The Complete Guide to TensorFlow 1.x

Become an expert in machine learning and deep learning with the new TensorFlow 1.x
RRP $124.99

Are you a data analyst, data scientist, or a researcher looking forward to a guide that will help you increase the speed and efficiency of your machine learning activities? If yes, then this course is for you!


Learn to implement TensorFlow in production


Perform highly accurate and efficient numerical computing with TensorFlow


Unlock the advanced techniques that bring more accuracy and speed to machine learning activities


Explore various possibilities with deep learning and gain amazing insights from data

Duration: 6 hours

Google's brainchild TensorFlow, in its first year, has more than 6000 open source repositories online. It has helped engineers, researchers, and many others make significant progress with everything from voice/sound recognition to language translation and face recognition. It has also proved to be useful in the early detection of skin cancer and preventing blindness in diabetics. TensorFlow is designed to make distributed machine and deep learning easy for everyone, but using it requires understanding some general principles and algorithms. Furthermore, the latest release of TensorFlow comes with lots of exciting features. It’s incredibly fast, flexible, and more production-ready than ever!

The aim of the course is to help you tackle the common commercial machine learning and deep learning problems that you’re facing in your day-to-day activities.

This Learning Journey begins with an introduction to machine learning and deep learning. You will explore the main features and capabilities of TensorFlow such as computation graph, data model, programming model, and TensorBoard. The key highlight is the course will teach you how to upgrade our code from TensorFlow 0.x to TensorFlow 1.x. Next, you will learn the different techniques of machine learning such as clustering, linear regression, and logistic regression with the help of real-world projects and examples. You will also learn the concepts of reinforcement learning, the Q-learning algorithm, and the OpenAI Gym framework. Moving ahead you will dive into neural networks and see how convolution, recurrent, and deep neural networks work and the main operation types used in building them. Next, you will learn the advanced concepts such as GPU computing and multimedia programming.  Finally, the course demonstrate an example on deep learning on Android using TensorFlow.

By the end of this course, you will have a solid knowledge of the all-new TensorFlow and be able to implement it efficiently in production.

What am I going to get from this course?

  • Learn about machine learning landscapes along with the historical development and progress of deep learning
  • Load, interact, process, and save complex datasets
  • Solve classification and regression problems using state-of-the-art techniques
  • Train machines quickly to learn from data by exploring reinforcement learning techniques
  • Classify images using deep neural network schemes
  • Learn about deep machine intelligence and GPU computing
  • Explore active areas of deep learning research and applications

Style and Approach

This course takes a step-by-step approach to teach you how to implement TensorFlow in production. Starting with the basics of TensorFlow, you will learn machine learning and deep learning techniques, along with the advanced concepts of TensorFlow. With the help of real-world projects and examples, this course will help you apply Tensorflow's features from scratch.

This course is a blend of text, videos, code examples, and assessments, all packaged up keeping your journey in mind. The curator of this course has combined some of the best that Packt has to offer in one complete package. It includes content from the following Packt products:

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