Illustrated Guide to Deep Learning with TensorFlow: Introduction, Principles, and Advanced Practice

During the learning process, I collected relevant flowcharts for each chapter of the book “Deep Learning with TensorFlow: Introduction, Principles, and Advanced Practice.” This makes the knowledge points in the book more intuitive and easier for readers to learn and review. The flowcharts were compiled by a team I encountered before, known as the Code Doctors, and I am sharing them with everyone here.

1. Basic Development Steps of TensorFlow

2. Basic Programming in TensorFlow

3. Recognizing Blurred Handwritten Digits in Images

4. Single Neuron

5. Multilayer Neural Network

6. Convolutional Neural Network

7. Recurrent Neural Network – Backward Subtraction Analysis

8. Recurrent Neural Network – RNN Fitting Echo Sequence Analysis

9. Recurrent Neural Network – RNN Practical Application

10. Recurrent Neural Network – Improved Principle Flowchart

11. Recurrent Neural Network – RNN for Speech Recognition Process

12. Recurrent Neural Network – seq2seq Organization

13. Autoencoder Network

14. Deep Neural Network

15. Generative Adversarial Network – infoGAN Example

16. Generative Adversarial Network – Adversarial Network Optimization

“Deep Learning with TensorFlow” is written in a “theory + practice” format, providing a comprehensive and in-depth explanation of both the principles of deep learning neural networks and the usage of TensorFlow through a large number of examples (96 in total). The examples in the book are highly practical, covering topics such as image classification, creating a simple chatbot, and image recognition. Each chapter in the book is accompanied by a teaching video that corresponds to the key content of the book, helping readers quickly grasp the main points of that chapter. The book also provides free source code and data samples for all examples, which not only facilitates learning for readers but also provides convenience for their future work.

The book consists of 12 chapters, divided into 3 parts. Part 1 covers the basics of deep learning and TensorFlow, including a quick introduction to artificial intelligence and TensorFlow, setting up the development environment, basic development steps of TensorFlow, basic programming in TensorFlow, and recognizing blurred handwritten digits; Part 2 focuses on the basics of deep learning – neural networks, introducing the basic models of neural networks, including single neurons, multilayer neural networks, convolutional neural networks, recurrent neural networks, and autoencoder networks; Part 3 is about advanced deep learning, which involves flexible application and combination of basic network models, synthesizing and elevating the previous knowledge, including deep neural networks and generative adversarial networks.

This book has a clear structure, rich cases, is easy to understand, and is highly practical, making it particularly suitable for beginners and advanced readers of TensorFlow deep learning as a self-study tutorial. Additionally, this book is also suitable as a textbook for relevant training schools and as a teaching reference for related majors in various universities.

Table of Contents:

Part 1: Basics of Deep Learning and TensorFlow Chapter 1: Quick Introduction to Artificial Intelligence and TensorFlow Chapter 2: Setting Up the Development Environment Chapter 3: Basic Development Steps of TensorFlow – Example of Fitting 2D Data with Logistic Regression Chapter 4: Basic Programming in TensorFlow Chapter 5: Recognizing Blurred Handwritten Digits (Example 21) Part 2: Basics of Deep Learning – Neural Networks Chapter 6: Single Neuron Chapter 7: Multilayer Neural Network – Solving Non-linear Problems Chapter 8: Convolutional Neural Network – Solving the Problem of Too Many Parameters Chapter 9: Recurrent Neural Network – Network with Memory Function…

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