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:

CS6910: Deep Learning

Pre-requisites.

Calculus [ Online course from MIT ]

Linear Algebra [CS6015 or equivalent] | [ Online course from MIT ]

Probability Theory [CS6015 or equivalent] | [ Online course from MIT ]

Non-linear Optimization [CS5020 or equivalent] | [First Course in Optimization by Prof. Soman (IITB) available on CDEEP]

Pattern Recognition and Machine Learning [CS5691 or equivalent] | [ Andrew Ng's ML course ]

Instructor : Mitesh M. Khapra

When : Jan-May 2024

Lectures : Slot H

Where : CS25

Teaching Assistants:

Name	Lab	Email	Office hours	Days
Anushka Singh	AI4Bharat	[email protected]	3-4 pm	Tuesday, Thursday
Oikantik Nath	AI4Bharat	[email protected]	12-1 pm	Tuesday, Thursday
Bibhuti Majhi	AI4Bharat	[email protected]	2-4 pm	Wednesday
Sarthak Naithani	AI4Bharat	[email protected]	3-4 pm	Tuesday, Wednesday
Putta Sai Sree Ram	AI4Bharat	[email protected]	3-4 pm	Monday, Friday
Ravi Prakash Singh	AI4Bharat	[email protected]	1-2 pm	Tuesday, Friday
Guddeppagari Shathish Kumar Reddy	AI4Bharat	[email protected]	3-4 pm	Monday, Friday
Poorbi Mukesh Dalal	-	[email protected]	1-3 pm	Wednesday
Ashok R	-	[email protected]	2-4 pm	Wednesday
Amit Kumar	-	[email protected]	2-4 pm	Wednesday

Reference Textbooks

--> -->

Lecture#	Contents	Lecture pdf	Lecture Videos	Extra Reading Material
Syllabus, Logistics		-	-
Lecture 1	(Partial) History of Deep Learning, Deep Learning Success Stories		| | | |
Lecture 2	McCulloch Pitts Neuron, Thresholding Logic, Perceptrons, Perceptron Learning Algorithm and Convergence, Multilayer Perceptrons (MLPs), Representation Power of MLPs		| | | | | | |	Chapters 1,2,3,4 from
Lecture 3	Sigmoid Neurons, Gradient Descent, Feedforward Neural Networks, Representation Power of Feedforward Neural Networks		| | | | | | |
Lecture 4	Feedforward Neural Networks, Backpropagation		| | | | | | |
Lecture 5	Gradient Descent (GD), Momentum Based GD, Nesterov Accelerated GD, Stochastic GD, AdaGrad, RMSProp, Adam	|	| | | | | | | | | | | | | |
Eigenvalues and eigenvectors, Eigenvalue Decomposition, Basis, Principal Component Analysis and its interpretations, Singular Value Decomposition	|	| | | | | | | |
Lecture 7	Autoencoders and relation to PCA, Regularization in autoencoders, Denoising autoencoders, Sparse autoencoders, Contractive autoencoders	|	| | | | |
Lecture 6	Bias Variance Tradeoff, L2 regularization, Early stopping, Dataset augmentation, Parameter sharing and tying, Injecting noise at input, Ensemble methods, Dropout		| | | | | | | | | | |
Lecture 7	Greedy Layerwise Pre-training, Better activation functions, Better weight initialization methods, Batch Normalization		| | | | |
Lecture 8	Convolutional Neural Networks, LeNet, AlexNet, ZF-Net, VGGNet, GoogLeNet, ResNet		| | | | | | |
Object Detection, RCNN, Fast RCNN, Faster RCNN, YOLO	|
Lecture 9	Visualizing Convolutional Neural Networks, Guided Backpropagation, Deep Dream, Deep Art, Fooling Convolutional Neural Networks		| | | | | | | | |
Lecture 10	Learning Vectorial Representations Of Words		| | | | | | | | | | | |
Lecture 11	Recurrent Neural Networks, Backpropagation Through Time (BPTT), Vanishing and Exploding Gradients, Truncated BPTT		| | | |
Lecture 12	Gated Recurrent Units (GRUs), Long Short Term Memory (LSTM) Cells, Solving the vanidhing gradient problem with LSTMs		| | |	(Nice blog)
Lecture 13	Encoder Decoder Models, Attention Mechanism, Attention over images, Hierarchical Attention		| |
Lecture 14	Transformers: Multi-headed Self Attention, Cross Attention		| | | |	-->
Directed Graphical Models	|
Lecture 17	Markov Networks	|
Lecture 18	Using joint distributions for classification and sampling, Latent Variables, Restricted Boltzmann Machines, Unsupervised Learning, Motivation for Sampling	|
Lecture 19	Markov Chains, Gibbs Sampling for training RBMs, Contrastive Divergence for training RBMs	|
Lecture 20	Variational autoencoders	|
Lecture 21	Autoregressive Models: NADE, MADE, PixelRNN	|
Lecture 22	Generative Adversarial Networks (GANs)	|

Quizzes/Assigments

	Topics	Resources	Release Date	Submission Date
History of DL		05-Feb-2021	15-May-2021
Assignment 1 ( )	Feedforward Neural Networks		19-Feb-2024	10-Mar-2024
Assignment 2 ( )	Convolutional Neural Networks		03-Mar-2024	03-Apr-2024
Assignment 3 ( )	Recurrent Neural Networks		03-Mar-2024	03-Apr-2024
Assignment 4 ( )	RBMs and GANs		03-Mar-2024	03-Mar-2024
Assignment 5 ( )	Transformers		22-Mar-2024	22-Apr-2024
		17-Feb-2022	--
Quiz 2			24-Mar-2022	--
Endsem			11-May-2024 (Saturday)	--

	Topics	Resources	Release Date	Submission Date
Project Part 1	Automatic validation of speech data		18-01-2022	12-Mar-2022
Project Part 2	ASR applications for Indian languages		18-01-2022	27-Apr-2022

	Topics	Resources	Release Date	Submission Date
Tutorial 1	Calculus
Tutorial 2	Linear Algebra
Tutorial 3	MP Neurons, Perceptrons
Tutorial 4	Sigmoid Neurons, Gradient Descent
Tutorial 5	Feedforward Neural Networks, Backpropagation
Lectures 1-7	-	20-Feb-2019
Assignment 4	Convolutional Neural Networks [Programming]	-	28-Feb-2019	15-Mar-2019
Quiz II	Lectures 8-15	-	27-Apr-2019
Assignment 5	Recurrent Neural Networks [Programming]	-	15-Mar-2019	10-Apr-2019
Assignment 6	Probability Refresher [Theory]	-	10-Apr-2019	15-Apr-2019
Assignment 7	Variational Autoencoders [Programming]	-	10-Apr-2019	26-Apr-2019
End Sem	Lectures 1-23	-	01-May-2019

Deep Learning for Computer Vision [from Stanford]

Deep Learning for NLP [from Stanford]

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Coursera: Neural Networks and Deep Learning - All weeks solutions [Assignment + Quiz] - deeplearning.ai

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

=== Week 1 ===

Assignments: .

• No Assignment for Week 1
• Neural Networks and Deep Learning (Week 1) Quiz ▸  Introduction to deep learning

=== Week 2 ===

Assignments:.

• Neural Networks and Deep Learning (Week 2) [Assignment Solution] ▸ Logistic Regression with a Neural Network mindset.
• Neural Networks and Deep Learning (Week 2) Quiz ▸  Neural Network Basics

=== Week 3 ===

• Neural Networks and Deep Learning (Week 3) [Assignment Solution] ▸ Planar data classification with one hidden layer.
• Neural Networks and Deep Learning (Week 3) Quiz ▸  Shallow Neural Networks

=== Week 4 ===

• Neural Networks and Deep Learning (Week 4A) [Assignment Solution] ▸ Building your Deep Neural Network: Step by Step.
• Neural Networks and Deep Learning (Week 4B) [Assignment Solution] ▸ Deep Neural Network for Image Classification: Application.
• Neural Networks and Deep Learning (Week 4) Quiz ▸  Key concepts on Deep Neural Networks

hello ,Can u send me the for deeplerning specialization assignment file(unsolved Zip file) actually i can not these afford there course if u can send those file it will be very helpfull to me Thanks [email protected]

Sorry. I can't do that.

Thank u So Much.

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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

Course Certificate

Category: Nptel Assignment Answers 2024

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Where can I get the assignment solutions for Coursera: Neural Networks and Deep Learning Course by deeplearning.ai?

I think Coursera is the best place to start learning “Machine Learning” by Andrew NG (Stanford University) followed by Neural Networks and Deep Learning by same tutor. This course is full of theory required with practical assignments in MATLAB & Python. It recommended to solve the assignments honestly by yourself for full understanding.

I have done the same. In case you stuck in between, You can refer my solutions just for understanding. Don’t just copy paste it.

(These solution might be helpful for you to understand the deep learning in better way…)

I have recently completed that and these are the solutions for the Coursera: Neural Networks and Deep learning course by Home - deeplearning.ai Assignment Solutions in Python.

I have tried to provide optimized solutions :

Logistic Regression with a Neural Network mindset: Coursera: Neural Networks and Deep Learning (Week 2) [Assignment Solution] - deeplearning.ai

Planar data classification with one hidden layer: Coursera: Neural Networks and Deep Learning (Week 3) [Assignment Solution] - deeplearning.ai

Building your Deep Neural Network: Step by Step: Coursera: Neural Networks and Deep Learning (Week 4A) [Assignment Solution] - deeplearning.ai

Deep Neural Network for Image Classification: Application: Coursera: Neural Networks and Deep Learning (Week 4B) [Assignment Solution] - deeplearning.ai

Thanks, - Akshay P Daga

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Deep Learning - IIT Ropar

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A deep learning specialization series of 5 courses offered by Andrew Ng at Coursera

abhishektripathi24/Deep-Learning-Specialization-Coursera

Folders and files.

Course Status :	Completed
Course Type :	Elective
Duration :	12 weeks
Category :
Credit Points :	3
Undergraduate/Postgraduate
Start Date :	25 Jul 2022
End Date :	14 Oct 2022
Enrollment Ends :	08 Aug 2022
Exam Date :	30 Oct 2022 IST

Name		Name
19 Commits

Repository files navigation

Deep learning specialization.

Master Deep Learning, and Break into AI

Instructor: Andrew Ng Community: deeplearning.ai

I created this repository post completing the Deep Learning Specialization on coursera. Its includes solutions to the quizzes and programming assignments which are required for successful completion of the courses.

Note: Coursera Honor Code advise against plagiarism. Readers are requested to use this repo only for insights and reference. If you are undertaking these courses at coursera, please submit you original work only.

The constitution of the repository as per course modules, quizzes and programming assignments is as follows:

• Quiz - Introduction to deep learning
• Quiz - Neural Network Basics
• Programming Assignment - Python basics with numpy
• Programming Assignment - Logistic Regression with a Neural Network mindset
• Quiz - Shallow Neural Networks
• Programming Assignment - Planar data classification with a hidden layer
• Programming Assignment - Building your Deep Neural Network: Step by Step
• Programming Assignment - Deep Neural Network - Application
• Quiz - Practical aspects of deep learning
• Programming Assignment - Initialization
• Programming Assignment - Regularization
• Programming Assignment - Gradient Checking
• Quiz - Optimization algorithms
• Programming Assignment - Optimization
• Quiz - Hyperparameter tuning, Batch Normalization, Programming Frameworks
• Programming Assignment - Tensorflow
• Quiz - Bird recognition in the city of Peacetopia (case study)
• Quiz - Autonomous driving (case study)
• Quiz - The basics of ConvNets
• Programming Assignment - Convolutional Model: step by step
• Programming Assignment - Convolutional Model: application
• Quiz - Deep convolutional models
• Programming Assignment - Keras Tutorial
• Programming Assignment - Residual Networks
• Quiz - Detection algorithms
• Programming Assignment - Car detection with YOLO
• Quiz - Special applications: Face recognition & Neural style transfer
• Programming Assignment - Art generation with Neural Style Transfer
• Programming Assignment - Face Recognition
• Quiz - 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
• Quiz - Natural Language Processing & Word Embeddings
• Programming Assignment - Operations on word vectors - Debiasing
• Programming Assignment - Emojify
• Quiz - Sequence models & Attention mechanism
• Programming Assignment - Neural Machine Translation with Attention
• Programming Assignment - Trigger word detection

Lecture Notes References

Here are some references of lecture notes and reviews drawn by some communities, authors and editors -

• https://www.deeplearning.ai/ai-notes/
• https://www.slideshare.net/TessFerrandez/notes-from-coursera-deep-learning-courses-by-andrew-ng
• https://towardsdatascience.com/deep-learning-specialization-by-andrew-ng-21-lessons-learned-15ffaaef627c

Acknowledgement

Deep Learning Specialization offered by Andrew Ng is an excellent blend of content for deep learning enthusiasts. I thoroughly enjoyed the course and earned the certificate .

• Jupyter Notebook 100.0%