coursera deep learning assignment solutions

Deep-Learning-Specialization-Coursera

This repo contains the updated version of all the assignments/labs (done by me) of deep learning specialization on coursera by andrew ng. it includes building various deep learning models from scratch and implementing them for object detection, facial recognition, autonomous driving, neural machine translation, trigger word detection, etc., deep learning specialization coursera [updated version 2021].

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This repo contains all of the solved assignments of Coursera’s most famous Deep Learning Specialization of 5 courses offered by deeplearning.ai

Instructor: Prof. Andrew Ng

This Specialization was updated in April 2021 to include developments in deep learning and programming frameworks. One of the most major changes was shifting from Tensorflow 1 to Tensorflow 2. Also, new materials were added. However, Most of the old online repositories still don’t have old codes. This repo contains updated versions of the assignments. Happy Learning :)

Programming Assignments

Course 1: Neural Networks and Deep Learning

• W2A1 - Logistic Regression with a Neural Network mindset
• W2A2 - Python Basics with Numpy
• W3A1 - Planar data classification with one hidden layer
• W3A1 - Building your Deep Neural Network: Step by Step¶
• W3A2 - Deep Neural Network for Image Classification: Application

Course 2: Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization

• W1A1 - Initialization
• W1A2 - Regularization
• W1A3 - Gradient Checking
• W2A1 - Optimization Methods
• W3A1 - Introduction to TensorFlow

Course 3: Structuring Machine Learning Projects

• There were no programming assignments in this course. It was completely thoeretical.
• Here is a link to the course

Course 4: Convolutional Neural Networks

• W1A1 - Convolutional Model: step by step
• W1A2 - Convolutional Model: application
• W2A1 - Residual Networks
• W2A2 - Transfer Learning with MobileNet
• W3A1 - Autonomous Driving - Car Detection
• W3A2 - Image Segmentation - U-net
• W4A1 - Face Recognition
• W4A2 - Neural Style transfer

Course 5: Sequence Models

• W1A1 - Building a Recurrent Neural Network - Step by Step
• W1A2 - Character level language model - Dinosaurus land
• W1A3 - Improvise A Jazz Solo with an LSTM Network
• W2A1 - Operations on word vectors
• W2A2 - Emojify
• W3A1 - Neural Machine Translation With Attention
• W3A2 - Trigger Word Detection
• W4A1 - Transformer Network
• W4A2 - Named Entity Recognition - Transformer Application
• W4A3 - Extractive Question Answering - Transformer Application

I’ve uploaded these solutions here, only for being used as a help by those who get stuck somewhere. It may help them to save some time. I strongly recommend everyone to not directly copy any part of the code (from here or anywhere else) while doing the assignments of this specialization. The assignments are fairly easy and one learns a great deal of things upon doing these. Thanks to the deeplearning.ai team for giving this treasure to us.

Connect with me

Name: Abdur Rahman

Institution: Indian Institute of Technology Delhi

Find me on:

Deep-Learning-Specialization

Coursera deep learning specialization, sequence models.

This course will teach you how to build models for natural language, audio, and other sequence data. Thanks to deep learning, sequence algorithms are working far better than just two years ago, and this is enabling numerous exciting applications in speech recognition, music synthesis, chatbots, machine translation, natural language understanding, and many others.

• Understand how to build and train Recurrent Neural Networks (RNNs), and commonly-used variants such as GRUs and LSTMs.
• Be able to apply sequence models to natural language problems, including text synthesis.
• Be able to apply sequence models to audio applications, including speech recognition and music synthesis.

Week 1: Sequence Models

Learn about recurrent neural networks. This type of model has been proven to perform extremely well on temporal data. It has several variants including LSTMs, GRUs and Bidirectional RNNs, which you are going to learn about in this section.

Assignment of Week 1

• Quiz 1: Recurrent Neural Networks
• Programming Assignment: Building a recurrent neural network - step by step
• Programming Assignment: Dinosaur Island - Character-Level Language Modeling
• Programming Assignment: Jazz improvisation with LSTM

Week 2: Natural Language Processing & Word Embeddings

Natural language processing with deep learning is an important combination. Using word vector representations and embedding layers you can train recurrent neural networks with outstanding performances in a wide variety of industries. Examples of applications are sentiment analysis, named entity recognition and machine translation.

Assignment of Week 2

• Quiz 2: Natural Language Processing & Word Embeddings
• Programming Assignment: Operations on word vectors - Debiasing
• Programming Assignment: Emojify

Week 3: Sequence models & Attention mechanism

Sequence models can be augmented using an attention mechanism. This algorithm will help your model understand where it should focus its attention given a sequence of inputs. This week, you will also learn about speech recognition and how to deal with audio data.

Assignment of Week 3

• Quiz 3: Sequence models & Attention mechanism
• Programming Assignment: Neural Machine Translation with Attention
• Programming Assignment: Trigger word detection

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Coursera: Neural Networks and Deep Learning (Week 2) [Assignment Solution] - deeplearning.ai

▸  logistic regression with a neural network mindset. i have recently completed the neural networks and deep learning course from coursera by deeplearning.ai while doing the course we have to go through various quiz and assignments in python. here, i am sharing my solutions for the weekly assignments throughout the course. these solutions are for reference only. >  it is recommended that you should solve the assignments by yourself honestly then only it makes sense to complete the course. >  but, in case you stuck in between, feel free to refer to the solutions provided by me., don't just copy paste the code for the sake of completion.  even if you copy the code, make sure you understand the code first. click here : coursera: machine learning by andrew ng  all week  assignments click here:  coursera: neural networks & deep learning (week 3) scroll down  for coursera: neural networks and deep learning (week 2) assignments . (adsbygoogle = window.adsbygoogle || []).push({}); recommended machine learning courses: coursera: machine learning    coursera: deep learning specialization coursera: machine learning with python coursera: advanced machine learning specialization udemy: machine learning linkedin: machine learning eduonix: machine learning edx: machine learning fast.ai: introduction to machine learning for coders, logistic regression with a neural network mindset.

• Do not use loops (for/while) in your code, unless the instructions explicitly ask you to do so.
• Initializing parameters
• Calculating the cost function and its gradient
• Using an optimization algorithm (gradient descent)
• Gather all three functions above into a main model function, in the right order.

1 - Packages

• numpy  is the fundamental package for scientific computing with Python.
• h5py  is a common package to interact with a dataset that is stored on an H5 file.
• matplotlib  is a famous library to plot graphs in Python.
• PIL  and  scipy  are used here to test your model with your own picture at the end.

2 - Overview of the Problem set

• Figure out the dimensions and shapes of the problem (m_train, m_test, num_px, ...)
• Reshape the datasets such that each example is now a vector of size (num_px * num_px * 3, 1)
• "Standardize" the data

Check-out our free tutorials on IOT (Internet of Things):

3 - General Architecture of the learning algorithm

4 - Building the parts of our algorithm

• Define the model structure (such as number of input features)
• Initialize the model's parameters
• Calculate current loss (forward propagation)
• Calculate current gradient (backward propagation)
• Update parameters (gradient descent)

4.1 - Helper functions

4.2 - initializing parameters, 4.3 - forward and backward propagation.

• You compute  A = σ ( w T X + b ) = ( a ( 1 ) , a ( 2 ) , . . . , a ( m − 1 ) , a ( m ) )
• You calculate the cost function:  J = − 1 m ∑ m i = 1 y ( i ) log ( a ( i ) ) + ( 1 − y ( i ) ) log ( 1 − a ( i ) )

4.4 - Optimization

• You have initialized your parameters.
• You are also able to compute a cost function and its gradient.
• Now, you want to update the parameters using gradient descent.
• Calculate  Y ̂  = A = σ ( w T X + b )
• Convert the entries of a into 0 (if activation <= 0.5) or 1 (if activation > 0.5), stores the predictions in a vector  Y_prediction . If you wish, you can use an  if / else  statement in a  for  loop (though there is also a way to vectorize this).
• Initialize (w,b)
• computing the cost and its gradient
• updating the parameters using gradient descent
• Use the learned (w,b) to predict the labels for a given set of examples

5 - Merge all functions into a model

6 - Further analysis (optional/ungraded exercise)

Choice of learning rate.

• Different learning rates give different costs and thus different predictions results.
• If the learning rate is too large (0.01), the cost may oscillate up and down. It may even diverge (though in this example, using 0.01 still eventually ends up at a good value for the cost).
• A lower cost doesn't mean a better model. You have to check if there is possibly overfitting. It happens when the training accuracy is a lot higher than the test accuracy.
• In deep learning, we usually recommend that you:
• Choose the learning rate that better minimizes the cost function.
• If your model overfits, use other techniques to reduce overfitting. (We'll talk about this in later videos.)

7 - Test with your own image (optional/ungraded exercise)

• Preprocessing the dataset is important.
• You implemented each function separately: initialize(), propagate(), optimize(). Then you built a model().
• Tuning the learning rate (which is an example of a "hyperparameter") can make a big difference to the algorithm. You will see more examples of this later in this course!
• http://www.wildml.com/2015/09/implementing-a-neural-network-from-scratch/
• https://stats.stackexchange.com/questions/211436/why-do-we-normalize-images-by-subtracting-the-datasets-image-mean-and-not-the-c
• coursera.org

39 Comments

In Forward and Backward propagation the second working solution does not seem to work. ### WORKING SOLUTION: 2 ### #cost = (-1/m)*(np.dot(Y,(np.log(A)).T)+(np.dot((1-Y),(np.log(1-A)).T))) # Dimention = Scalar # compute cost Can you check it again?

Hi Ashish, I have checked it. and It is working for me. can you please tell me what error you are getting there in ### WORKING SOLUTION: 2 ###?

give me please whole submmited file in .py

Sorry Chirag. I won't be able to provide that. I think, I have already provided enough content to understand along with necessary comments.

how to upload assignments ,that is in which format

bro r u studying or working in some companies. and whats ur opinion on appliedaicourse site?

your blog help me a lot thanks ..

hi i am stuck at submission of assignment please help me to how to submit the assignment

HOW TO SUBMIT PLEASE TELL ME

hi bro please reply how to submit the assignment

How to submit is already given in your course. Just before the assignment. (Please read that). If you are unable to submit the assignment even after reading that, then you should raise this concern to Coursera forum.

i am getting an assertion error at the optimization cell grads, cost = propagate(w, b, X, Y) and also in assert(dw.shape == w.shape)

I am getting this error everytime i try to run the code- NameError Traceback (most recent call last) in () 4 num_px = None 5 ----> 6 m_train = train_set_x_orig.shape[0] 7 m_test = test_set_x_orig.shape[0] 8 num_px = train_set_x_orig.shape[1] NameError: name 'train_set_x_orig' is not defined

Hi Akshay, Can you explain the vectorized method at ln[15]... Will you be able to share some links so that I can learn more. Also thanks for making this... It helps a lot in understanding things...

I would suggest, There are some exercises for practice on Machine Learning by Andrew NG course on coursera. If you do that, you will get little bit idea about what vectorisation is? and how it works?

Hi and thanks for all your great posts about ML. I'm completely new to both Python and ML so having this as a reference is great (I'm doing the Coursera Deep Learning Specialization - trying hard to work out my own solutions but sometimes I get stuck...) However, I too have difficulties in understanding the vectorized solution at ln[15] - it is beautiful in it's simplicity - but what is actually taking place there? I've watched all Andrew Ngs videos and read the material but still can't figure this one out.

OK, think I figured it out. ">=" operator is built in python comparison functionality returning true or false (told you I am a beginner :-) and the "*1.0" simply converts true to 1 and false to 0

You understood it correctly. and Don't worry. We all started like this only. All the Best (y)

Hi Akshay, I am getting the following error while running the cell for optimize function: File "", line 40 dw = grads["dw"] ^ IndentationError: unindent does not match any outer indentation level Can you please help me understand this error & to resolve the same ! Thanks

Python doesn't use bracket or braces to control the flow of the program. In python, the flow is controlled by indentation only. If your indentation is wrong then it throws IndentationError. Please read more about Python Indentation.

d = model(train_set_x, train_set_y, test_set_x, test_set_y, num_iterations = 2000, learning_rate = 0.005, print_cost = True) sir, in this line i am getting error. the error is --------------------------------------------------------------------------- NameError Traceback (most recent call last) in () ----> 1 d = model(train_set_x, train_set_y, test_set_x, test_set_y, num_iterations = 2000, learning_rate = 0.005, print_cost = True) NameError: name 'train_set_x' is not defined

can you just show a single file of code that show what exact you have to submitted in assignment just for a sample because i have submitted and i get error i.e malformed feedback

Hello Akshay! How do I upload my code in coursera? I have a .ipynb file. But they are asking to upload a json file. How do I convert my code to .json? Please help to submit my assignment . Thankyou

There is "submit" button on top-right of the page (notebook). Just click on it.

sir i stuck in this:- real output is this:- Expected Output: Cost after iteration 0 0.693147 ⋮⋮ ⋮⋮ Train Accuracy 99.04306220095694 % Test Accuracy 70.0 % but i get that output:- Cost after iteration 0: 0.693147 Cost after iteration 100: 0.584508 Cost after iteration 200: 0.466949 Cost after iteration 300: 0.376007 Cost after iteration 400: 0.331463 Cost after iteration 500: 0.303273 Cost after iteration 600: 0.279880 Cost after iteration 700: 0.260042 Cost after iteration 800: 0.242941 Cost after iteration 900: 0.228004 Cost after iteration 1000: 0.214820 Cost after iteration 1100: 0.203078 Cost after iteration 1200: 0.192544 Cost after iteration 1300: 0.183033 Cost after iteration 1400: 0.174399 Cost after iteration 1500: 0.166521 Cost after iteration 1600: 0.159305 Cost after iteration 1700: 0.152667 Cost after iteration 1800: 0.146542 Cost after iteration 1900: 0.140872 --------------------------------------------------------------------------- NameError Traceback (most recent call last) in () ----> 1 d = model(train_set_x, train_set_y, test_set_x, test_set_y, num_iterations = 2000, learning_rate = 0.005, print_cost = True) in model(X_train, Y_train, X_test, Y_test, num_iterations, learning_rate, print_cost) 31 32 # Predict test/train set examples (≈ 2 lines of code) ---> 33 Y_prediction_test = predict(w, b, X_test) 34 Y_prediction_train = predict(w, b, X_train) 35 ### END CODE HERE ### NameError: name 'predict' is not defined

bro did u upload the solutions for other courses in the deep learning specialization??

Yes. https://www.apdaga.com/2020/05/coursera-improving-deep-neural-networks-hyperparameter-tuning-regularization-and-optimization-all-weeks-solutions-assignment-quiz.html I will keep on updating more courses.

Sir I accidentally deleted my jupyter notebook week 2, 1st Practice Programming assignment (Python Basics with numpy)of Neural Network and Deep learning. Sir can you please provide the solution how to get it or sir please upload the notebook so i can go through it..

AttributeError Traceback (most recent call last) in () 1 2 w, b, X, Y = np.array([[1], [2]]), 2, np.array([[1,2], [3,4]]), np.array([[1, 0]]) ----> 3 grads, cost = propagate(w, b, X, Y) 4 print ("dw = " + str(grads["dw"])) 5 print ("db = " + str(grads["db"])) in propagate(w, b, X, Y) 25 ### START CODE HERE ### (≈ 2 lines of code) 26 A = sigmoid(np.dot(w.T,X)+b) # compute activation ---> 27 cost = (-1/m)*np.sum(Y*np.log(A)+(1-Y)*(np.log(1-A))) # compute cost 28 ### END CODE HERE ### 29 AttributeError: 'NoneType' object has no attribute 'log' please help :)

fundametals of comunication network quiz answer plz

Thanks alot. I already completed that course, but have not downloaded my submission. And Coursera has blocked the Labs. Your uploads are helpful to me in this regards.

Can u plz provide the dataset for this problem,i havent enrolled the course,but solving this assignment.

Sorry. I don't have the dataset with me.

Can you please help me how to this Assigment handle-!

how this Assigment submitted to coursera...

Hey bro i am facing assertion error in the place where initialize_with_zeros(dim)

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Solutions to all quiz and all the programming assignments!!!

HeroKillerEver/coursera-deep-learning

Folders and files, repository files navigation.

A series of online courses offered by deeplearning.ai . I would like to say thanks to Prof. Andrew Ng and his colleagues for spreading knowledge to normal people and great courses sincerely.

The reason I would like to create this repository is purely for academic use (in case for my future use). I am really glad if you can use it as a reference and happy to discuss with you about issues related with the course even for further deep learning techniques.

Please only use it as a reference . The quiz and assignments are relatively easy to answer, hope you can have fun with the courses.

1. Neural Network and Deep Learning

• Logistic Regression as a Neural Network
• Building your Deep Neural Network - Step by Step
• Deep Neural Network Application-Image Classification

2. Improving Deep Neural Networks-Hyperparameter tuning, Regularization and Optimization

• Initialization
• Regularization
• Gradient Checking
• Optimization

3. Structuring Machine Learning Projects

4. convolutional neural network.

• Convolutional Model- step by step
• Car detection for Autonomous Driving
• Neural Style Transfer
• Face Recognition

5. Sequence Models

• Building a Recurrent Neural Network - Step by Step
• Dinosaur Island -- Character-level language model
• Jazz improvisation with LSTM
• Word Vector Representation
• Machine Translation
• Trigger Word Detection

Haibin Yu/ @HeroKillerEver

Contributors 6

• Jupyter Notebook 98.9%
• Python 1.1%