Book Image

TensorFlow 1.x Deep Learning Recipes for Artificial Intelligence Applications [Video]

By : Alvaro Fuentes
Book Image

TensorFlow 1.x Deep Learning Recipes for Artificial Intelligence Applications [Video]

By: Alvaro Fuentes

Overview of this book

This course is all about some of the most exciting applications of Deep Learning and how to implement them in TensorFlow. You will learn how to build models to solve problems in different domains such as Computer vision, Natural Language Processing, Reinforcement Learning, Finance, and more. Taking a Cookbook approach, this course presents you with easy-to-follow recipes to show the use of advanced Deep Learning techniques and their implementation in TensorFlow. After taking this tutorial you will be able to start building advanced Deep Learning models with TensorFlow for applications with a wide range of fields. All the code and supporting files for this course are available on Github at https://github.com/PacktPublishing/-TensorFlow-1.X-Deep-Learning-Recipes-for-Artificial-Intelligence-Applications-v-

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TensorFlow 1.x Deep Learning Recipes for Artificial Intelligence Applications [Video]
Alvaro Fuentes
Jun, 2018 | 3 hours 5 minutes
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Table of Contents (4 chapters)
Free Chapter
1
Convolutional Neural Networks for Computer Vision
The Course Overview
Installation and Setup
Defining Layers for Image Recognition
Building an Image Classifier with CNNs
Building Better CNNs with Regularization
Transfer Learning
2
Applications of Recurrent Neural Networks
The Intuition Behind RNNs
Time Series Forecasting with RNN
Producing Word Embeddings for NLP Tasks
Processing Text Sequences with LSTM Networks
3
Application of Deep Learning to AI Problems
Guessing Correlations from Scatter Plots
Introduction to Generative Adversarial Networks
Creating Images with GANs
Sequence to Sequence Models
Building a Language Translator
4
Introduction to Reinforcement Learning
Key Concepts in Reinforcement Learning
A Simple Environment and Basic Policies
Training a Neural Network Policy
Using an Intelligent Agent
Chapter 1
Convolutional Neural Networks for Computer Vision
Content Locked
Section 5
Building Better CNNs with Regularization
Show how to build better CNN models using regularization and different architectures of layers. - Show a different option of architectures of layers that can be used in CNN - Show how to add dropout regularization to a CNN - Compare the results of the new model in term of classification accuracy
Previous Section
End of Section 5
Next Section