Advanced Deep Learning with Keras [Video]

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Advanced Deep Learning with Keras [Video]

Philippe Remy

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Explore Deep Learning with Keras

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Video Details

ISBN 139781788623957
Course Length5 hours and 11 minutes

Video Description

Keras is an open source neural network library written in Python. It is capable of running on top of MXNet, Deeplearning4j, Tensorflow, CNTK, or Theano. Designed to enable fast experimentation with deep neural networks, it focuses on being minimal, modular, and extensible.

This course provides a comprehensive introduction to deep learning. We start by presenting some famous success stories and a brief recap of the most common concepts found in machine learning. Then, we introduce neural networks and the optimization techniques to train them. We’ll show you how to get ready with Keras API to start training deep learning models, both on CPU and on GPU. Then, we present two types of neural architecture: convolutional and recurrent neural networks

First, we present a well-known use case of deep learning: recommender systems, where we try to predict the "rating" or "preference" that a user would give to an item. Then, we introduce an interesting subject called style transfer. Deep learning has this ability to transform images based on a set of inputs, so we’ll morph an image with a style image to combine them into a very realistic result. In the third section, we present techniques to train on very small datasets. This comprises transfer learning, data augmentation, and hyperparameter search, to avoid overfitting and to preserve the generalization property of the network.

Finally, we complete this course by what Yann LeCun, Director at Facebook, considered as the biggest breakthrough in Machine Learning of the last decade: Generative Adversarial Networks. These networks are amazingly good at capturing the underlying distribution of a set of images to generate new images.

Style and Approach

Expect a smooth combination of theory and practice. Code examples illustrate all the important concepts in the course, and you can implement them yourself, guided by the course

Table of Contents

Introduction to Deep Learning
The Course Overview
What is Deep Learning?
Machine Learning Concepts
Foundations of Neural Networks
Optimization
Get Started with Keras
Configuration of Keras
Presentation of Keras and Its API
Design and Train Deep Neural Networks
Regularization in Deep Learning
Convolutional and Recurrent Neural Networks
Introduction to Computer Vision
Convolutional Networks
CNN Architectures
Image Classification Example
Image Segmentation Example
Introduction to Recurrent Networks
Recurrent Neural Networks
“One to Many” Architecture
“Many to One” Architecture
“Many to Many” Architecture
Embedding Layers
Recommender Systems
What are Recommender Systems?
Content/Item Based Filtering
Collaborative Filtering
Hybrid System
Neural Style Transfer
Introduction to Neural Style Transfer
Single Style Transfer
Advanced Techniques
Style Transfer Explained
Advanced Techniques
Data Augmentation
Transfer Learning
Hyper Parameter Search
Natural Language Processing
Generative Adversarial Networks
An Introduction to Generative Adversarial Networks (GAN)
Run Our First GAN
Deep Convolutional Generative Adversarial Networks (DCGAN)
Techniques to Improve GANs

What You Will Learn

  • Understand the main concepts of machine learning and deep learning
  • Install and use Python and Keras to build deep learning models
  • Build, train, and run fully-connected, convolutional and recurrent neural networks
  • Optimize deep neural networks through efficient hyper parameter searches
  • See many real-world applications to identify which tasks can be leveraged with deep learning
  • Work with any kind of data involving images, text, time series, sound and videos
  • Use GPUs to leverage the training experience.
  • Discover some advanced neural architectures such as generative adversarial networks
  • Find out about a wide range of subjects from recommender systems to transfer learning

Authors

Table of Contents

Introduction to Deep Learning
The Course Overview
What is Deep Learning?
Machine Learning Concepts
Foundations of Neural Networks
Optimization
Get Started with Keras
Configuration of Keras
Presentation of Keras and Its API
Design and Train Deep Neural Networks
Regularization in Deep Learning
Convolutional and Recurrent Neural Networks
Introduction to Computer Vision
Convolutional Networks
CNN Architectures
Image Classification Example
Image Segmentation Example
Introduction to Recurrent Networks
Recurrent Neural Networks
“One to Many” Architecture
“Many to One” Architecture
“Many to Many” Architecture
Embedding Layers
Recommender Systems
What are Recommender Systems?
Content/Item Based Filtering
Collaborative Filtering
Hybrid System
Neural Style Transfer
Introduction to Neural Style Transfer
Single Style Transfer
Advanced Techniques
Style Transfer Explained
Advanced Techniques
Data Augmentation
Transfer Learning
Hyper Parameter Search
Natural Language Processing
Generative Adversarial Networks
An Introduction to Generative Adversarial Networks (GAN)
Run Our First GAN
Deep Convolutional Generative Adversarial Networks (DCGAN)
Techniques to Improve GANs

Video Details

ISBN 139781788623957
Course Length5 hours and 11 minutes
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