TensorFlow is an upcoming library that is backed by Google, quickly spawning very interesting projects. The flexible architecture allows you to deploy computation to one or more CPUs or GPUs in a desktop, server, or mobile device with a single API. TensorFlow facilitates ML to build and train systems, in particular neural networks, similar to the ways that humans use reasoning and observation to learn.

This comprehensive 3-in-1 course will teach you how to build Deep Learning models with TensorFlow by clear recipes. You’ll improve the performance and speed of your machine learning models by applying advanced Deep Learning techniques. You’ll also learn how to use Deep Learning and TensorFlow to solve valuable problems using real-world datasets. You’ll upgrade your knowledge to the second generation of machine learning with this guide on TensorFlow.

Contents and Overview

This training program includes 3 complete courses, carefully chosen to give you the most comprehensive training possible.

The first course, Machine Learning with TensorFlow, covers Google’s TensorFlow library and helps you build deployable solutions. Each video in this course addresses common commercial machine learning problems using Google’s TensorFlow library. It will not only help you discover what TensorFlow is and how to use it, but will also show you the unbelievable things that can be done in machine learning with the help of examples/real-world use cases.

The second course,TensorFlow 1.X Recipes for Supervised and Unsupervised Learning, covers 19 hands-on recipes that will help you perform advanced Machine Learning with TensorFlow. 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.

The third course, TensorFlow for Machine Learning Solutions, covers machine learning concepts using the latest numerical computing library – TensorFlow. The independent solutions in this video course will teach you how to use TensorFlow for complex data computations and will let you dig deeper and gain more insights into your data than ever before. You’ll work through solutions on training models, model evaluation and sentiment analysis – each using Google’s machine learning library TensorFlow.

By the end of this training program you’ll be able to tackle common machine learning problems and build deployable solutions using machine learning concepts with Google’s TensorFlow library.

About the Authors

• Shams Ul Azeem is an undergraduate in electrical engineering from NUST Islamabad, Pakistan. He has a great interest in the computer science field, and he started his journey with Android development. Now, he’s pursuing his career in Machine Learning, particularly in deep learning, by doing medical-related freelancing projects with different companies. He was also a member of the RISE lab, NUST, and he has a publication credit at the IEEE International Conference, ROBIO as a co-author of Designing of motions for humanoid goalkeeper robots.
  
  
• Alvaro Fuentes is a Data Scientist with an M.S. in Quantitative Economics and a M.S. in Applied Mathematics with more than 10 years of experience in analytical roles. He worked 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, Psychology and Mass Media. He also has taught many (online and in-site) courses to students from around the world in topics like Data Science, Mathematics, Statistics, R programming and Python. Alvaro Fuentes is a big Python fan and has been working with Python for about 4 years and uses it routinely for analyzing data and producing predictions. He also has used it in a couple of software projects. He is also a big R fan, and doesn't like the controversy between what is the “best” R or Python, he uses them both. He is also very interested in the Spark approach to Big Data, and likes the way it simplifies complicated

  things. He is not a software engineer or a developer but is generally interested in web technologies. He also has technical skills in R programming, Spark, SQL (PostgreSQL), MS Excel, machine learning, statistical analysis, econometrics, mathematical modeling. Predictive Analytics is a topic in which he has both professional and teaching experience. Having solved practical problems in his consulting practice using the Python tools for predictive analytics and the topics of predictive analytics are part of a more general course on Data Science with Python that he teaches online.
  
  

• Nick McClure is currently a senior data scientist at PayScale, Inc. in Seattle, WA. Prior to this, he has worked at Zillow and Caesar's Entertainment. He got his degrees in Applied Mathematics from The University of Montana and the College of Saint Benedict and Saint John's University. He has a passion for learning and advocating for analytics, machine learning, and artificial intelligence. Nick occasionally puts his thoughts and musings on his blog, or through his Twitter account, @nfmcclure.