What you would learn in Advanced AI: Deep Reinforcement Learning in Python course?
This course focuses on applying deep learning and neural networks to reinforcement learning..
If you've attended my first reinforcement learning class, you're aware of the importance of reinforcement learning. It's the cutting edge of what we can accomplish using AI.
Notably, the combination of deep learning and reinforcement learning has resulted in AlphaGo beating the world champion in the game of strategy Go and has led to autonomous cars and computers that can play video games on the level of a human.
The concept of reinforcement learning is in use since the 70s, but it has never been attainable until now.
The world is changing at the speed of light. California is one of the states where California has changed its laws to allow self-driving car manufacturers can test their vehicles without the need for a human inside the vehicle to monitor them.
We've discovered how reinforcement learning works as a completely different type of machine learning that is different from unsupervised and supervised learning.
Unsupervised and supervised machine-learning algorithms are designed to analyze and make data predictions. At the same time, reinforcement learning is the process of training agents to communicate with their environment to maximize their rewards.
Contrary to unsupervised and supervised learning algorithms, reinforcement learning algorithms are motivated by a desire to succeed and achieve an objective.
It's such an interesting viewpoint, and it could create a situation where supervised or unsupervised machine learning and " data science" appear dull when you look back. Why would you train a neural network to discover the information in a database when you could train a neural network to communicate with the natural world?
Deep reinforcement learning, as well as AI, is full of promise; the technology also comes with a risk of huge one.
Bill Gates and Elon Musk have publicly issued statements regarding some of the threats that AI could pose to our economic stability and even our existence.
We learned this in my very first reinforcement learning course. One of the primary fundamentals of training reinforcement agents is that they can be unintended outcomes when learning an AI.
AIs don't have the same thinking abilities as humans, so they develop innovative and genuine solutions to meet their goals, usually with methods that astonish experts in their field. These human beings are the most adept at what they do.
OpenAI is a not-for-profit organization founded by Elon Musk, Sam Altman (Y Combinator), and others to ensure that AI develops in a way that is beneficial instead of damaging.
The primary motivation for OpenAI is the danger that AI can pose to human beings. They believe that collaboration open is one of the key elements to reducing the risk.
One of the most appealing aspects of OpenAI is that they offer an application known as"the OpenAI Gym that we'll use extensively throughout this class.
It allows anyone, anyplace in the world, to train reinforcement agents anywhere in everyday settings.
The course builds on the lessons learned from the previous course, exploring more complex environments, particularly those offered by the OpenAI Gym:
To develop effective agents for learning, we'll require innovative methods.
We'll expand our understanding of temporal differences in learning by examining an algorithm called the TD Lambda algorithm. We'll explore a specific kind of neural network called the RBF network. We'll also explore the policy gradient technique and conclude the course by examining Deep Q-Learning (DQN) and A3C (Asynchronous Advantage Actor-Critic).
Thank you for reading, And I'll be seeing you at class!
"If you can't implement it, you don't understand it."
Or, as the famous scientist Richard Feynman said: "What I cannot create, I do not understand".
These are my courses. They are the only courses in which you can learn how to create machine learning algorithms from beginning to finish.
Other courses will show you how to connect your data to libraries; however, do you need assistance by writing three codes?
When you do the same thing using ten datasets, you realize that you haven't learned ten things. You learned one thing and then repeated the exact three pages of code repeatedly...
The suggested prerequisites are:
Math at the college level is practical (calculus and probability)
Python coding: if/else, loops, lists, dicts, sets
Numpy Coding: Matrix and vector operations
Learn to build ANNs and CNNs using Theano or TensorFlow
Markov Decision Processes (MDPs)
Learn the best ways to apply Dynamic Programming, Monte Carlo, and Temporal Difference Learning to solve MDPs.
- Create various deep learning agents (including DQN and A3C)
- Use a variety of the most advanced reinforcement learning algorithms to solve any issue
- Q-Learning using Deep Neural Networks
- Policy Gradient Methods Using Neural Networks
- Reinforcement Learning using RBF Networks
- Make use of Convolutional Neural Networks to learn Deep Q-Learning
Download Advanced AI: Deep Reinforcement Learning in Python from below links NOW!