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TensorFlow 1.X Recipes for Supervised and Unsupervised Learning [Video]

More Information
  • Define and work with the main objects in the TensorFlow library
  • Understand the basic workflow of building models and implement TensorFlow programs
  • Build Deep Learning models and use them to solve real problems
  • Gain practice using both the low-level and the high-level APIs of TensorFlow and understand which one is better for your project 
  • Boost the performance of the traditional supervised and unsupervised machine learning models with the use of Deep Learning

Deep Learning models often perform significantly better than traditional machine learning algorithms in many tasks. This course consists of hands-on recipes to use deep learning in the context of supervised and unsupervised learning tasks.

After covering the basics of working with TensorFlow, it shows you how to perform the traditional machine learning tasks in supervised learning: regression and classification. This course also covers how to perform unsupervised learning using cutting-edge techniques from Deep Learning.

To address many different use cases, this product presents recipes for both the low-level API (TensorFlow core) as well as the high-level APIs (tf.contrib.lean and Keras).

All the code and supporting files for this course are available on Github at https://github.com/PacktPublishing/TensorFlow-1.X-Recipes-for-Supervised-and-Unsupervised-Learning

Style and Approach

The course takes a recipe-based approach and will show you how to perform traditional machine learning tasks in supervised learning and also covers how to perform unsupervised learning using cutting-edge techniques from Deep Learning.

  • Learn quickly how to build Deep Learning models with TensorFlow by following clear recipes
  • Learn how to improve the performance and speed of your machine learning models by applying advanced Deep Learning techniques
  • Learn how to use Deep Learning and TensorFlow to solve valuable problems using real-world datasets
Course Length 3 hours 19 minutes
Date Of Publication 21 Mar 2018


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.