How do beginners learn about AI from scratch? You'll understand when you've finished. ai

Link: oschina.net/news/78629/beginners-how-to-learn-from-zero-artificial-intelligence

This is a list of the best learning resources for beginners who want to enter the field of artificial intelligence, but do not know where to start.

First, machine learning

For the best introduction to the field of machine learning, watch Coursera's Andrew Ng machine learning course. It explains the basic concepts and gives you a good understanding of the most important algorithms.

For a brief overview of the ML algorithm, check out this tutsplus course "Machine Learning distilled".

The book "Programming Collective Intelligence" is a good resource for learning the actual implementation of the ML algorithm in Python. It takes you through many practical projects to cover all the necessary foundations.

You may also be interested in these good resources:

Perer Norvig udacity Course on ml (ml udacity course)

Tom Mitchell at Cameron University Professor Another Course on ml (another ML course)

A machine learning tutorial on YouTube Mathematicalmonk

Second, the depth of learning

For the best introduction to depth learning, the best I've encountered is Deep Learning with Python. It doesn't go deep into difficult math, nor does it have a long list of prerequisites, but describes a simple way to start a DL, explaining how to quickly start building and learn everything in practice. It explains the most advanced tools (Keras,tensorflow) and takes you through several practical projects to explain how to achieve the most advanced results in all the best DL applications.

Google also has a great introductory DL course, and Sephen Welch's great explanation of neural.

Then, to get a better understanding, there are some interesting resources here:

Geoffrey Hinton's Coursera course "neural Networks for Machine Learning". This course will take you through the classical problem of ANN--mnist character recognition process, and will explain everything in depth.

MIT Deep Learning (Deep learning) book.

UFLDL Tutorial by Stanford (Stanford UFLDL Tutorial)

Deeplearning.net Tutorial

Michael Nielsen's neural Networks and Deep Learning (neural network and deep learning) book

Simon O. Haykin's neural Networks and Learning machines (neural Network and machine learning) book

Third, artificial intelligence

"Artificial intelligence:a Modern Approach (Aima)" (Artificial Intelligence: Modern method) is the best book on "Old School" AI. This book outlines the field of artificial intelligence in general and explains all the basic concepts you need to know.

Artificial Intelligence Course (AI) from the University of California, Berkeley, is a series of excellent video lectures that explain the basics through a very interesting practice program (training AI to play Pacman games). I recommend that you read Aima together at the same time because it is based on this book and explains a lot of similar concepts from different angles, making them easier to understand. The explanation is relatively deep and is a very good resource for beginners.

How the Brain works

If you're interested in AI, you might want to know how people's brains work, and the following books will explain the best modern theories in an intuitive and interesting way.

Jeff Hawkins on Intelligence (audio readings)

Gödel, Escher, Bach

I suggest getting started with these two books, and they're a good way to explain the general theory of brain work.

Other resources:

Ray Kurzweil's How to create a Mind (create a Brain Ray Kurzweil) (audio readings).

Principles of Neural Science (neuroscience) is the best book I can find, deep in NS. It talks about core science, neurological anatomy, and so on. Very interesting, but also very long-I am still reading it.

Four, mathematics

Here are the very basic mathematical concepts you need to learn about AI:

Calculus

Khan Academy Calculus videos (calculus video of Khan College)

MIT Lectures on Multivariable calculus (mit Lectures on Multivariable calculus)

Linear algebra

Khan Academy Linear algebra videos (linear algebra video of Khan College)

MIT linear algebra Videos by Gilbert Strang (mit linear algebra Video of Gilbert Strang)

Coding The matrix (coded matrices)-Brown University thread algebra CS Course

Probability and statistics

Khan College Probability (probability) and Statistics (statistics) video

edx probability Course (edx probability course)

V. Computer Science

To master AI, you should be familiar with computer science and programming.

If you've just started, I suggest reading Dive into Python 3 (Deep Python 3), and most of the knowledge you need in Python programming will be mentioned.

To learn more about the nature of computer programming-see this classic MIT course (mit Course). This is one of the most influential books on the basics of Lisp and Computer science, based on CS-structure and the interpretation of a computerized program.

Vi. Other Resources

metacademy– is your knowledge of the "Package manager." You can use this great tool to understand all the prerequisites you need to learn about different ml topics.

kaggle– Machine Learning Platform