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].
Announcement
[!IMPORTANT] Check our latest paper (accepted in ICDAR’23) on Urdu OCR
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 | 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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IMAGES
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Notes, programming assignments and quizzes from all courses within the Coursera Deep Learning specialization offered by deeplearning.ai: (i) Neural Networks and Deep Learning; (ii) Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization; (iii) Structuring Machine Learning Projects; (iv) Convolutional Neural Networks; (v) Sequence Models - amanchadha/coursera-deep ...
Programming assignments from all courses in the Coursera Deep Learning specialization offered by deeplearning.ai.. Instructor: Andrew Ng In five courses, you will learn the foundations of Deep Learning, understand how to build neural networks, and learn how to lead successful machine learning projects.
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. - abdur75648/Deep-Learning-Specialization-Coursera
Announcement [!IMPORTANT] Check our latest paper (accepted in ICDAR'23) on Urdu OCR — 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 What's New. This Specialization was updated in April 2021 to include developments in deep learning and programming frameworks.
Week 1: Introduction to deep learning. Be able to explain the major trends driving the rise of deep learning, and understand where and how it is applied today. Quiz 1: Introduction to deep learning; Week 2: Neural Networks Basics. Learn to set up a machine learning problem with a neural network mindset. Learn to use vectorization to speed up ...
Neural Networks and Deep Learning: A Textbook. Springer. 2019. Dive into Deep Learning / Schedule *M = Module (each lecture is broken down into smaller modules) Lecture# Contents Lecture pdf ... Assignment 1 (Graded) Feedforward Neural Networks: Link 19-Feb-2024: 10-Mar-2024: Assignment 2 (Ungraded ) Convolutional Neural Networks: Link: 03-Mar ...
The culmination of all of the Homework Part 1's will be your own custom deep learning library MyTorch©, along with detailed examples. It is structured similarly to popular deep library learning libraries like PyTorch and TensorFlow, and you can easily import and reuse modules of code for your subsequent homeworks.
Click here to see solutions for all Machine Learning Coursera Assignments. Click here to see more codes for Raspberry Pi 3 and similar Family. Click here to see more codes for NodeMCU ESP8266 and similar Family. Click here to see more codes for Arduino Mega (ATMega 2560) and similar Family. Feel free to ask doubts in the comment section. I will try my best to answer it.
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 ...
🔊 Deep Learning NPTEL Elective Course July 2022🔴ABOUT THE COURSE :Deep Learning has received a lot of attention over the past few years and has been employ...
Solutions of Deep Learning Specialization by Andrew Ng on Coursera - muhac/coursera-deep-learning-solutions. ... Programming Assignments. Course A - Neural Networks and Deep Learning. Week 2 - Neural Networks Basics. Python Basics with numpy (optional)
Nptel Assignment Answers 2024. Sorted: Introduction To Industry 4.0 And Industrial Internet Of Things Programming Data Structure And Algorithms Using Python Artificial Intelligence Search Methods For Problem Solving Machine Learning and Deep Learning - Fundamentals and Applications.
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
The Deep Learning Specialization is a foundational program that will help you understand the capabilities, challenges, and consequences of deep learning and prepare you to participate in the development of leading-edge AI technology.. In this Specialization, you will build and train neural network architectures such as Convolutional Neural Networks, Recurrent Neural Networks, LSTMs ...
#deeplearning #nptel #npteldeeplearning Deep Learning In this video, we're going to unlock the answers to the Deep Learning questions from the NPTEL 2024 Jan...
In this course we will learn about the building blocks used in these Deep Learning based solutions. Specifically, we will learn about feedforward neural networks, convolutional neural networks, recurrent neural networks and attention mechanisms. ... Average assignment score = 25% of average of best 8 assignments out of the total 12 assignments ...
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.
Kingisepp. Kingisepp is a small town on the Luga River in the southwestern part of the West Leningrad Oblast, near the border with Estonia, a regional center with a turbulent military past and a quiet provincial present. Photo: Serko, Public domain. Photo: Anastasia6786, CC BY-SA 4.0.
Contents: Cities and Settlements The population of all cities and urban settlements in Leningrad Oblast according to census results and latest official estimates. The icon links to further information about a selected place including its population structure (gender).
This repository contains all the solutions of the programming assignments along with few output images. It also has some of the important papers which are referred during the course. NOTE : Use the solutions only for reference purpose :) This specialisation has five courses. Courses: Course 1: Neural Networks and Deep Learning. Learning Objectives:
Kingisepp. The city of Kingisepp was founded as the Yam Fortress in 1384; it was later know as Yamburg before being given its current name in honour of the Estonian revolutionary Viktor Kingisepp. This small city can be easily visited along with Ivangorod and from here it is possible to get to the impressive ruins of the Koporye Fortress.
The constitution of the repository as per course modules, quizzes and programming assignments is as follows: Neural Networks and Deep Learning. week 1 Quiz - Introduction to deep learning; week 2 Quiz - Neural Network Basics; Programming Assignment - Python basics with numpy; Programming Assignment - Logistic Regression with a Neural Network ...
Kingisepp 2D Maps. This page provides an overview of Kingisepp, Leningrad Oblast, Northwest, Russia region maps. Maps show the Kingisepp as seen from above. Choose from a wide range of map styles and many different color schemes. Get free map for your website.