Video description
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.
The companion materials for this LiveLesson can be found at https://github.com/the-deep-learners/TensorFlow-LiveLessons/.
Skill Level
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.
Table of Contents
Introduction
Deep Learning with TensorFlow: Introduction
Lesson 1: Introduction to Deep Learning
Topics
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
Topics
2.1 The Families of Deep Neural Nets and their Applications
2.2 Essential Theory I—Neural Units
2.3 Essential Theory II—Cost Functions, Gradient Descent, and Backpropagation
2.4 TensorFlow Playground—Visualizing a Deep Net in Action
2.5 Data Sets for Deep Learning
2.6 Applying Deep Net Theory to Code I
Lesson 3: Convolutional Networks
Topics
3.1 Essential Theory III—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
Lesson 4: Introduction to TensorFlow
Topics
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
Topics
5.1 Improving Performance and Tuning Hyperparameters
5.2 How to Build Your Own Deep Learning Project
5.3 Resources for Self-Study
Summary
Deep Learning with TensorFlow: Summary