This repository contains Jupyter notebooks implementing Deep Learning algorithms to solve different problems.
The notebooks use Keras and Tensorflow 2.1.
- 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