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7+ Hours of Video Instruction
An intuitive, application-focused introduction to deep learning and TensorFlow, Keras, and PyTorch
Overview
Deep Learning with TensorFlow, Keras, and PyTorch LiveLessons is an introduction to deep learning that brings the revolutionary machine-learning approach to life with interactive demos from the most popular deep learning library, TensorFlow, and its high-level API, Keras, as well as the hot new library PyTorch. Essential theory is whiteboarded to provide an intuitive understanding of deep learnings 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.
Customer Review
I enjoyJon's material because he painstakingly walks you through the mechanics of theoperation.
Skill Level
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