InformIT – Deep Learning with Tensorflow, Keras, and PyTorch LiveLesson (Video Training), 2nd Edition 2020-2 – Download

explanation

Deep Learning LiveLessons with Tensorflow, Keras, and PyTorch, 2nd Edition is an introductory course focused on applying deep learning with TensorFlow, Keras, and PyTorch. In this course, you will learn more about the principles of deep learning, or artificial intelligence, and work practically using the most famous deep learning libraries.

What to Learn in Deep Learning with Tensorflow, Keras, and PyTorch LiveLesson (Video Training), 2nd Edition:

Build deep learning models from all major libraries, including TensorFlow, Keras, and PyTorch.
Understanding the language and theory of artificial neural networks
Excellence in a wide range of computing problems, including machine vision, natural language processing, and reinforcement learning
Building algorithms with modern technology
Do your own deep learning project

Course specifications

Posted by InformIT
teacher: john crone
Language: English
Education level: medium
Course: 38
Duration: 7 hours 19 minutes

Course Topics:

introduction

Unit 1: Introduction to Deep Learning and Artificial Intelligence
subject
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 Deep Net in Action
1.6 Running hands-on code examples in a Jupyter notebook
1.7 Introductory Neural Networks with TensorFlow and Keras – Part 1
1.8 Introductory Neural Networks with TensorFlow and Keras – Part 2

Lesson 2: How Deep Learning Works
subject
2.1 Nerve units – Part 1
2.2 Nerve Unit – Part 2
2.3 Neural Networks – Part 1
2.4 Neural Networks – Part 2
2.5 Deep Neural Network Training – Part 1
2.6 Deep Neural Network Training – Part 2
2.7 Deep Neural Network Training – Part 3
2.8 Intermediate Neural Networks using TensorFlow and Keras

Lesson 3: High-Performance Deep Learning Networks
subject
3.1 Weight initialization
3.2 Unstable gradients and batch normalization
3.3 Model generalization – avoid overfitting
3.4 Awesome Optimizer
3.5 Deep neural networks using TensorFlow and Keras
3.6 Regression model
3.7 Interpretation of TensorBoard and model output

Lesson 4: Convolutional Neural Networks
subject
4.1 Convolutional layer
4.2 ConvNet using TensorFlow and Keras
4.3 Machine vision applications

Unit 5: Developing your own deep learning project
subject
5.1 Comparison of major deep learning libraries
5.2 Deep Learning using PyTorch – Part 1
5.3 Deep Learning using PyTorch – Part 2
5.4 Hyperparameter tuning
5.5 Datasets for deep learning and resources for unsupervised learning

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