StephanePEILLET/Deep_Learning_Specialization

Neural networks theory, improving networks: hyperparameter tuning, regularization and optimization, structuring ML projects, CNN, RNN, NLP

Deep Learning Specialization

This repository contains notebooks of the Deep Learning Specialization offered by deeplearning.ai on Coursera.

Course 1 - Neural Networks and Deep Learning

1. Introduction to deep learning
2. Neural Networks Basics
3. Shallow neural networks
4. Deep Neural Networks

Course 2 - Improving DNN: Hyperparameter tuning, Regularization and Optimization

1. Practical aspects of Deep Learning
   - Initialization
   - Regularization
   - Gradient Checking
2. Optimization Methods
3. Hyperparameter tuning, Batch Normalization and Programming Frameworks

Course 3 - Structuring Machine Learning Projects

1. Introduction to ML Strategy
2. ML Strategy

Course 4 - Convolutional Neural Networks

1. [Foundations of Convolutional Neural Networks]
   - Convolution Model - Application
   - Convolution Model - Step by Step
2. Deep convolutional models: case studies - ResNets
3. Object detection (Car Autonomous driving)
4. Special applications:
   - Face recognition
   - Neural style transfer

Course 5 - Sequence Models

1. Recurrent Neural Networks
   - RNN Step by Step
   - Character-Level Language Modeling
   - Jazz improvisatoin with LSTM
2. Natural Language Processing & Word Embeddings
   - Emojify
   - Word Vector Representation
3. Sequence models & Attention mechanism
   -Machine Translation
   -Trigger Word Detection