Deep Learning with Tensorflow Keras and PyTorch Live Lessons

Description

Deep Learning with Tensorflow, Keras and PyTorch LiveLessons (Video Training), 2nd Edition, is an introductory training course focusing on the application of deep learning using TensorFlow, Keras and PyTorch. In this course, you will become more familiar with the theory of the basics of deep learning, i.e. artificial intelligence, and you will also work practically with the most famous deep learning libraries.

What you will learn in Deep Learning with Tensorflow, Keras and PyTorch LiveLessons (Video Training), 2nd Edition:

Building deep learning models in all major libraries: TensorFlow, Keras and PyTorch
Understanding the language and theory of artificial neural networks
Excelling in a wide range of computational problems including machine vision, natural language processing and reinforcement learning
Making algorithms with modern technology
Do your own deep learning projects by yourself

Course details

Publisher: InformIT
Instructors: Jon Krohn
English language
Education level: Intermediate
Number of courses: 38
Duration: 7 hours and 19 minutes

Course topics:

Introduction

Lesson 1: Introduction to Deep Learning and Artificial Intelligence
Topics
1.1 Neural Networks, Machine Learning, and Artificial Intelligence–Part 1
1.2 Neural Networks, Machine Learning, and Artificial Intelligence–Part 2
1.3 A Visual Introduction to Deep Learning–Part 1
1.4 A Visual Introduction to Deep Learning–Part 2
1.5 TensorFlow Playground–Visualizing a Deep Net in Action
1.6 Running the Hands-On Code Examples in Jupyter Notebooks
1.7 An Introductory Neural Network with TensorFlow and Keras–Part 1
1.8 An Introductory Neural Network with TensorFlow and Keras–Part 2

Lesson 2: How Deep Learning Works
Topics
2.1 Neural Units–Part 1
2.2 Neural Units–Part 2
2.3 Neural Networks–Part 1
2.4 Neural Networks–Part 2
2.5 Training Deep Neural Networks–Part 1
2.6 Training Deep Neural Networks–Part 2
2.7 Training Deep Neural Networks–Part 3
2.8 An Intermediate Neural Net with TensorFlow and Keras

Lesson 3: High-Performance Deep Learning Networks
Topics
3.1 Weight Initialization
3.2 Unstable Gradients and Batch Normalization
3.3 Model Generalization – Avoiding Overfitting
3.4 Fancy Optimizers
3.5 A Deep Neural Net with TensorFlow and Keras
3.6 Regression Models
3.7 TensorBoard and the Interpretation of Model Outputs

Lesson 4: Convolutional Neural Networks
Topics
4.1 Convolutional Layers
4.2 A ConvNet with TensorFlow and Keras
4.3 Machine Vision Applications

Lesson 5: Moving Forward with Your Own Deep Learning Projects
Topics
5.1 Comparison of the Leading Deep Learning Libraries
5.2 Deep Learning with PyTorch–Part 1
5.3 Deep Learning with PyTorch–Part 2
5.4 Hyperparameter Tuning
5.5 Datasets for Deep Learning and Resources for Self-Study

Summary