muhac/coursera-deep-learning-solutions

Solutions of Deep Learning Specialization by Andrew Ng on Coursera

Solutions Manual _{Deep Learning Andrew Ng}

Solutions manual to accompany Deep Learning Specialization on Coursera.

Programming Assignments	Course A	Course B	Course C	Course D	Course E
Practice Questions	Course A	Course B	Course C	Course D	Course E

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Programming Assignments

Course A - Neural Networks and Deep Learning

Week 2 - Neural Networks Basics

• Python Basics with numpy (optional)

• Logistic Regression with a Neural Network mindset

Week 3 - Shallow Neural Networks

• Planar data classification with a hidden layer

Week 4 - Deep Neural Networks

• Building your Deep Neural Network: Step by Step

• Deep Neural Network - Application

Course B - Improving Deep Neural Networks

Week 1 - Practical Aspects of Deep Learning

• Initialization

• Regularization

• Gradient Checking

Week 2 - Optimization Algorithms

• Optimization

Week 3 - Hyperparameter Tuning, Batch Normalization and Programming Frameworks

• TensorFlow

Course D - Convolutional Neural Networks

Week 1 - Foundations of Convolutional Neural Networks

• Convolutional Model: step by step

• Convolutional Model: application

Week 2 - Deep Convolutional Models: case studies

• Keras Tutorial (not graded)

• Residual Networks

Week 3 - Object Detection

• Car detection with YOLO

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

• Art generation with Neural Style Transfer

• Face Recognition

Course E - Sequence Models

Week 1 - Recurrent Neural Networks

• Building a recurrent neural network - step by step

• Dinosaur Island - Character-Level Language Modeling

• Jazz improvisation with LSTM

Week 2 - Natural Language Processing & Word Embeddings

• Operations on word vectors - Debiasing

• Emojify

Week 3 - Sequence Models & Attention Mechanism

• Neural Machine Translation with Attention

• Trigger word detection

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Practice Questions

Course A - Neural Networks and Deep Learning

Week 1 - Introduction to Deep Learning

• Introduction to deep learning

Week 2 - Neural Networks Basics

• Neural Network Basics

Week 3 - Shallow Neural Networks

• Shallow Neural Networks

Week 4 - Deep Neural Networks

• Key concepts on Deep Neural Networks

Course B - Improving Deep Neural Networks

Week 1 - Practical Aspects of Deep Learning

• Practical aspects of deep learning

Week 2 - Optimization Algorithms

• Optimization algorithms

Week 3 - Hyperparameter Tuning, Batch Normalization and Programming Frameworks

• Hyperparameter tuning, Batch Normalization and Programming Frameworks

Course C - Structuring Machine Learning Projects

Week 1 - ML Strategy (1)

• Bird recognition in the city of Peacetopia (case study)

Week 2 - ML Strategy (2)

• Autonomous driving (case study)

Course D - Convolutional Neural Networks

Week 1 - Foundations of Convolutional Neural Networks

• The basics of ConvNets

Week 2 - Deep Convolutional Models: case studies

• Deep convolutional models

Week 3 - Object Detection

• Detection algorithms

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

• Special applications: Face recognition & Neural style transfer

Course E - Sequence Models

Week 1 - Recurrent Neural Networks

• Recurrent Neural Networks

Week 2 - Natural Language Processing & Word Embeddings

• Natural Language Processing & Word Embeddings

Week 3 - Sequence Models & Attention Mechanism

• Sequence Models & Attention Mechanism