"Machine Learning Algorithms and Implementations - Python Programming and Application Examples" - First Post Introduction

传媒学子

"Machine Learning Algorithms and Implementations - Python Programming and Application Examples" - First Post Introduction [Copy link]

Introduction: I am usually very busy at work and very tired after work, so it is difficult to find time to make electronic products. Therefore, I have missed many interesting review activities in the forum. By chance, I found that the forum recently launched some excellent books for everyone to review. I saw that this book is very consistent with the field of artificial intelligence that I am currently engaged in, so I participated in this event. Because I received the book around June 27, the first sharing is a few days later than the original plan. I spent a few days reading the overall framework of the book and chapters 1-3, and I would like to share it with you.

About the Author:

An excellent book must be written by an excellent author. The author of this book, Mr. Bu Shuhui, is a professor at Northwestern Polytechnical University. Together with teachers and students from the Aircraft Intelligent Cognition and Control Laboratory of Northwestern Polytechnical University, he compiled this book for everyone. Mr. Bu is the winner of the Wu Wenjun First Prize for Artificial Intelligence Science and Technology Invention in 2021. I flipped through this book and found that it is a rare book introducing artificial intelligence in terms of both breadth and depth of knowledge. The editor-in-chief, Tan Meiping, also put a lot of effort. The entire layout looks very refreshing, the whole book has beautiful drawings, elegant fonts, and professionalism.

Introduction to the book framework:

This book starts with the history of machine learning, describes the basic concepts of machine learning, the basic knowledge of the Python programming language commonly used in machine learning, common algorithms for machine learning, and neural networks (deep neural networks) commonly used in the current popular artificial intelligence. Finally, using the Pytorch framework as a carrier, it describes the basic principles and applications of some common algorithms for target detection. The book goes from the simple to the complex, combining theory with practice. This is my initial feeling. I will see if it matches my own cognition after reading it.

Chapter 1 Introduction – Reading Notes

I used to be confused about the difference between artificial intelligence and machine learning, but I got a good explanation from this book. Artificial intelligence is a broader concept than machine learning. Machine learning is an important method in the field of artificial intelligence. The essence of machine learning is a general term for a class of algorithms that can mine implicit rules from a large amount of historical data and use them for classification, regression, and clustering.

Both regression and classification belong to supervised learning. Common regressions include linear regression, predicting future house prices and temperatures, etc. The purpose of classification problems is to label things, and the results are generally discrete.

Generalization: The model's ability to recognize new inputs. The better the recognition ability, the stronger the generalization ability.

The generalization performance is very poor, indicating that the model is overfitting. Overfitting means that the model performs very well on the training data set, but has poor recognition ability for new data sets.

Classification of machine learning: supervised learning, unsupervised learning and semi-supervised learning. Deep learning belongs to semi-supervised learning and is progressing towards unsupervised learning, but there is still a lot to do.

Artificial neural networks can be divided into shallow neural networks and deep neural networks. Deep neural networks are a new research direction in machine learning and are closest to realizing artificial intelligence. Deep neural networks are inspired by the way the human brain works. Multi-layer networks can better mine image features and thus improve the detection rate of target difference recognition.

The method of learning artificial intelligence should be from the shallow to the deep, and gradually make up the basics. We should not hesitate because it involves too much knowledge, nor should we ignore its theoretical basis because of too much focus on model learning.

Chapter 2 Python Language and Chapter 3 Python Common Libraries

Python is an interpreted language. From my perspective, Python helps me save a lot of programming time and has a relatively low learning cost. Recently, pytorch is a popular language for artificial intelligence learning. As the name suggests, py is inseparable from python. I won't go into details. With the help of the community version of pycharm and anaconda, a basic artificial intelligence model training and learning platform can be implemented.

秦天qintian0303

Have you taken a general look at what the main focus of this book is?

Verifone

This kind of algorithm book is a headache to read. It takes too much time to calm down and is difficult to understand.

通途科技

Primary school students who are learning machine learning algorithms and implementations, come on! ! !

nian1206

Mark it and I will use it later. Thank you very much for sharing. May good people have a peaceful life!

传媒学子

Qintianqintian0303 posted on 2024-7-5 09:13 Have you taken a general look at what the main focus of this book is?

I will answer your question after I finish reading this book. This book is a relatively introductory textbook and is relatively comprehensive.

传媒学子

Verifone posted on 2024-7-5 10:23 This kind of algorithm book is a headache to read. It takes too much time to calm down. It is difficult to understand

Actually, it’s not that hard. If you have more experience with some things, it’s not difficult to understand. The core of AI is to design your own model, and many of us are actually trying to figure out how to use it. We may not even be able to use it well. AI is a combination of software and hardware, and it covers everything from mathematics to modeling to chip design.

tryisbest

Machine learning algorithms and implementations, keep learning new things as they come out, thanks for sharing!

传媒学子

Qintianqintian0303 posted on 2024-7-5 09:13 Have you taken a general look at what the main focus of this book is?

This book focuses on machine learning, from traditional machine learning to neural networks, deep neural networks, from basic mathematical theory to Python implementation, all are explained in a relatively thorough manner.