Last updated 6/2021
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz
Language: English | Size: 541.29 MB | Duration: 1h 25m
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
Homepage
Code:
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