Coursera

My notes / works from Coursera courses.

Introduction

This repository contains my solutions for labs and programming assignments on Coursera courses. Certain resources required by the codes may be lacking due to limitations on downloading course materials from Coursera and uploading them to GitHub. The lacking resources are mostly datasets, pre-trained models or certain weight matrices.

Courses

TensorFlow: Advanced Techniques (Specialization)

1. Custom Models, Layers, and Loss Functions with TensorFlow

Details

Week 1 - Functional APIs

Week 2 - Custom Loss Functions

Lab: Huber Loss lab
Lab: Huber Loss object
Lab: Contrastive loss in the siamese network (same as week 1's siamese network)
Programming Assignment: Creating a custom loss function

Week 3 - Custom Layers

Week 4 - Custom Models

Week 5 - Bonus Content - Callbacks

2. Custom and Distributed Training with Tensorflow

Details

Week 1 - Differentiation and Gradients

Week 2 - Custom Training

Week 3 - Graph Mode

Week 4 - Distributed Training

3. Advanced Computer Vision with TensorFlow

Details

Week 1 - Introduction to Computer Vision

Week 2 - Object Detection

Week 3 - Image Segmentation

Week 4 - Visualization and Interpretability

DeepLearning.AI TensorFlow Developer (Specialization)

1. Introduction to TensorFlow for Artificial Intelligence, Machine Learning, and Deep Learning

Details

2. Convolutional Neural Networks in TensorFlow

Details

Week 1 - Exploring a Larger Dataset

Programming Assignment: Exercise 1 - Cats vs. Dogs

Week 2 - Augmentation: A Technique to Avoid Overfitting

Programming Assignment: Exercise 2 - Cats vs. Dogs using augmentation

Week 3 - Transfer Learning

Programming Assignment: Exercise 3 - Horses vs. humans using Transfer Learning

Week 4 - Multiclass Classifications

Programming Assignment: Exercise 4 - Multi-class classifier

Unable to download horse-or-human.zip

3. Natural Language Processing in TensorFlow

Details

Week 1 - Sentiment in Text

Week 2 - Word Embeddings

Week 3 - Sequence Models

Week 4 - Sequence Models and Literature

4. Sequences, Time Series and Prediction

Details

Week 1 - Sequences and Prediction

Week 2 - Deep Neural Networks for Time Series

Week 3 - Recurrent Neural Networks for Time Series

Week 4 - Real-world Time Series Data

Generative Adversarial Networks (GANs) (Specialization)

1. Build Basic Generative Adversarial Networks (GANs)

Details

Week 1 - Intro to GANs

Week 2 - Deep Convolutional GANs

Programming Assignment: Deep Convolutional GAN (DCGAN)

Week 3 - Wasserstein GANs with Gradient Penalty

Week 4 - Conditional GAN & Controllable Generation

2. Build Better Generative Adversarial Networks (GANs)

Details

Week 1 - Evaluation of GANs

Unable to download inception_v3_google-1a9a5a14.pth, fid_images_tensor.npz

Week 2 - GAN Disadvantages and Bias

Week 3 - StyleGAN and Advancements

3. Apply Generative Adversarial Networks (GANs)

Details

Week 1 - GANs for Data Augmentation and Privacy

Programming Assignment: Data Augmentation

Week 2 - Image-to-Image Translation with Pix2Pix

Unable to download pix2pix_15000.pth, maps

Week 3 - Unpaired Translation with CycleGAN

Unable to download horse2zebra, cycleGAN_100000.pth

Programming Assignment: CycleGAN

Natural Language Processing (Specialization)

1. Natural Language Processing with Classification and Vector Spaces

Details

Week 1 - Sentiment Analysis with Logistic Regression

Week 2 - Sentiment Analysis with Naive Bayes

Week 3 - Vector Space Models

Week 4 - Machine Translation and Document Search

2. Natural Language Processing with Probabilistic Models

Details

Week 1 - Autocorrect

Week 2 - Part of Speech Tagging and Hidden Markov Models

Week 3 - Autocomplete and Language Models

Week 4 - Word Embeddings with Neural Networks

3. Natural Language Processing with Sequence Models

Details

Week 1 - Neural Netowrks for Sentiment Analysis

Week 2 - Recurrent Neural Networks for Language Modelling

Week 3 - LSTMs and Named Entity Recognition

Week 4 - Siamese Networks

4. Natural Language Processing with Attention Models

Details

Week 1 - Neural Machine Translation

Week 2 - Text Summarization

Week 3 - Question Answering

Week 4 - Chatbot

Deep Learning (Specialization)

1. Neural Networks and Deep Learning

Details

Week 1 - Introduction to Deep Learning

No labs / programming assignments

Week 2 - Neural Network Basics

Week 3 - Shallow Neural Networks

Programming Assignment: Planar data classification with a hidden layer

Week 4 - Deep Neural Networks

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

Details

Week 1 - Practical Aspects of Deep Learning

Week 2 - Optimization Algorithms

Programming Assignment: Optimization

Week 3 - Hyperparameter Tuning, Batch Normalization and Programming Frameworks

Programming Assignment: Tensorflow

3. Structuring Machine Learning Projects

Details

No labs / programming assignments

4. Convolutional Neural Networks

Details

Week 1 - Foundations of Convolutional Neural Networks

Week 2 - Deep Convolutional Models: Case Studies

Week 3 - Object Detection

Programming Assignment: Car detection with YOLO

Week 4 - Special Applications: Face Recognition & Neural Style Transfer

5. Sequence Models

Details

Week 1 - Recurrent Neural Networks

Week 2 - Natural Language Processing & Word Embeddings

Week 3 - Sequence Models & Attention Mechanism

Contributing

Please refer to CONTRIBUTE.md for details. 😍

License

Coursera is licensed under the MIT license.

Name		Name	Last commit message	Last commit date
Latest commit History 101 Commits
Advanced Computer Vision with TensorFlow		Advanced Computer Vision with TensorFlow
Apply Generative Adversarial Networks (GANs)		Apply Generative Adversarial Networks (GANs)
Build Basic Generative Adversarial Networks (GANs)		Build Basic Generative Adversarial Networks (GANs)
Build Better Generative Adversarial Networks (GANs)		Build Better Generative Adversarial Networks (GANs)
Convolutional Neural Networks in TensorFlow		Convolutional Neural Networks in TensorFlow
Convolutional Neural Networks		Convolutional Neural Networks
Custom Models, Layers, and Loss Functions with TensorFlow		Custom Models, Layers, and Loss Functions with TensorFlow
Custom and Distributed Training with Tensorflow		Custom and Distributed Training with Tensorflow
Improving Deep Neural Networks Hyperparameter tuning, Regularization and Optimization		Improving Deep Neural Networks Hyperparameter tuning, Regularization and Optimization
Introduction to TensorFlow for Artificial Intelligence, Machine Learning, and Deep Learning		Introduction to TensorFlow for Artificial Intelligence, Machine Learning, and Deep Learning
Natural Language Processing in TensorFlow		Natural Language Processing in TensorFlow
Natural Language Processing with Attention Models		Natural Language Processing with Attention Models
Natural Language Processing with Classification and Vector Spaces		Natural Language Processing with Classification and Vector Spaces
Natural Language Processing with Probabilistic Models		Natural Language Processing with Probabilistic Models
Natural Language Processing with Sequence Models		Natural Language Processing with Sequence Models
Neural Networks and Deep Learning		Neural Networks and Deep Learning
Sequence Models		Sequence Models
Sequences, Time Series and Prediction		Sequences, Time Series and Prediction
Structuring Machine Learning Projects		Structuring Machine Learning Projects
.gitattributes		.gitattributes
.gitignore		.gitignore
CONTRIBUTE.md		CONTRIBUTE.md
LICENSE		LICENSE
README.md		README.md

License

y33-j3T/Coursera-Deep-Learning

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Coursera

Contents

Introduction

Courses

TensorFlow: Advanced Techniques (Specialization)

1. Custom Models, Layers, and Loss Functions with TensorFlow

Week 1 - Functional APIs

Week 2 - Custom Loss Functions

Week 3 - Custom Layers

Week 4 - Custom Models

Week 5 - Bonus Content - Callbacks

2. Custom and Distributed Training with Tensorflow

Week 1 - Differentiation and Gradients

Week 2 - Custom Training

Week 3 - Graph Mode

Week 4 - Distributed Training

3. Advanced Computer Vision with TensorFlow

Week 1 - Introduction to Computer Vision

Week 2 - Object Detection

Week 3 - Image Segmentation

Week 4 - Visualization and Interpretability

DeepLearning.AI TensorFlow Developer (Specialization)

1. Introduction to TensorFlow for Artificial Intelligence, Machine Learning, and Deep Learning

Week 1 - A New Programming Paradigm

Week 2 - Introduction to Computer Vision

Week 3 - Enchancing Vision with Convolutional Neural Networks

Week 4 - Using Real-world Images

2. Convolutional Neural Networks in TensorFlow

Week 1 - Exploring a Larger Dataset

Week 2 - Augmentation: A Technique to Avoid Overfitting

Week 3 - Transfer Learning

Week 4 - Multiclass Classifications

3. Natural Language Processing in TensorFlow

Week 1 - Sentiment in Text

Week 2 - Word Embeddings

Week 3 - Sequence Models

Week 4 - Sequence Models and Literature

4. Sequences, Time Series and Prediction

Week 1 - Sequences and Prediction

Week 2 - Deep Neural Networks for Time Series

Week 3 - Recurrent Neural Networks for Time Series

Week 4 - Real-world Time Series Data

Generative Adversarial Networks (GANs) (Specialization)

1. Build Basic Generative Adversarial Networks (GANs)

Week 1 - Intro to GANs

Week 2 - Deep Convolutional GANs

Week 3 - Wasserstein GANs with Gradient Penalty

Week 4 - Conditional GAN & Controllable Generation

2. Build Better Generative Adversarial Networks (GANs)

Week 1 - Evaluation of GANs

Week 2 - GAN Disadvantages and Bias

Week 3 - StyleGAN and Advancements

3. Apply Generative Adversarial Networks (GANs)

Week 1 - GANs for Data Augmentation and Privacy

Week 2 - Image-to-Image Translation with Pix2Pix

Week 3 - Unpaired Translation with CycleGAN

Natural Language Processing (Specialization)

1. Natural Language Processing with Classification and Vector Spaces

Week 1 - Sentiment Analysis with Logistic Regression

Week 2 - Sentiment Analysis with Naive Bayes

Week 3 - Vector Space Models

Week 4 - Machine Translation and Document Search

2. Natural Language Processing with Probabilistic Models

Week 1 - Autocorrect

Week 2 - Part of Speech Tagging and Hidden Markov Models

Week 3 - Autocomplete and Language Models

Week 4 - Word Embeddings with Neural Networks

3. Natural Language Processing with Sequence Models

Week 1 - Neural Netowrks for Sentiment Analysis

Week 2 - Recurrent Neural Networks for Language Modelling

Week 3 - LSTMs and Named Entity Recognition

Week 4 - Siamese Networks

4. Natural Language Processing with Attention Models

Week 1 - Neural Machine Translation