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Deep Learning with TensorFlow LiveLessons

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Deep Learning with TensorFlow LiveLessons

Online Video

  • Your Price: $199.99
  • List Price: $249.99
  • Estimated Release: Aug 28, 2017
  • About this video
  • Video accessible from your Account page after purchase.


  • Copyright 2018
  • Edition: 1st
  • Online Video
  • ISBN-10: 0-13-477085-4
  • ISBN-13: 978-0-13-477085-7

6+ Hours of Video Instruction

Deep Learning with TensorFlow LiveLessons is an introduction to Deep Learning that bring the revolutionary machine-learning approach to life with interactive demos from the most popular Deep Learning library, TensorFlow, and its high-level API, Keras. Essential theory is whiteboarded to provide an intuitive understanding of Deep Learning’s underlying foundations, i.e., artificial neural networks. Paired with tips for overcoming common pitfalls and hands-on code run-throughs provided in Python-based Jupyter notebooks, this foundational knowledge empowers individuals with no previous understanding of neural networks to build powerful state-of-the-art Deep Learning models.

Skill Level

  • Intermediate

Learn How To

  • Build Deep Learning models in TensorFlow and Keras
  • Interpret the results of Deep Learning models
  • Troubleshoot and improve Deep Learning models
  • Understand the language and fundamentals of artificial neural networks
  • Build your own Deep Learning project

Who Should Take This Course

This course is perfectly suited to software engineers, data scientists, analysts, and statisticians with an interest in Deep Learning. Code examples are provided in Python, so familiarity with it or another object-oriented programming language would be helpful. Previous experience with statistics or machine learning is not necessary.

Course Requirements

Some experience with any of the following are an asset, but none are essential:

  • Object-oriented programming, specifically Python
  • Simple shell commands, e.g., in Bash
  • Machine learning or statistics
  • First-year college calculus

About the Instructor

Jon Krohn is the chief data scientist at untapt, a machine learning startup in New York. He leads a Deep Learning Study Group and, having obtained his doctorate in neuroscience from Oxford University, continues to publish academic papers.

About Pearson Video Training

Pearson publishes expert-led video tutorials covering a wide selection of technology topics designed to teach you the skills you need to succeed. These professional and personal technology videos feature world-leading author instructors published by your trusted technology brands: Addison-Wesley, Cisco Press, Pearson IT Certification, Prentice Hall, Sams, and Que. Topics include: IT Certification, Network Security, Cisco Technology, Programming, Web Development, Mobile Development, and more.  Learn more about Pearson Video training at  http://www.informit.com/video.

Sample Content

Table of Contents


Lesson 1: Introduction to Deep Learning


1.1 Neural Networks and Deep Learning

1.2 Running the Code in These LiveLessons

1.3 An Introductory Artificial Neural Network

Lesson 2: How Deep Learning Works


2.1 The Families of Deep Neural Nets and their Applications

2.2 Essential Theory I—Neural Units, Cost Functions, Gradient Descent, and Backpropagation

2.3 TensorFlow Playground—Visualizing a Deep Net in Action

2.4 Data Sets for Deep Learning

2.5 Applying Deep Net Theory to Code I

Lesson 3: Convolutional Networks


3.1 Essential Theory II—Mini-Batches, Unstable Gradients, and Avoiding Overfitting

3.2 Applying Deep Net Theory to Code II

3.3 Introduction to Convolutional Neural Networks for Visual Recognition

3.4 Classic ConvNet Architectures—LeNet-5

3.5 Classic ConvNet Architectures—AlexNet and VGGNet

3.6 TensorBoard and the Interpretation of Model Outputs

Lessons 4: Introduction to TensorFlow


4.1 Comparison of the Leading Deep Learning Libraries

4.2 Introduction to TensorFlow

4.3 Fitting Models in TensorFlow

4.4 Dense Nets in TensorFlow

4.5 Deep Convolutional Nets in TensorFlow

Lesson 5: Improving Deep Networks


5.1 Improving Performance and Tuning Hyperparameters

5.2 How to Build Your Own Deep Learning Project

5.3 Resources for Self-Study



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