Oreilly – Deep Learning with JavaScript 2020-2

Deep Learning with JavaScript. This course helps JavaScript developers implement deep learning applications directly in the browser or Node.js. Written by Google engineers, Deep Learning with JavaScript is a comprehensive guide to building machine learning models using TensorFlow.js. This technology enables the creation of intelligent applications in areas such as computer vision, natural language processing, and image recognition without the need for languages ​​such as Python. TensorFlow.js is highly flexible and scalable, and the models generated in it can be run on any platform that JavaScript supports. This book covers basic to advanced concepts such as transfer learning and image generation, with practical examples including text analysis, audio processing, and game AI. By following this course, developers can develop deep learning-based applications efficiently and practically.

What you will learn

• Image and Language Processing in the Browser: Participants learn how to perform image and natural language processing tasks directly in the browser.
• Tuning Machine Learning Models with Client-Side Data: Learn how to optimize machine learning models using data available on the client side (browser).
• Text and Image Creation with Generative Deep Learning: Participants will learn the principles and applications of generative deep learning for text and image generation.
• Source code samples for testing and modification: This course provides ready-made code samples that you can test and modify based on your needs.

This course is suitable for people who:

• JavaScript Developers Interested in Deep Learning: This course is suitable for developers who are fluent in JavaScript and want to enter the field of deep learning.

Deep Learning with JavaScript Course Details

• Publisher: Oreilly
• Instructor:   Stanley Bileschi ,  Shanqing Cai ,  Eric Nielsen
• Training level: Beginner to advanced
• Training duration: 16 hours and 52 minutes

Course headings

• Part 1. Motivation and basic concepts
  Chapter 1. Deep learning and JavaScript
• Part 2. A gentle introduction to TensorFlow.js
  Chapter 2. Getting started: Simple linear regression in TensorFlow.js
  Chapter 3. Adding nonlinearity: Beyond weighted sums
  Chapter 4. Recognizing images and sounds using convnets
  Chapter 5. Transfer learning: Reusing pretrained neural networks
• Part 3. Advanced deep learning with TensorFlow.js
  Chapter 6. Working with data
  Chapter 7. Visualizing data and models
  Chapter 8. Underfitting, overfitting, and the universal workflow of machine learning
  Chapter 9. Deep learning for sequences and text
  Chapter 10. Generative deep learning
  Chapter 11. Basics of deep reinforcement learning
• Part 4. Summary and closing words
  Chapter 12. Testing, optimizing, and deploying models
  Chapter 13. Summary, conclusions, and beyond
• Appendix B. A quick tutorial of tensors and operations in TensorFlow.js

Course images

Sample course video

Download link

Download Part 1 – 1 GB

Download Part 2 – 1 GB

Download Part 3 – 536 MB

File(s) password: www.downloadly.ir

File size

2.5 GB