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Implementing Research Papers is the Most Effective Way to Learn Artificial Intelligence
The Neuralnet Academy will save you dozens of hours of frustration in your AI education.
What is the Neuralnet Academy?
Streamlined Learning
Artificial intelligence is difficult, but it doesn't have to be confusing. We break down complicated topics into their core ideas to get you coding fast.
Complete Curriculum
Many other courses leave out key topics; not the Academy. Everything you need to go from beginner to expert is inside.
Learn Like the Experts
Top tier researchers aren't reading Medium articles for insights, and neither should you. In the Academy you will learn how to implement cutting edge research articles from premier research institutions like Deep Mind and OpenAI.
Interactive Lessons
Try your hand at the solution first, then see an expert code the solution. Complete with line by line explanations.
Framework Agnostic
We will show you how to code deep reinforcement learning algorithms in both PyTorch and Tensorflow 2. Both have a place in industry, and should have a place in your toolkit.
Slack Community
Build connections with other Academy students in the Slack channel. We're a group of researchers, post docs, industry experts and hobbyists helping each other learn.
Courses Included In Your Purchase
Get instant access to over 240 lessons & 40 hours of video.
Introduction to Implementing Papers
52 Minutes
7 Lessons
This course is a beginner friendly introduction to our framework for implementing deep reinforcement learning papers. This course is FREE for website subscribers - no purchase necessary.
Introduction to Reinforcement Learning
Under Construction!
1 - 2 hours | About 10 - 20 lessons
This course will cover a conceptual introduction to the core concepts of reinforcement learning. Students will learn enough to dive right into the more advanced courses. This will be a FREE course for website subscribers.
Actor Critic Methods
9 Hours 52 Minutes
76 Lessons
Topics include a brief introduction to reinforcement learning, policy gradient and actor critic methods, deep deterministic policy gradients (DDPG), twin delayed deep deterministic policy gradients (TD3), and soft actor critic (SAC). Algorithms are implemented using both the PyTorch and Tensorflow 2 frameworks.
Deep Q Learning
6 Hours 23 Minutes
47 Lessons
Topics include a comprehensive introduction to reinforcement learning and neural networks, deep Q learning (DQN), double deep Q learning (DQN), and dueling deep Q learning (D3QN). Algorithms are implemented using both the PyTorch and Tensorflow 2 frameworks.
Curiosity Based Learning
3 Hours 46 Minutes
25 Lessons
This is an intermediate level course that jumps straight into the heart of the topic. We cover asynchronous advantage actor critic methods (A3C) and the intrinsic curiosity module (ICM). These algorithms are implemented using the PyTorch framework
Advanced Replay Memory Strategies
4 Hours 58 Minutes
24 Lessons
This is an intermediate level course that covers hindsight experience replay memory, and prioritized experience replay. Students also learn to code their own custom environments.
Advanced Actor Critic Methods
2 Hours 40 Minutes
10 Lessons
This is an expert level course that begins with proximal policy optimization (PPO) in both continuous and discrete action spaces. Students also learn a multithreaded implementation in the Atari library.
Introduction to Natural Language Processing
3 Hours 13 Minutes
25 Lessons
This is a beginner level course that shows students how to implement the word2vec algorithm starting from first principles.
Writing Our Own Framework
6 Hours 46 Minutes
19 Lessons
In this course students will unify all the code from the preceding courses into a single framework, called ProtoRL. This framework is designed with rapid prototyping of research papers in mind, and will serve as the basis for future courses.
Multi Agent Reinforcement Learning
1 Hours 49 Minutes
11 Lessons
In the multi agent reinforcement learning course, we tackle co-operative and competitive behaviors in multi agent environments. This course utilizes PettingZoo and PyTorch for the MADDPG algorithm.
MAPPO coming soon!
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About Phil.
In 2012 I received my PhD in experimental condensed matter physics from West Virginia University. Following that I was a dry etch process engineer for Intel Corporation, where I leveraged big data to make essential process improvements in a billion dollar state of the art facility After leaving Intel in 2015, I have been on a mission to educate the next generation of artificial intelligence engineers.
Frequently Asked Questions
No matter where you are in your journey, you will benefit from the Academy. The Deep Q Learning course assumes no knowledge of reinforcement learning and starts students out with the very basics. Advanced courses such as the course on Intrinsic Curiosity and Advanced Actor Critic Methods cover challenging topics for students with a strong foundation. Regardless of your level, the only way to master programming is by programming, so start putting code to editor.
New content is being added monthly, if not more often. Updates were made on the following dates:
February 2022: Academy Launched. Included all Udemy course material plus tensorflow 2 implementations and hindsight experience replay
March 2022: PPO Course added with continuous action spaces, single threaded solution
April 2022: PPO course updated with discrete action spaces and multithreading
May 2022: First modules of prioritized experience replay module added
June 2022: Prioritized Experience Replay course updated
July 2022: Prioritized Experience Replay course updated
September 2022: Prioritized Experience Replay course updated
October 2022: Prioritized Experience Replay Course finished
November 2022: Writing an RL Framework course first update
December 2022: Writing an RL Framework course updated
January 2023: Writing an RL Framework course updated
February 2023: Writing an RL Framework course updated
April 2023: Writing an RL Framework course updated
May 2023: Intro To Implementing Papers Course Added
June 2023: MADDPG Course added
Absolutely. Send an email to sales@neuralnet.ai and we'll hook you up with a discount code.
You can cancel any time. When you do, you'll finish out your current billing period and won't be charged the following cycle.
Absolutely. The Udemy courses cover some great foundational content, but the Academy expands on the "learn by implementing papers" philosophy. If you want to reach the next level in your AI engineer journey, this is the product for you.
