Video description
"A great introduction to using Keras for deep learning."
Daniel Williams, Software Professional
Keras in Motion teaches you to build neural-network models for real-world data problems using Python and Keras. In over two hours of hands-on, practical video lessons, you'll apply Keras to common machine learning scenarios, ranging from regression and classification to implementing Autoencoders and applying transfer learning. In each crystal-clear video module, you'll put your new knowledge into practice, as you teach your network to recognize text and even create an algorithm for a self-driving car!
What you will learn:- Regression and classification problems
- Using neural networks for image processing
- Building autoencoders
- Designing and implementing a self-driving car
- Hands-on coding with practical examples
Designed for intermediate-level data scientists, developers, and machine learning engineers. Code examples are in Python.
Dan Van Boxel is an engineer and data scientist with a background in both engineering and mathematics. On his livestream, Dan demonstrates a different machine learning library, method, or model weekly.
Makes deep learning much more straight forward.
Peter Hampton, AI Researcher, Ulster University
The instructor is capable of breaking complex concepts into easily understandable examples.
Gustavo Patino, Assistant Professor, Oakland University William Beaumont School of Medicine
Table of Contents
UNIT 1 - Installation and Basics
What is Keras
00:03:58
Installation
00:05:03
Fitting Lines
00:06:34
Linear Regression in Keras
00:09:17
Predicting Categories
00:09:55
Logistic Regression in Keras
00:05:08
UNIT 2 - Font Recognition
Exploring Font Data
00:05:48
Neural Networks Review
00:04:01
Neural Networks in Keras
00:05:22
Finding Simple Features
00:07:14
Convolutional Neural Networks in Keras
00:07:32
Visualization and Wrap Up
00:07:13
UNIT 3 - Self Driving Car
Exploring Self Driving Car Data
00:06:29
Simple Steering Model
00:05:02
Convolutional Steering Model
00:07:56
Combined Steering Throttle Model
00:02:46
Summary and Extensions
00:03:22
UNIT 4 - Autoencoders
Introduction to Autoencoders
00:03:47
Autoencoding MNIST Digits
00:07:59
Transfer Learning
00:04:19
Course Review
00:02:38
UNIT 5 - Converting Keras
Converting Keras1 to Keras2
00:03:15