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 4A) [Assignment Solution] - deeplearning.ai

▸  building your deep neural network: step by step. 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: neural networks & deep learning (week 3) click here:  coursera: neural networks & deep learning (week 4b) scroll down  for  coursera: neural networks & deep learning (week 4a) 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, building your deep neural network: step by step, in this notebook, you will implement all the functions required to build a deep neural network. in the next assignment, you will use these functions to build a deep neural network for image classification. after this assignment you will be able to: use non-linear units like relu to improve your model build a deeper neural network (with more than 1 hidden layer) implement an easy-to-use neural network class notation : superscript  [ l ]  denotes a quantity associated with the  l t h  layer. example:  a [ l ]  is the  l t h  layer activation.  w [ l ]  and  b [ l ]  are the  l t h  layer parameters. superscript  ( i )  denotes a quantity associated with the  i t h  example. example:  x ( i )  is the  i t h  training example. lowerscript  i i  denotes the  i t h  entry of a vector. example:  a [ l ] i  denotes the  i t h  entry of the  l t h  layer's activations). let's get started, 1 - packages, let's first import all the packages that you will need during this assignment. numpy  is the main package for scientific computing with python. matplotlib  is a library to plot graphs in python. dnn_utils provides some necessary functions for this notebook. testcases provides some test cases to assess the correctness of your functions np.random.seed(1) is used to keep all the random function calls consistent. it will help us grade your work. please don't change the seed., 2 - outline of the assignment.

• Initialize the parameters for a two-layer network and for an  L -layer neural network.
• Complete the LINEAR part of a layer's forward propagation step (resulting in  Z [ l ] ).
• We give you the ACTIVATION function (relu/sigmoid).
• Combine the previous two steps into a new [LINEAR->ACTIVATION] forward function.
• Stack the [LINEAR->RELU] forward function L-1 time (for layers 1 through L-1) and add a [LINEAR->SIGMOID] at the end (for the final layer  L ). This gives you a new L_model_forward function.
• Compute the loss.
• Complete the LINEAR part of a layer's backward propagation step.
• We give you the gradient of the ACTIVATE function (relu_backward/sigmoid_backward)
• Combine the previous two steps into a new [LINEAR->ACTIVATION] backward function.
• Stack [LINEAR->RELU] backward L-1 times and add [LINEAR->SIGMOID] backward in a new L_model_backward function
• Finally update the parameters.

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

3 - Initialization

3.1 - 2-layer neural network.

• The model's structure is:  LINEAR -> RELU -> LINEAR -> SIGMOID .
• Use random initialization for the weight matrices. Use  np.random.randn(shape)*0.01  with the correct shape.
• Use zero initialization for the biases. Use  np.zeros(shape) .

3.2 - L-layer Neural Network

• The model's structure is  [LINEAR -> RELU]  × ×  (L-1) -> LINEAR -> SIGMOID . I.e., it has  L − 1 L − 1  layers using a ReLU activation function followed by an output layer with a sigmoid activation function.
• Use random initialization for the weight matrices. Use  np.random.randn(shape) * 0.01 .
• Use zeros initialization for the biases. Use  np.zeros(shape) .
• We will store  n [ l ] n [ l ] , the number of units in different layers, in a variable  layer_dims . For example, the  layer_dims  for the "Planar Data classification model" from last week would have been [2,4,1]: There were two inputs, one hidden layer with 4 hidden units, and an output layer with 1 output unit. Thus means  W1 's shape was (4,2),  b1  was (4,1),  W2  was (1,4) and  b2  was (1,1). Now you will generalize this to  L L  layers!
• Here is the implementation for  L = 1 L = 1  (one layer neural network). It should inspire you to implement the general case (L-layer neural network).

4 - Forward propagation module

4.1 - linear forward.

• LINEAR -> ACTIVATION where ACTIVATION will be either ReLU or Sigmoid.
• [LINEAR -> RELU]  × ×  (L-1) -> LINEAR -> SIGMOID (whole model)

4.2 - Linear-Activation Forward

• Sigmoid :  σ ( Z ) = σ ( W A + b ) = 1 1 + e − ( W A + b ) . We have provided you with the  sigmoid  function. This function returns  two  items: the activation value " a " and a " cache " that contains " Z " (it's what we will feed in to the corresponding backward function). To use it you could just call: A , activation_cache = sigmoid ( Z )
• ReLU : The mathematical formula for ReLu is  A = R E L U ( Z ) = m a x ( 0 , Z ) A = R E L U ( Z ) = m a x ( 0 , Z ) . We have provided you with the  relu  function. This function returns  two items: the activation value " A " and a " cache " that contains " Z " (it's what we will feed in to the corresponding backward function). To use it you could just call: A , activation_cache = relu ( Z )

d) L-Layer Model

• Use the functions you had previously written
• Use a for loop to replicate [LINEAR->RELU] (L-1) times
• Don't forget to keep track of the caches in the "caches" list. To add a new value  c  to a  list , you can use  list.append(c) .

5 - Cost function

6 - backward propagation module.

• LINEAR backward
• LINEAR -> ACTIVATION backward where ACTIVATION computes the derivative of either the ReLU or sigmoid activation
• [LINEAR -> RELU]  × ×  (L-1) -> LINEAR -> SIGMOID backward (whole model)

6.1 - Linear backward

6.2 - Linear-Activation backward

• sigmoid_backward : Implements the backward propagation for SIGMOID unit. You can call it as follows:
• relu_backward : Implements the backward propagation for RELU unit. You can call it as follows:

6.3 - L-Model Backward

6.4 - Update Parameters

7 - conclusion.

• A two-layer neural network
• An L-layer neural network

hi bro...i was working on the week 4 assignment .i am getting an assertion error on cost_compute function.help me with this..but the same function is working for the l layer model AssertionError Traceback (most recent call last) in () ----> 1 parameters = two_layer_model(train_x, train_y, layers_dims = (n_x, n_h, n_y), num_iterations = 2500, print_cost= True) in two_layer_model(X, Y, layers_dims, learning_rate, num_iterations, print_cost) 46 # Compute cost 47 ### START CODE HERE ### (≈ 1 line of code) ---> 48 cost = compute_cost(A2, Y) 49 ### END CODE HERE ### 50 /home/jovyan/work/Week 4/Deep Neural Network Application: Image Classification/dnn_app_utils_v3.py in compute_cost(AL, Y) 265 266 cost = np.squeeze(cost) # To make sure your cost's shape is what we expect (e.g. this turns [[17]] into 17). --> 267 assert(cost.shape == ()) 268 269 return cost AssertionError:

Hey,I am facing problem in linear activation forward function of week 4 assignment Building Deep Neural Network. I think I have implemented it correctly and the output matches with the expected one. I also cross check it with your solution and both were same. But the grader marks it, and all the functions in which this function is called as incorrect. I am unable to find any error in its coding as it was straightforward in which I used built in functions of SIGMOID and RELU. Please guide.

hi bro iam always getting the grading error although iam getting the crrt o/p for all

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