Deep Learning

This repository contains Jupyter notebooks implementing Deep Learning algorithms to solve different problems.

The notebooks use Keras and Tensorflow 2.1.

Table of contents

• 1 - Logistic Regression with a Neural Network
• 2 - Planar Data Classification with a Neural Network
• 3 - Deep Neural Networks: Application
• 4 - Building your Deep Neural Network: Step by Step
• 5 - Initialization
• 6 - Regularization and Dropout
• 7 - Optimization Methods
• 8 - Multi-Class Classification with a Neural Network
• 9 - Convolutional Neural Networks: Application
• 10 - Building your Convolutional Neural Network: Step by Step
• 11 - CNN Visualization
• 12 - Data Augmentation
• 13 - Transfer Learning
• 14 - Fully Convolutional Networks
• 15 - Batch Normalization: Emotion Detection
• 16 - Residual Networks
• 17 - Siamese Network: Face Recognition
• 18 - Neural Style Transfer
• 19 - Recurrent Neural Networks: Application
• 20 - Building your Recurrent Neural Network: Step by Step
• 21 - LSTM: Application
• 22 - Embeddings
• 23 - Embeddings: Application
• 24 - Bidirectional LSTM: Application
• 25 - Trigger Word Detection with a Sequential Model
• 26 - Time Series Forecasting