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

More Information
Learn
  • Understand how to implement Convolutional Neural Networks and use them for Computer Vision
  • Learn how to build Recurrent Neural Network models and use them for Natural Language Processing tasks
  • Apply foundational models in Reinforcement Learning
  • Use Deep Learning models implemented in TensorFlow to solve problems in many domains
  • Start building your own Deep Learning applications
About

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-

Style and Approach

The course takes a Cookbook approach and will show you how to build models to solve problems in different domains such as computer vision, natural language processing, Reinforcement Learning, Finance, and more.

Features
  • Learn how to build models to solve problems in Computer vision, Natural Language Processing, Reinforcement Learning, Finance, and more 
  • A hands-on approach to the most exciting applications of Deep Learning
  • Understand how to build advanced Deep Learning models with TensorFlow
Course Length 3 hours 5 minutes
ISBN9781788623209
Date Of Publication 27 Jun 2018

Authors

Alvaro Fuentes

Alvaro Fuentes is a data scientist with more than 12 years of experience in analytical roles. He holds an M.S. in applied mathematics and an M.S. in quantitative economics. He worked for many years in the Central Bank of Guatemala as an economic analyst, building models for economic and financial data. He founded Quant Company to provide consulting and training services in data science topics and has been a consultant for many projects in fields such as business, education, medicine, and mass media, among others.

He is a big Python fan and has been using it routinely for five years to analyze data, build models, produce reports, make predictions, and build interactive applications that transform data into intelligence.