which customers are likely to buy if they receive discounted offers. In this case,

• Known data (or input data) is all relevant data about the customer which can include demographic data, previous purchases, online behavior, etc.
• The outcome to be predicted is whether the customer will make a purchase after receiving the offer.

Artificial Neural Network (ANN)  is a mathematical model with a structure inspired by brain’s neural circuitry. Though its structure may be complex, it is essentially a function that makes predictions given input variables. We use the word “inspired” because brain’s structure is quite complex compared to even the most complex neural networks, is analog and highly optimized closely coupling processing, computation and software.

Test dataset  is not used as part of the training. It has the same format as training data and it is used to test the model’s results and decide whether model’s predictions are accurate enough for the busines goals.

Predictions  are outputs of the model. When trying to predict which customers are likely to buy if they receive discounted offers, the model predicts an outcome (will buy, will not buy) for each customer in the dataset. The company can use these predictions to decide which customers to reach out. Furthermore, model can assign a confidence score to each prediction, helping the company further refine the actions it will take. For example, if an incorrect prediction is costlier than a correct prediction, the company may not act on a prediction if the confidence level of the model for that data point is low.

What are its benefits?

Deep learning models can lead to better, faster and cheaper predictions which lead to better business, higher revenues and reduced costs.

• Better predictions:  Which business wouldn’t want to be able to call just the customers who are ready to buy or keep just the right amount of stock? All of these decisions can be improved with better predictions.
• Faster predictions:  Deep learning, and machine learning in general, automates a company’s decision making increasing its execution speed. Consider customers that leave their contact info to get more details about a tech solution for their company. Maybe it is obvious from the contact info that this is a very high potential and needs to be contacted. Thanks to the model in place, no one needs to manually check that data, the potential customer will be immediately prioritized. Speed is especially important in this example because customers contacted sooner are more likely to convert.

• Cheaper predictions:  Companies that do not implement operational decision making models, rely on analysts to make decisions which are orders of magnitude costlier than running deep-learning models. However, deep learning models also have setup time and costs. Therefore, the business case for models need to be investigated before rolling out models.

What are its important use cases?

Deep learning is a machine learning technique so its areas of applications are almost limitless. However, business benefit of a model need to be compared with the cost of setting up such a model.

Any business application would benefit from better predictions. After all, life is the decisions we make and our decisions are as good as our predictions. Examples of applications include:

• Image classification: From recognizing customers who enter the store to automatically identifying defects, image classification applications exist in almost all industries
• Other predictions: Predicting churn in marketing, likelihood to buy in sales, customer’s emotional state from her voice in customer service contact centres are all some of the applications of deep learning

Models have widespread applications areas but also have setup time and costs. Therefore, the business case for models need to be investigated before rolling out models. In short, areas where models provide the best value are:

• Valuable predictions where machines outperform humans. Soon, medical image analysis could be within this domain as for example a cancer diagnosis is quite valuable and machines could be doing better than humans in the near future.
• Lower value predictions that need to be repeated often. Most machine learning models tend to fall into this category. Going through millions of customers to identify the right customers for a campaign is too costly without having a model to pick the right customers.

Industries with the most data are likely to benefit the most from deep learning models. If you want to read more about deep learning use cases in different industries:

• Top 41 Deep Learning Use Cases
• Deep Learning Use Cases in Finance
• Deep Learning Use Cases in Healthcare
• Especially companies leveraging Industrial Internet of Things (IIoT) have access to copious amounts of data which can power deep learning models. Feel free to explore Deep Learning Use Cases in Manufacturing

Which business functions benefit the most from deep learning?

Business functions with more data are likely to benefit more from deep learning. Some data-rich business functions are:

• Commercial functions such as sales, marketing and customer service
• Cost centers such as technology that create detailed log files including granular data

How is deep learning expected to evolve in the future?

Deep learning domain is expected to gain new capabilities and overcome its challenges with new research and studies such as capsule networks and adversarial learning. Feel free to read our article about future of deep learning .

What are the challenges in deep learning?

Deep learning models have challenges such as

• Data privacy/consumer data protection: Deep learning algorithms rely on training data which may include personal or sensitive data. Personal data in training datasets may be demographic information, income, health, interests, etc. This raises concerns about privacy in deep learning applications. However, by encrypting models, companies are able to protect personal data stored in models
• Data hungry
• Hard to explain or interpret
• High energy costs

Researchers and industry pioneers have some ideas to overcome these barriers including:

• Minimize use of personal data in models by learning from fewer examples as in the case of few shot learning .
• Provide explanations about the predictions of deep learning models by developing new models following XAI approaches
• Prevent bias in deep learning with multiple approaches like introducing more diversity in the field
• Improving efficiency of deep learning models to accelerate them and reduce deployment and hardware costs. For example, there is significant effort to build better AI chips
• Taking steps to reduce the skill shortage in deep learning domain. Currently deep learning models are hard to build and data science professionals are needed to build advanced models. However, that is changing as companies adopt no code AI solutions .

You can also check our article on deep learning challenges and ways to overcome them .

If you are ready to use deep learning in your firm, we prepared a data driven list of companies offering deep learning platforms .

If you need help in choosing among deep learning vendors who can help you get started, let us know:

This article was drafted by former AIMultiple industry analyst Ayşegül Takımoğlu.

Cem has been the principal analyst at AIMultiple since 2017. AIMultiple informs hundreds of thousands of businesses (as per similarWeb) including 60% of Fortune 500 every month. Cem's work has been cited by leading global publications including Business Insider , Forbes, Washington Post , global firms like Deloitte , HPE, NGOs like World Economic Forum and supranational organizations like European Commission . You can see more reputable companies and media that referenced AIMultiple. Throughout his career, Cem served as a tech consultant, tech buyer and tech entrepreneur. He advised businesses on their enterprise software, automation, cloud, AI / ML and other technology related decisions at McKinsey & Company and Altman Solon for more than a decade. He also published a McKinsey report on digitalization. He led technology strategy and procurement of a telco while reporting to the CEO. He has also led commercial growth of deep tech company Hypatos that reached a 7 digit annual recurring revenue and a 9 digit valuation from 0 within 2 years. Cem's work in Hypatos was covered by leading technology publications like TechCrunch and Business Insider . Cem regularly speaks at international technology conferences. He graduated from Bogazici University as a computer engineer and holds an MBA from Columbia Business School.

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• Open access
• Published: 12 November 2020

Deep learning accelerators: a case study with MAESTRO

• Hamidreza Bolhasani   ORCID: orcid.org/0000-0003-0698-6141 1 &
• Somayyeh Jafarali Jassbi 1  

Journal of Big Data volume  7 , Article number:  100 ( 2020 ) Cite this article

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In recent years, deep learning has become one of the most important topics in computer sciences. Deep learning is a growing trend in the edge of technology and its applications are now seen in many aspects of our life such as object detection, speech recognition, natural language processing, etc. Currently, almost all major sciences and technologies are benefiting from the advantages of deep learning such as high accuracy, speed and flexibility. Therefore, any efforts in improving performance of related techniques is valuable. Deep learning accelerators are considered as hardware architecture, which are designed and optimized for increasing speed, efficiency and accuracy of computers that are running deep learning algorithms. In this paper, after reviewing some backgrounds on deep learning, a well-known accelerator architecture named MAERI (Multiply-Accumulate Engine with Reconfigurable interconnects) is investigated. Performance of a deep learning task is measured and compared in two different data flow strategies: NLR (No Local Reuse) and NVDLA (NVIDIA Deep Learning Accelerator), using an open source tool called MAESTRO (Modeling Accelerator Efficiency via Spatio-Temporal Resource Occupancy). Measured performance indicators of novel optimized architecture, NVDLA shows higher L1 and L2 computation reuse, and lower total runtime (cycles) in comparison to the other one.

Introduction

The main idea of neural networks (NN) is based on biological neural system structure, which consists of several connected elements named neurons [ 1 ]. In biological systems, neurons get signals from dendrites and pass them to the next neurons via axon as shown in Fig.  1 .

Typical biological neurons [ 20 ]

Neural networks are made up of artificial neurons for handling brain tasks like learning, recognition and optimization. In this structure, the nodes are neurons, links can be considered as synapses and biases as activation thresholds [ 2 ]. Each layer extracts some information related to the features and forwards them with a weight to the next layer. Output is the sum of all these information gains multiplied by their related weights. Figure  2 represents a simple artificial neural network structure.

Simple artificial neural network structure

Deep neural networks are complex artificial neural networks with more than two layers. Nowadays, these networks are widely used for several scientific and industrial purposes such as visual object detection, segmentation, image classification, speech recognition, natural language processing, genomics, drug discovery, and many other areas [ 3 ].

Deep learning is a new subset of machine learning including algorithms that are used for learning concepts in different levels, utilizing artificial neural networks [ 4 ].

As Fig.  3 shows, if each neuron and its weight are represented by X i and W i j respectively, the output result (Y j ) would be:

A typical deep neural network structure

where \(\sigma\) is the activation function. A popular function that is used for activation in deep neural networks is ReLU (Rectified Linear Unit) function, which is defined in Eq. ( 2 ).

Leaky ReLU, tanhh and Sigmoid functions are some other activation functions with less frequent usage [ 5 ].

As shown in Fig.  4 , each layer of a deep neural network’s role is to extract some features and send them to the next layer with its corresponding weight. For example, in the first layer, color properties (green, red blue) are gained; in the next layer, edge of objects are determined and so on.

Deep learning setup for object detection [ 21 ]

Convolutional neural networks are a type of deep neural networks that is mostly used for recognition, mining and synthesis applications like face detection, handwritting recognition and natural language processing [ 6 ]. Since parallel computations is an unavoidable part of CNNs, several efforts and research works have been done for designing an optimized hardware for it. As a result, many application-specific integrated circuits (ASICs) as hardware accelerators have been introduced and evaluated in the recent decade [ 7 ]. In the next section, some of the most successful and impressive works related to CNN accelerators are introduced.

Related works

Tianshi et al. [ 8 ] proposed DianNao as a hardware accelerator for large-scale convolutional neural networks (CNNs) and deep neural networks (DNNs). The main focus of the suggested model is on the memory structure to be optimized for big neural network computations. The experimental results showed speedup in computation and reduction of overhead in performance and energy. This research also demonstrated that the accelerator can be implemented in very small area in order of 3 mm 2 and 485 mW power.

Zidong et al. [ 9 ] suggested ShiDianNao as a CNN accelerator for image processing close to a CMOS or CCD sensor. The performance and energy of this architecture is compared to CPU, GPU and DainNao, which has been discussed in previous work [ 8 ]. Utilizing SRAM instead of DRAM made it 60 times more enery effiecent than DianNao. It is also 50×, 30× and 1.87× faster than a mainstream CPU, GPU and DianNao, with just 65 nm usage area and 320 mW power.

Wenyan et al. [ 6 ] offered a flexible dataflow accelerator for convolutional neural networks called FlexFlow. Working on different types of parallelism is the substantial contribution of this model. Results of the tests showed 2–10 × performance speedup and 2.5–10 × power efficiency in comparison with three investigated baseline architectures.

Eyriss is a spatial architecture for energy efficient data flows for CNNs which presented by Yu-Hsin et al. [ 10 ]. This hardware model is based on a dataflow named row stationary (RS). This dataflow minimizes energy consumption by reusing computation of filter weights. The proposed RS dataflow is investigated on AlexNet CNN configuration, which proved energy efficiency improvement.

Morph is a flexible accelerator for 3D CNN-based video processing that offered by Katrik et al. [ 7 ]. Since the previous work and proposed architectures didn’t specificly focus on video processing, this model can be considered as a novelty in this area. Comparison of energy consumption in this architecture with previous idea, Eyriss [ 10 ] showed a high level of reduction that means energy saving. The main reason of this improvement is effective data reuse which reduces the access to higher level buffers and high cost off-cheap memory.

Michael et al. [ 11 ] described Buffets that is an efficient and composable accelerator and independent of any particular design. Through this research, explicit decoupled data orchestration (EDDO) is introduced which allows evaluation of energy efficiency in acceleators. Result of this work showed that with a smaller usage area, higher energy efficiency and lower control overhead is acquired.

Deep learning applications

Deep learning has a wide range of applications in recognition, classification and prediction, and since it tends to work like the human brain and consequently does the human jobs in a more accurate and low cost manner, its usage is dramatically increasing. More than 100 papers published from 2015 to 2020, helped categorize the main applications as below:

Computer vision

Translation

Health monitoring

Disease prediction

Medical image analysis

Drug discovery

Biomedicine

Bioinformatics

Smart clothing

Personal health advisors

Pixel restoration for photos

Sound restoration in videos

Describing photos

Handwriting recognition

Predicting natural disasters

Cyber physical security systems [ 12 ]

Intelligent transportation systems [ 13 ]

Computed tomography image reconstruction [ 14 ]

As mentioned previously, artificial intelligence and deep learning applications are growing drastically, but they have high complexity computation, energy consumption, costs and memory bandwidth. All these reasons were major motivations for developing deep learning accelerators (DLA) [ 15 ]. A DLA is a hardware architecture that is specially designed and optimized for deep learning purposes. Recent DLA architectures (e.g. OpenCL) have mainly focused on maximizing computation reuse and minimizing memory bandwidth, which led to higher speed and performance [ 16 ].

Generally, most of the accelerators support just fixed data flow and are not reconfigurable, but for doing huge deployments, they need to be programmable. Hyoukjun et al. [ 15 ] proposed a novel architecture named MAERI (Multiply-Accumulate Engine with Reconfigurable Interconnects), which is reconfigurable and employs ART (Augmented Reduction Tree) which showed 8 ~ 459% better utilization for different data flows over a strict network-on-chip (NoC) fabric. Figure  5 shows the overall structure of MAERI DLA.

MAERI micro architecture [ 15 ]

In another research, Hyoukjun et al. offered a framework called “MAESTRO” (Modeling Accelerator Efficiency via Spatio-Temporal Resource Occupancy) for predicting energy performance and efficiency in DLAs [ 17 ]. MAESTRO is an open-source tool that is capable of computing many NoC parameters for a proposed accelerator and related data flow such as maximum performance (roofline throughput), compute runtime, total runtime, NoC analysis, L1 to L2 NoC bandwidth, L2 to L1 bandwidth analysis, buffer analysis, L1 and L2 computation reuse, L1 and L2 weight reuse, L1 and L2 input reuse and so on. The topology, tool flow and relationship between each of its blocks of this framework are presented in Fig.  6 .

MAESTRO topology [ 15 ]

Results and discussion

In this paper, we used MAESTRO to investigate buffer, NoC, and performance parameters of a DLA in comparison to a classical architecture for a specific deep learning data flow. For running MAESTRO and getting the related analysis, some parameters should be configured, as follows:

LayerFile: Including the information related to the layers of neural network.

DataFlow File: Information related to data flow.

Vector Width: Width of the vectors.

NoCBand width: Bandwidth of NoC.

Multicast Supported: This logical indictor (True/False) is for defining that the NoC supports multicast or not.

NumAverageHopsinNoC: Average number of hops in the NoC.

NumPEs: Number of processing elements.

For the simulation of this paper, we configured the mentioned parameters as presented in Table 1 .

As presented in Table 1 , we have selected Vgg16_conv11 as LayerFile, which is a convolutional neural network that has proposed by K. Simonyan and A. Zisserman. This deep convolutional network model was offered for image recognition with 92.7% accuracy on ImageNet dataset [ 18 ].

Two different data flow strategies are investigated and compared in this study: NLR and NVDLA. NLR stands for “No Local Reuse” which expresses its specific strategy and NVDLA is a novel DLA designed by NVIDIA Co [ 19 ].

Other parameters such as vector width, NoC bandwidth, multicast support capability, average numbers of hops and numbers of processing elements in NoC have been selected based on a real hardware condition.

Simulation results demonstrated that NVDLA has better performance, runtime, higher computation reuse and lower memory bandwidth in comparison to NLR as presented in Table 2 and Figs. 7 , 8 , and 9 .

Comparing L1 Weight and Input Reuse

Comparing L2 Weight and Input Reuse

Total Runtime comparison

Artificial intelligence, machine learning and deep learning are growing trends affecting our lives in almost all aspects of human’s life. These technologies make our life easier by assigning routine tasks of human resources to the machines that are much more accurate and fast. Therefore, any effort for optimizing performance, speed, and accuracy of these technologies is valuable. In this research, we focused on performance improvements of the hardware that are used for deep learning purposes named deep learning accelerators. Investigating recent researches conducted on these hardware accelerators shows that they can optimize costs, energy consumption, run time about 8–459% based on MAERI’s investigation by minimizing memory bandwidth and maximizing computation reuse. Utilizing an open source tool named MAESTRO, we compared buffer, NoC and performance parameters of NLR and NVDLA data flows. Results showed higher computation reuse for both L1 and L2 of the NVDLA data flow which is designed and optimized for deep learning purposes and studied as deep leraning accelerator in this study. The results showed that the customized hardware accelartor for deep learning (NVDLA) had much shorter total runtime in comparison with NLR.

Availability of data and materials

Abbreviations.

Multiply-accumulate engine with reconfigurable interconnects

No local reuse

NVIDIA deep learning accelerator

Modeling accelerator efficiency via spatio-temporal resource occupancy

Rectified linear unit

• Deep learning accelerator

Neural network

Convolutional neural network

Deep neural network

Row stationary

Application-specific integrated circuits

Augmented reduction tree

Network on chip

L1 read sum

L1 write sum

L2 read sum

L2 write sum

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Investigating deep learning accelerators functionality. Analyzing a deep learning accelerator’s architecture. Performance measurement of NVIDIA deep learning accelerator as a case study. Higher computation reuse and lower total runtime for the studied deep learning accelerator in comparison with non-optimized architecture.

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Bolhasani, H., Jassbi, S.J. Deep learning accelerators: a case study with MAESTRO. J Big Data 7 , 100 (2020). https://doi.org/10.1186/s40537-020-00377-8

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Language Models for Deep Learning Programming: A Case Study with Keras

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This chapter explores the application of language models to programming languages and our work in constructing a dataset for the task. More particularly, we focus on the Keras programming language, a popular framework for implementing Deep Learning experiments. Our original model KerasBERT has since been expanded by adding more data and re-training the language model. The original KerasBERT model was trained on two categories of Keras Code Examples and the Keras API reference. This chapter documents adding Keras GitHub Examples, Kaggle Notebook containing Keras Code, Medium articles describing how to use Keras, and StackOverflow questions regarding Keras. With these new data sources, we present new domain generalization analysis, as well as independent and identically distributed (i.i.d.) test set losses. We qualitatively evaluate how well KerasBERT learns the Keras Deep Learning framework through cloze test evaluation. We present miscellaneous properties of these cloze tests such as mask positioning and prompt paraphrasing. KerasBERT is an 80 million parameter RoBERTa model, which we compare to the Zero-Shot learning capability of the 6 billion parameter GPT-Neo model. We present a suite of cloze tests crafted from the Keras documentation to evaluate these models. We find some exciting completions that show KerasBERT is a promising direction for question answering and schema-free database querying. In this chapter, we document the reuse of KerasBERT and integration of additional data sources. We have tripled the size of the original data set and have identified five main sources of Keras information data. With these sources, we present new analyses of how models generalize to novel sources of data, such as a language model trained on Kaggle notebooks and tested on GitHub code. We conclude our work by discussing some future directions for KerasBERT and the development of language models for code documentation support.

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Shorten, C., Khoshgoftaar, T.M. (2023). Language Models for Deep Learning Programming: A Case Study with Keras. In: Wani, M.A., Palade , V. (eds) Deep Learning Applications, Volume 4. Advances in Intelligent Systems and Computing, vol 1434. Springer, Singapore. https://doi.org/10.1007/978-981-19-6153-3_6

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Deep Learning for Recommender Systems: A Netflix Case Study

• Harald Steck Netflix
• Linas Baltrunas Netflix
• Ehtsham Elahi Netflix
• Dawen Liang Netflix
• Yves Raimond Netflix
• Justin Basilico Netflix

Deep learning has profoundly impacted many areas of machine learning. However, it took a while for its impact to be felt in the field of recommender systems. In this article, we outline some of the challenges encountered and lessons learned in using deep learning for recommender systems at Netflix. We first provide an overview of the various recommendation tasks on the Netflix service. We found that different model architectures excel at different tasks. Even though many deep-learning models can be understood as extensions of existing (simple) recommendation algorithms, we initially did not observe significant improvements in performance over well-tuned non-deep-learning approaches. Only when we added numerous features of heterogeneous types to the input data, deep-learning models did start to shine in our setting. We also observed that deep-learning methods can exacerbate the problem of offline–online metric (mis-)alignment. After addressing these challenges, deep learning has ultimately resulted in large improvements to our recommendations as measured by both offline and online metrics. On the practical side, integrating deep-learning toolboxes in our system has made it faster and easier to implement and experiment with both deep-learning and non-deep-learning approaches for various recommendation tasks. We conclude this article by summarizing our take-aways that may generalize to other applications beyond Netflix.

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Title: challenges and practices of deep learning model reengineering: a case study on computer vision.

Abstract: Many engineering organizations are reimplementing and extending deep neural networks from the research community. We describe this process as deep learning model reengineering. Deep learning model reengineering - reusing, reproducing, adapting, and enhancing state-of-the-art deep learning approaches - is challenging for reasons including under-documented reference models, changing requirements, and the cost of implementation and testing. In addition, individual engineers may lack expertise in software engineering, yet teams must apply knowledge of software engineering and deep learning to succeed. Prior work has examined on DL systems from a "product" view, examining defects from projects regardless of the engineers' purpose. Our study is focused on reengineering activities from a "process" view, and focuses on engineers specifically engaged in the reengineering process. Our goal is to understand the characteristics and challenges of deep learning model reengineering. We conducted a case study of this phenomenon, focusing on the context of computer vision. Our results draw from two data sources: defects reported in open-source reeengineering projects, and interviews conducted with open-source project contributors and the leaders of a reengineering team. Our results describe how deep learning-based computer vision techniques are reengineered, analyze the distribution of defects in this process, and discuss challenges and practices. Integrating our quantitative and qualitative data, we proposed a novel reengineering workflow. Our findings inform several future directions, including: measuring additional unknown aspects of model reengineering; standardizing engineering practices to facilitate reengineering; and developing tools to support model reengineering and model reuse.

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Deep learning case study interview

Many accomplished students and newly minted AI professionals ask us$:$ How can I prepare for interviews? Good recruiters try setting up job applicants for success in interviews, but it may not be obvious how to prepare for them. We interviewed over 100 leaders in machine learning and data science to understand what AI interviews are and how to prepare for them.

TABLE OF CONTENTS

• I What to expect in the deep learning case study interview
• II Recommended framework
• III Interview tips
• IV Resources

AI organizations divide their work into data engineering, modeling, deployment, business analysis, and AI infrastructure. The necessary skills to carry out these tasks are a combination of technical, behavioral, and decision making skills. Deep learning skills are sometimes required, especially in organizations focusing on computer vision, natural language processing, or speech recognition.

The deep learning case study interview focuses on technical and decision making skills, and you’ll encounter it during an onsite round for a Deep Learning Engineer (DLE), Deep Learning Researcher (DLR), or Software Engineer-Deep Learning (SE-DL) role. You can learn more about these roles in our AI Career Pathways report and about other types of interviews in The Skills Boost .

I   What to expect in the deep learning case study interview

The interviewer is evaluating your approach to a real-world deep learning problem. The interview is usually a technical discussion on an open-ended question. There is no exact solution to the question; it’s your thought process that the interviewer is evaluating. Here’s a list of interview questions you might be asked:

• How would you build a speech recognition system powering a virtual assistant like Amazon Alexa, Google Home, Apple Siri, and Baidu’s DuerOS?
• As a deep learning engineer, you are asked to build an object detector for a zoo. How would you get started?
• How would you build an algorithm that auto-completes your sentence when writing an email?
• In your opinion, what are technical challenges related to the deployment of an autonomous vehicle in a geofenced area?
• You built a computer vision algorithm that can detect pneumonia from chest X-rays. How would you convince a radiologist to use it?
• You are tackling the school dropout problem. How would you build a model that can determine whether a student is at-risk or not, and plan an intervention?

II   Recommended framework

All interviews are different, but the ASPER framework is applicable to a variety of case studies:

• Ask . Ask questions to uncover details that were kept hidden by the interviewer. Specifically, you want to answer the following questions: “what are the product requirements and evaluation metrics?”, “what data do I have access to?”, ”how much time and computational resources do I have to run experiments?”, ”how will the learning algorithm be used at test time, and does it need to be regularly re-trained?”.
• Suppose . Make justified assumptions to simplify the problem. Examples of assumptions are: “we are in small data regime”, “the data distribution won’t change over time”, “our model performs better than humans”, “labels are reliable”, etc.
• Plan . Break down the problem into tasks. A common task sequence in the deep learning case study interview is: (i) data engineering, (ii) modeling, and (iii) deployment.
• Execute . Announce your plan, and tackle the tasks one by one. In this step, the interviewer might ask you to write code or explain the maths behind your proposed method.
• Recap . At the end of the interview, summarize your answer and mention the tools and frameworks you would use to perform the work. It is also a good time to express your ideas on how the problem can be extended.

III   Interview tips

Every interview is an opportunity to show your skills and motivation for the role. Thus, it is important to prepare in advance. Here are useful rules of thumb to follow:

Show your motivation.

In deep learning case study interviews, the interviewer will evaluate your excitement for the company’s product. Make sure to show your curiosity, creativity and enthusiasm.

Listen to the hints given by your interviewer.

Example: You’re asked to automatically identify words indicating a location in science fiction books. You decide to use word2vec word embeddings. If your interviewer asks you “how were the word2vec embeddings created?”, she is digging into your understanding of word2vec and might be expecting you to question your choice. Seize this opportunity to display your mastery of the word2vec algorithm, and to ask a clarifying question. In fact, maybe the data distribution in the science fiction books is very different from the data distribution of the text corpora used to train word2vec. Maybe the interviewer is expecting you to say “although it will require significant amounts of data, we could train our own word embeddings on science fiction books.”

Show that you understand the development life cycle of an AI project.

Many candidates are only interested in what model they will use and how to train it. Remember that developing AI projects involves multiple tasks including data engineering, modeling, deployment, business analysis, and AI infrastructure.

Avoid clear-cut statements.

Because case studies are often open-ended and can have multiple valid solutions, avoid making categorical statements such as “the correct approach is …” You might offend the interviewer if the approach they are using is different from what you describe. It’s also better to show your flexibility with and understanding of the pros and cons of different approaches.

Study topics relevant to the company.

Deep learning case studies are often inspired by in-house projects. If the team is working on a domain-specific application, explore the literature.

Example 1: If the team is building an automatic speech recognition (ASR) software, review popular speech papers such as Deep Speech 2 (Amodei et al., 2015), audio datasets like Librispeech (Panayotov et al., 2015), as well as evaluation metrics like word error rate used to evaluate speech models.

Example 2: If the team is working on a face verification product, review the face recognition lessons of the Coursera Deep Learning Specialization ( Course 4 ), as well as the DeepFace (Taigman et al., 2014) and FaceNet (Schroff et al., 2015) papers prior to the onsite.

Example 3: If you’re interviewing with the perception team of a company building autonomous vehicles, you might want to read about topics such as object detection, path planning, safety, or edge deployment.

Articulate your thoughts in a compelling narrative.

Your interviewer will often judge the clarity of your thought process, your scientific rigor, and how comfortable you are using technical vocabulary.

Example 1: When explaining how a convolution layer works, your interviewer will notice if you say “ filter ” when you actually meant “ feature map ”.

Example 2: Mispronouncing a widely used technical word or acronym such as NER , MNIST, or CIFAR can affect your credibility. For instance, MNIST is pronounced “ɛm nist” rather than letter by letter.

Example 3: Show your ability to strategize by drawing the AI project development life cycle on the whiteboard.

Don’t mention methods you’re not able to explain.

Example: If you mention batch normalization , you can expect the interviewer to ask: “could you explain batch normalization?”.

Write clearly, draw charts, and introduce a notation if necessary.

The interviewer will judge the clarity of your thought process and your scientific rigor.

Example: Show your ability to strategize by drawing the AI project development life cycle on the whiteboard.

When you are not sure of your answer, be honest and say so.

Interviewers value honesty and penalize bluffing far more than lack of knowledge.

When out of ideas or stuck, think out loud rather than staying silent.

Talking through your thought process will help the interviewer correct you and point you in the right direction.

IV   Resources

You can build AI decision making skills by reading deep learning war stories and exposing yourself to projects . Here’s a list of useful resources to prepare for the deep learning case study interview.

In deeplearning.ai ’s course Structuring your Machine Learning Project , you’ll find insights drawn from Andrew Ng’s experience building and shipping many deep learning products. This course also has two “flight simulators” that let you practice decision-making as a machine learning project leader. It provides “industry experience” that you might otherwise get only after years of ML work experience.

• Deep Learning intuition is an interactive lecture illustrating AI decision making skills with examples from image classification, face recognition, neural style transfer, and trigger-word detection.
• In Full-cycle deep learning projects and Deep Learning Project Strategy , you’ll learn about the lifecycle of AI projects through concrete examples.
• In AI+Healthcare Case Studies , Pranav Rajupurkar presents challenges and opportunities for building and deploying AI for medical image interpretation .
• The popular real-time object detector YOLO (Redmon et al., 2015) was originally written in a framework called Darknet. Darkflow (Trieu) translates Darknet to Tensorflow and allows users to leverage transfer learning, retrain or fine-tune their YOLO models, an export model parameters in formats deployable on mobile.
• OpenPose (Cao et al., 2018) is a real-time multi-person system that can jointly detect human body, hand, facial, and foot keypoints on single images. You can find the authors’ code in the Git repository openpose .
• Learn about simple and efficient implementations of Named Entity Recognition models coded in Tensorflow in tf_ner (Genthial, 2018).
• By studying the code of ChatterBot (Cox, 2018), learn how to program a trainable conversational dialog engine in Python.
• Companies use convolutional neural networks (CNNs) for an assortment of purposes. They care about how accurately a CNN completes a task, and in many cases, about its speed. In Faster Neural Networks Straight from JPEG , Uber scientists (Gueguen et al.) describe an approach for making convolutional neural networks smaller, faster, and more accurate all at the same time by hacking libjpeg and leveraging the internal image representations already used by JPEG, the popular image format. Read carefully, and scrutinize the decisions making process throughout the project.
• Prediction models have to meet many requirements before they can be run in production at scale. In Using Deep Learning at Scale in Twitter’s Timelines , Twitter engineers Koumchatzky and Andryeyev explain how they incorporated deep learning into their modeling stack and how increased both audience and engagement on Twitter.
• Network quality is difficult to characterize and predict. While the average bandwidth and round trip time supported by a network are well-known indicators of network quality, other characteristics such as stability and predictability make a big difference when it comes to video streaming. Read Using Machine Learning to Improve Streaming Quality at Netflix (Ekanadham, 2018) to learn how machine learning enables a high-quality streaming experience for a global audience.

A convolution layer's filter is a set of trainable parameters that convolves across the convolution layer's input.

A feature map is one channel of a convolution layer's output. It results from convolving a filter on the input of a convolution layer.

In natural language processing, NER refers to Named Entity Recognition. It is the task of locating and classifying named entity (e.g., Yann Lecun, Trinidad and Tobago, and Dragon Ball Z) in text into pre-defined categories such as person names, organizations, locations, etc.

Batch normalization is a technique for improving the speed, performance, and stability of artificial neural networks. It was introduced by Ioffe et al. in 2015. (Wikipedia)

• Kian Katanforoosh - Founder at Workera, Lecturer at Stanford University - Department of Computer Science, Founding member at deeplearning.ai

Acknowledgment(s)

• The layout for this article was originally designed and implemented by Jingru Guo , Daniel Kunin , and Kian Katanforoosh for the deeplearning.ai AI Notes , and inspired by Distill .

Footnote(s)

• Job applicants are subject to anywhere from 3 to 8 interviews depending on the company, team, and role. You can learn more about the types of AI interviews in The Skills Boost . This includes the machine learning algorithms interview , the deep learning algorithms interview , the machine learning case study interview , the deep learning case study interview , the data science case study interview , and more coming soon.
• It takes time and effort to acquire acumen in a particular domain. You can develop your acumen by regularly reading research papers, articles, and tutorials. Twitter, Medium, and machine learning conferences (e.g., NeurIPS, ICML, CVPR, and the like) are good places to read the latest releases. You can also find a list of hundreds of Stanford students' projects on the Stanford CS230 website .

To reference this article, please use:

Workera, "Deep Learning Case Study Interview".

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Python Case Studies

Machine Learning business case studies solved using python. These are examples of how you can solve similar use cases for your own project and deploy the models into production.

I have discussed below points in each of the case studies.

• How to explore the given data?
• How to perform data pre-processing (missing values, outliers, transformations, etc.)
• How to create new columns based on existing columns (Feature Engineering)?
• How to select the best columns for machine learning (Feature Selection)?
• How to find the best ML algorithm for the given data?
• How to tune the predictive models.
• How to deploy predictive models into production?
• What happens after the model deployment?

Time Series Use Cases

• Time Series Forecasting Forecasting monthly sales quantity for Superstore dataset

NLP Use Cases

• TF-IDF Text classification Support Ticket Classification using TF-IDF Vectorization
• Sentiment Analysis using BERT Finding the sentiment of Indigo flight passengers based on their tweets
• Transfer Learning using GloVe Microsoft Ticket classification using GloVe
• Text classification using Word2Vec How to create classification models on text data using word2vec encodings

Regression Use Cases

• Zomato restaurant rating How to predict the future rating of a restaurant based on an ML model. A Case study in python.
• Predicting diamond prices Creating an ML model to predict the apt price of a given diamond.
• Evaluating old car price Predicting the right price for an old car using python machine learning.
• Bike rental demand prediction Create an ML model to forecast the demand of rental bikes every hour of the day.
• Computer price prediction Estimating the price of a computer, based on its specs.
• Concrete strength prediction How strong will this concrete be? Predicting the strength of concrete based on its mixture details.
• Boston housing price prediction House price prediction case study on the famous Boston data.

Classification Use Cases

• Loan Classification A predictive model to approve/reject a new loan application.
• German Credit Risk Classification of a loan as a potential risk or safe for the bank.
• Salary Band Classification Identify if you deserve a salary more than $50,000 or not .
• Titanic survival A case study to see what type of passengers survived the titanic crash.
• Advertisement Click Prediction A case study to predict if a user will click on advertisements or not

Deep Learning Use Cases

• ANN-Regression Creating an Artificial Neural Network model for Regression
• ANN-Classification Creating an Artificial Neural Network Model for Classification
• LSTM Predicting Infosys stock price using Long Short Term Memory network
• CNN Creating a face recognition model using the Convolution Neural Network

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Data Analytics Case Study Guide 2024

by Sam McKay, CFA | Data Analytics

Data analytics case studies reveal how businesses harness data for informed decisions and growth.

For aspiring data professionals, mastering the case study process will enhance your skills and increase your career prospects.

So, how do you approach a case study?

Use these steps to process a data analytics case study:

Understand the Problem: Grasp the core problem or question addressed in the case study.

Collect Relevant Data: Gather data from diverse sources, ensuring accuracy and completeness.

Apply Analytical Techniques: Use appropriate methods aligned with the problem statement.

Visualize Insights: Utilize visual aids to showcase patterns and key findings.

Derive Actionable Insights: Focus on deriving meaningful actions from the analysis.

This article will give you detailed steps to navigate a case study effectively and understand how it works in real-world situations.

By the end of the article, you will be better equipped to approach a data analytics case study, strengthening your analytical prowess and practical application skills.

Let’s dive in!

Table of Contents

What is a Data Analytics Case Study?

A data analytics case study is a real or hypothetical scenario where analytics techniques are applied to solve a specific problem or explore a particular question.

It’s a practical approach that uses data analytics methods, assisting in deciphering data for meaningful insights. This structured method helps individuals or organizations make sense of data effectively.

Additionally, it’s a way to learn by doing, where there’s no single right or wrong answer in how you analyze the data.

So, what are the components of a case study?

Key Components of a Data Analytics Case Study

A data analytics case study comprises essential elements that structure the analytical journey:

Problem Context: A case study begins with a defined problem or question. It provides the context for the data analysis , setting the stage for exploration and investigation.

Data Collection and Sources: It involves gathering relevant data from various sources , ensuring data accuracy, completeness, and relevance to the problem at hand.

Analysis Techniques: Case studies employ different analytical methods, such as statistical analysis, machine learning algorithms, or visualization tools, to derive meaningful conclusions from the collected data.

Insights and Recommendations: The ultimate goal is to extract actionable insights from the analyzed data, offering recommendations or solutions that address the initial problem or question.

Now that you have a better understanding of what a data analytics case study is, let’s talk about why we need and use them.

Why Case Studies are Integral to Data Analytics

Case studies serve as invaluable tools in the realm of data analytics, offering multifaceted benefits that bolster an analyst’s proficiency and impact:

Real-Life Insights and Skill Enhancement: Examining case studies provides practical, real-life examples that expand knowledge and refine skills. These examples offer insights into diverse scenarios, aiding in a data analyst’s growth and expertise development.

Validation and Refinement of Analyses: Case studies demonstrate the effectiveness of data-driven decisions across industries, providing validation for analytical approaches. They showcase how organizations benefit from data analytics. Also, this helps in refining one’s own methodologies

Showcasing Data Impact on Business Outcomes: These studies show how data analytics directly affects business results, like increasing revenue, reducing costs, or delivering other measurable advantages. Understanding these impacts helps articulate the value of data analytics to stakeholders and decision-makers.

Learning from Successes and Failures: By exploring a case study, analysts glean insights from others’ successes and failures, acquiring new strategies and best practices. This learning experience facilitates professional growth and the adoption of innovative approaches within their own data analytics work.

Including case studies in a data analyst’s toolkit helps gain more knowledge, improve skills, and understand how data analytics affects different industries.

Using these real-life examples boosts confidence and success, guiding analysts to make better and more impactful decisions in their organizations.

But not all case studies are the same.

Let’s talk about the different types.

Types of Data Analytics Case Studies

Data analytics encompasses various approaches tailored to different analytical goals:

Exploratory Case Study: These involve delving into new datasets to uncover hidden patterns and relationships, often without a predefined hypothesis. They aim to gain insights and generate hypotheses for further investigation.

Predictive Case Study: These utilize historical data to forecast future trends, behaviors, or outcomes. By applying predictive models, they help anticipate potential scenarios or developments.

Diagnostic Case Study: This type focuses on understanding the root causes or reasons behind specific events or trends observed in the data. It digs deep into the data to provide explanations for occurrences.

Prescriptive Case Study: This case study goes beyond analytics; it provides actionable recommendations or strategies derived from the analyzed data. They guide decision-making processes by suggesting optimal courses of action based on insights gained.

Each type has a specific role in using data to find important insights, helping in decision-making, and solving problems in various situations.

Regardless of the type of case study you encounter, here are some steps to help you process them.

Roadmap to Handling a Data Analysis Case Study

Embarking on a data analytics case study requires a systematic approach, step-by-step, to derive valuable insights effectively.

Here are the steps to help you through the process:

Step 1: Understanding the Case Study Context: Immerse yourself in the intricacies of the case study. Delve into the industry context, understanding its nuances, challenges, and opportunities.

Identify the central problem or question the study aims to address. Clarify the objectives and expected outcomes, ensuring a clear understanding before diving into data analytics.

Step 2: Data Collection and Validation: Gather data from diverse sources relevant to the case study. Prioritize accuracy, completeness, and reliability during data collection. Conduct thorough validation processes to rectify inconsistencies, ensuring high-quality and trustworthy data for subsequent analysis.

Step 3: Problem Definition and Scope: Define the problem statement precisely. Articulate the objectives and limitations that shape the scope of your analysis. Identify influential variables and constraints, providing a focused framework to guide your exploration.

Step 4: Exploratory Data Analysis (EDA): Leverage exploratory techniques to gain initial insights. Visualize data distributions, patterns, and correlations, fostering a deeper understanding of the dataset. These explorations serve as a foundation for more nuanced analysis.

Step 5: Data Preprocessing and Transformation: Cleanse and preprocess the data to eliminate noise, handle missing values, and ensure consistency. Transform data formats or scales as required, preparing the dataset for further analysis.

Step 6: Data Modeling and Method Selection: Select analytical models aligning with the case study’s problem, employing statistical techniques, machine learning algorithms, or tailored predictive models.

In this phase, it’s important to develop data modeling skills. This helps create visuals of complex systems using organized data, which helps solve business problems more effectively.

Understand key data modeling concepts, utilize essential tools like SQL for database interaction, and practice building models from real-world scenarios.

Furthermore, strengthen data cleaning skills for accurate datasets, and stay updated with industry trends to ensure relevance.

Step 7: Model Evaluation and Refinement: Evaluate the performance of applied models rigorously. Iterate and refine models to enhance accuracy and reliability, ensuring alignment with the objectives and expected outcomes.

Step 8: Deriving Insights and Recommendations: Extract actionable insights from the analyzed data. Develop well-structured recommendations or solutions based on the insights uncovered, addressing the core problem or question effectively.

Step 9: Communicating Results Effectively: Present findings, insights, and recommendations clearly and concisely. Utilize visualizations and storytelling techniques to convey complex information compellingly, ensuring comprehension by stakeholders.

Step 10: Reflection and Iteration: Reflect on the entire analysis process and outcomes. Identify potential improvements and lessons learned. Embrace an iterative approach, refining methodologies for continuous enhancement and future analyses.

This step-by-step roadmap provides a structured framework for thorough and effective handling of a data analytics case study.

Now, after handling data analytics comes a crucial step; presenting the case study.

Presenting Your Data Analytics Case Study

Presenting a data analytics case study is a vital part of the process. When presenting your case study, clarity and organization are paramount.

To achieve this, follow these key steps:

Structuring Your Case Study: Start by outlining relevant and accurate main points. Ensure these points align with the problem addressed and the methodologies used in your analysis.

Crafting a Narrative with Data: Start with a brief overview of the issue, then explain your method and steps, covering data collection, cleaning, stats, and advanced modeling.

Visual Representation for Clarity: Utilize various visual aids—tables, graphs, and charts—to illustrate patterns, trends, and insights. Ensure these visuals are easy to comprehend and seamlessly support your narrative.

Highlighting Key Information: Use bullet points to emphasize essential information, maintaining clarity and allowing the audience to grasp key takeaways effortlessly. Bold key terms or phrases to draw attention and reinforce important points.

Addressing Audience Queries: Anticipate and be ready to answer audience questions regarding methods, assumptions, and results. Demonstrating a profound understanding of your analysis instills confidence in your work.

Integrity and Confidence in Delivery: Maintain a neutral tone and avoid exaggerated claims about findings. Present your case study with integrity, clarity, and confidence to ensure the audience appreciates and comprehends the significance of your work.

By organizing your presentation well, telling a clear story through your analysis, and using visuals wisely, you can effectively share your data analytics case study.

This method helps people understand better, stay engaged, and draw valuable conclusions from your work.

We hope by now, you are feeling very confident processing a case study. But with any process, there are challenges you may encounter.

Key Challenges in Data Analytics Case Studies

A data analytics case study can present various hurdles that necessitate strategic approaches for successful navigation:

Challenge 1: Data Quality and Consistency

Challenge: Inconsistent or poor-quality data can impede analysis, leading to erroneous insights and flawed conclusions.

Solution: Implement rigorous data validation processes, ensuring accuracy, completeness, and reliability. Employ data cleansing techniques to rectify inconsistencies and enhance overall data quality.

Challenge 2: Complexity and Scale of Data

Challenge: Managing vast volumes of data with diverse formats and complexities poses analytical challenges.

Solution: Utilize scalable data processing frameworks and tools capable of handling diverse data types. Implement efficient data storage and retrieval systems to manage large-scale datasets effectively.

Challenge 3: Interpretation and Contextual Understanding

Challenge: Interpreting data without contextual understanding or domain expertise can lead to misinterpretations.

Solution: Collaborate with domain experts to contextualize data and derive relevant insights. Invest in understanding the nuances of the industry or domain under analysis to ensure accurate interpretations.

Challenge 4: Privacy and Ethical Concerns

Challenge: Balancing data access for analysis while respecting privacy and ethical boundaries poses a challenge.

Solution: Implement robust data governance frameworks that prioritize data privacy and ethical considerations. Ensure compliance with regulatory standards and ethical guidelines throughout the analysis process.

Challenge 5: Resource Limitations and Time Constraints

Challenge: Limited resources and time constraints hinder comprehensive analysis and exhaustive data exploration.

Solution: Prioritize key objectives and allocate resources efficiently. Employ agile methodologies to iteratively analyze and derive insights, focusing on the most impactful aspects within the given timeframe.

Recognizing these challenges is key; it helps data analysts adopt proactive strategies to mitigate obstacles. This enhances the effectiveness and reliability of insights derived from a data analytics case study.

Now, let’s talk about the best software tools you should use when working with case studies.

Top 5 Software Tools for Case Studies

In the realm of case studies within data analytics, leveraging the right software tools is essential.

Here are some top-notch options:

Tableau : Renowned for its data visualization prowess, Tableau transforms raw data into interactive, visually compelling representations, ideal for presenting insights within a case study.

Python and R Libraries: These flexible programming languages provide many tools for handling data, doing statistics, and working with machine learning, meeting various needs in case studies.

Microsoft Excel : A staple tool for data analytics, Excel provides a user-friendly interface for basic analytics, making it useful for initial data exploration in a case study.

SQL Databases : Structured Query Language (SQL) databases assist in managing and querying large datasets, essential for organizing case study data effectively.

Statistical Software (e.g., SPSS , SAS ): Specialized statistical software enables in-depth statistical analysis, aiding in deriving precise insights from case study data.

Choosing the best mix of these tools, tailored to each case study’s needs, greatly boosts analytical abilities and results in data analytics.

Final Thoughts

Case studies in data analytics are helpful guides. They give real-world insights, improve skills, and show how data-driven decisions work.

Using case studies helps analysts learn, be creative, and make essential decisions confidently in their data work.

Check out our latest clip below to further your learning!

Frequently Asked Questions

What are the key steps to analyzing a data analytics case study.

When analyzing a case study, you should follow these steps:

Clarify the problem : Ensure you thoroughly understand the problem statement and the scope of the analysis.

Make assumptions : Define your assumptions to establish a feasible framework for analyzing the case.

Gather context : Acquire relevant information and context to support your analysis.

Analyze the data : Perform calculations, create visualizations, and conduct statistical analysis on the data.

Provide insights : Draw conclusions and develop actionable insights based on your analysis.

How can you effectively interpret results during a data scientist case study job interview?

During your next data science interview, interpret case study results succinctly and clearly. Utilize visual aids and numerical data to bolster your explanations, ensuring comprehension.

Frame the results in an audience-friendly manner, emphasizing relevance. Concentrate on deriving insights and actionable steps from the outcomes.

How do you showcase your data analyst skills in a project?

To demonstrate your skills effectively, consider these essential steps. Begin by selecting a problem that allows you to exhibit your capacity to handle real-world challenges through analysis.

Methodically document each phase, encompassing data cleaning, visualization, statistical analysis, and the interpretation of findings.

Utilize descriptive analysis techniques and effectively communicate your insights using clear visual aids and straightforward language. Ensure your project code is well-structured, with detailed comments and documentation, showcasing your proficiency in handling data in an organized manner.

Lastly, emphasize your expertise in SQL queries, programming languages, and various analytics tools throughout the project. These steps collectively highlight your competence and proficiency as a skilled data analyst, demonstrating your capabilities within the project.

Can you provide an example of a successful data analytics project using key metrics?

A prime illustration is utilizing analytics in healthcare to forecast hospital readmissions. Analysts leverage electronic health records, patient demographics, and clinical data to identify high-risk individuals.

Implementing preventive measures based on these key metrics helps curtail readmission rates, enhancing patient outcomes and cutting healthcare expenses.

This demonstrates how data analytics, driven by metrics, effectively tackles real-world challenges, yielding impactful solutions.

Why would a company invest in data analytics?

Companies invest in data analytics to gain valuable insights, enabling informed decision-making and strategic planning. This investment helps optimize operations, understand customer behavior, and stay competitive in their industry.

Ultimately, leveraging data analytics empowers companies to make smarter, data-driven choices, leading to enhanced efficiency, innovation, and growth.

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ORIGINAL RESEARCH article

This article is part of the research topic.

Innovative Technology and Techniques for Effective Weed Control

Autonomous Diode Laser Weeding Mobile Robot in Cotton Field Using Deep Learning, Visual Servoing and Finite State Machine Provisionally Accepted

• 1 University of Georgia, United States

The final, formatted version of the article will be published soon.

Small autonomous robotic platforms can be utilized in agricultural environments to target weeds in their early stages of growth and eliminate them. Autonomous solutions reduce the need for labor, cut costs, and enhance productivity. To eliminate the need for chemicals in weeding, and other solutions that can interfere with the crop’s growth, lasers have emerged as a viable alternative. Lasers can precisely target weed stems, effectively eliminating or stunting their growth. In this study an autonomous robot that employs a diode laser for weed elimination was developed and its performance in removing weeds in a cotton field was evaluated. The robot utilized a combination of visual servoing for motion control, the Robotic operating system (ROS) finite state machine implementation (SMACH) to manage its states, actions, and transitions. Furthermore, the robot utilized deep learning for weed detection, as well as navigation when combined with GPS and dynamic window approach path planning algorithm. Employing its 2D cartesian arm, the robot positioned the laser diode attached to a rotating pan-and-tilt mechanism for precise weed targeting. In a cotton field, without weed tracking, the robot achieved an overall weed elimination rate of 47% in a single pass, with a 9.5 second cycle time per weed treatment when the laser diode was positioned parallel to the ground. When the diode was placed at a 10°downward angle from the horizontal axis, the robot achieved a 63% overall elimination rate on a single pass with 8 seconds cycle time per weed treatment. With the implementation of weed tracking using DeepSORT tracking algorithm, the robot achieved an overall weed elimination rate of 72.35% at 8 seconds cycle time per weed treatment. With a strong potential for generalizing to other crops, these results provide strong evidence of the feasibility of autonomous weed elimination using low-cost diode lasers and small robotic platforms.

Keywords: Non-chemical weeding, Robotic weeding, precision agriculture, Weed detection, Autonomous navigation, weed stem laser targeting Normal, Indent: Left: 0.25", Space After: 0 pt

Received: 19 Feb 2024; Accepted: 29 Apr 2024.

Copyright: © 2024 Mwitta, Rains and Prostko. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

* Correspondence: Dr. Canicius Mwitta, University of Georgia, Athens, United States Dr. Glen C. Rains, University of Georgia, Athens, United States

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