Practical Deep Learning with Keras and Python

Learn to apply machine learning to your problems. Follow a complete pipeline, including pre-processing and training, using Keras and Python.

Course curriculum

1. About the Instructor
2. Dive into Machine Learning
3. Making Predictions
1. Machine Learning Pipeline
2. Regression
3. Binary and Multi-class Classification
4. Recap and a Link to More Theory
1. Environment Setup for Windows (and some issues with it)
2. Environment Setup for Mac and Linux
1. Data Preparation
2. Training and Testing
1. Problem Description and Data View
2. Pre-processing the Data
3. Loading Data and Getting the Shapes Right
4. Train, Test Split
5. Shapes in Depth (or how to avoid headaches)
6. Sequential Model
7. Functional API
1. Basics and Rationale
2. CNN in Keras (or why Keras is better than your ML tool)
3. Pooling (and why it's not that important)
4. Dropout (and why you should always consider it)
1. Functional API for CNN
2. Inception Module
3. Residual Connections
1. Saving and Loading Model Weights
2. Parting Words

Enroll today