The artificial neural network (ANN) is the core behind all the rapid development of AI technology in the past 15 years. The foundation of ANN is the model of a single neuron, called Perceptron, and the layered network formed by Perceptrons, called Multi-layer Perceptron (MLP). A solid understanding of Perceptron and MLP will enable you to grasp the basic ideas of the new AI technology. Here are some tutorials that can help you enter this fancy world of AI.

Friendly introduction  with Python implementation from Python Simplified:

1. Machine Learning FOR BEGINNERS - Supervised, Unsupervised and Reinforcement Learning (8:47)

2. Neural Network Simply Explained - ML for Beginners (6:37)

3. Perceptron Algorithm with Code Example - ML for beginners! (8:54)

4. Cross Entropy Loss Error Function - ML for beginners! (11:14)

5. Gradient Descent - Simply Explained! ML for beginners with Code Example! (12:34)

Step-by-step classroom-style teaching of neural network from Math and Science:

1. Intro to Machine Learning & Neural Networks. How Do They Work? (1:42:18)

In depth introduction of neural network with rich animation from 3Blue1Brown:

1. But what is a neural network? | Chapter 1, Deep learning (19:13)

2. Gradient descent, how neural networks learn | Chapter 2, Deep learning (21:00)

If you are still looking for more in-depth introduction of neural network, you can watch a lecture series from First Principles of Computer Vision presented by Shree Nayar who is faculty in  the Computer Science Department, School of Engineering and Applied Sciences, Columbia University.

1. Neural Networks (playlist, 1:19:42)

As of 2023, Python is the most popular language for AI development. There are several advantages such as ease of use (read & write),  rich ecosystem (lots of libraries available), community support (large user base), flexible for integration (a general-purpose programming language), free (open-source), etc.

Python resources are abundant online. The following are a few examples for your reference. Each one may involve different level of effort. Feel free to try out any one of them to find one suitable for you. 

• Codecademy Learn Python 3: Easy to follow tutorials. You can just use the "free" version and don't need to use the "paid" plan.

• Kaggle Python: A free, concise course to acquire language skills for AI applications.

• Google Crash Course on Python: A formal online course which is part of the Google IT Automation with Python Professional Certificate.

Perceptron is the fundamental building block of all the complex neural networks (CNN, RNN, Transformer, etc.) used today. It worth the efforts to learn its biological source of inspiration, mathematical construct and computational implementation. 

We had introduced the Python implementation from Python Simplified in our previous post. After going through some Python tutorials, it's time to revisit these tutorials and focus on its Python implementation.

1. Perceptron Algorithm with Code Example - ML for beginners! (8:54)

2. Cross Entropy Loss Error Function - ML for beginners! (11:14)

3. Gradient Descent - Simply Explained! ML for beginners with Code Example! (12:34)

You can create and run your Python code using a platform called Replit. Go to the Replit website and create your account. The following is a tutorial to help you use the platform.  

1. Welcome to Replit!  (7:59)