Master Autoencoders In Keras

Complete course on Autoencoders and its variants with implementation in Keras

What you’ll learn
Master Autoencoders and its different models using Keras.
Requirements
Basic understanding of Neural Networks and Python
Description
Autoencoders are a very popular neural network architecture in Deep Learning. It consists of 2 parts – Encoder and Decoder. Encoder encodes the data into some smaller dimension, and Decoder tries to reconstruct the input from the encoded lower dimension. The lowest dimension is known as Bottleneck layer. So, it can be used for Data compression.In this course we explore the different types of Autoencoders, starting from simple to complex models. We’ll also look at how to implement different Autoencoder models using Keras, which one of the most popular Deep Learning frameworks.

Overview

Section 1: Introduction

Lecture 1 Introduction

Lecture 2 What are Autoencoders?

Section 2: Simple Autoencoder in Keras

Lecture 3 Simple Autoencoder implementation in Keras

Lecture 4 Simple Autoencoder – Visualizing Encoded output

Section 3: Deep Autoencoders in Keras

Lecture 5 Deep Autoencoder using Sequential API

Lecture 6 Deep Autoencoders using Keras Functional API

Section 4: Convolutional Autoencoder

Lecture 7 Convolutional Autoencoder – Functional API

Machine learning Engineers, Data Scientists, Research Engineers, Software Developers