MP4 | Video: h264, 1280×720 | Audio: AAC, 44.1 KHz
Language: English | Size: 12.6 GB | Duration: 34h 23m
Learn to implement basic to advanced deep learning, artificial intelligence algorithms for real world games!
Learn to implement basic to advanced deep learning, artificial intelligence algorithms for real world games!
What you’ll learn
Learn to implement MinMax algorithm
Learn about Q-Learning by implementing games
Learn about Artificial Intelligence in games
Learn about gym module
Implement Deep Q-Learning
Implement Deep convolution Q-Learning
Learn about Tensorflow and Keras
Learn to build complex AI player player
Learn about Bellman equation and Dynamic Programming
Learn about Monte-Carlo simulation
Learn to implement Neural Network from Scratch
Requirements
High school Mathematics: Basic Probability and Statistics
No programming experience required.
Description
If you’re interested in learning how to make your own Artificially Intelligent games using Python, then this is the course for you!
This course is full of tutorial videos along with materials which one can run to get familiar with this discipline. You no longer need to read complex research papers and have a solid foundation in mathematics to get going. Just follow this course and materials and you’re on your way.
Let’s take a look at the structure of this course
We are going to start with a simple game that implements popular board game algorithm: MinMax. In this game we are going to create TicTacToe and write an algorithm that plays against human player and tries to beat human player.
Next we are going to learn about gym module: a popular library which can be used to write and test our AI algorithms.
After that, we are going to learn about Bellman Equation and Dynamic Programming. We are going to learn how to find the optimal value of the states using Bellman equations through model dynamics. We are going to implement maze game to implement Q-learning algorithm.
Then, we are going to learn about Monte-Carlo Simulation. We are going to check how value function can be predicted using Monte Carlo simulation when model dynamics is unknown.
Similarly, we are going to implement following games throughout this course
1. BlackJack game using Monte-Carlo and Q-Learning
2. Pacman using Deep Convolution Neural Network
3. Make unbeatable AI TicTacToe player using Tensorflow and Keras (Human Vs AI)
4. MinMax algorithm for Board game
General Q/A’s
When most people hear the term artificial intelligence, the first thing they usually think of is robots. That’s because big-budget films and novels weave stories about human-like machines that wreak havoc on Earth. But nothing could be further from the truth.
Artificial intelligence is based on the principle that human intelligence can be defined in a way that a machine can easily mimic it and execute tasks, from the most simple to those that are even more complex. The goals of artificial intelligence include mimicking human cognitive activity. Researchers and developers in the field are making surprisingly rapid strides in mimicking activities such as learning, reasoning, and perception, to the extent that these can be concretely defined. Some believe that innovators may soon be able to develop systems that exceed the capacity of humans to learn or reason out any subject. But others remain skeptical because all cognitive activity is laced with value judgments that are subject to human experience.
Artificial intelligence (AI) refers to the simulation of human intelligence in machines that are programmed to think like humans and mimic their actions. The term may also be applied to any machine that exhibits traits associated with a human mind such as learning and problem-solving.
Who this course is for
Beginners who want to learn to create Artificially intelligent games
Programmers who want to implement AI algorithms
Beginners who want to learn complex algorithms in fun way by creating games
Anyone who want to learn python, pygame (game development tool) and Artificial Intelligence in general.
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