Reinforcement Learning with Python, Tensorflow and OpenAI 2-days workshop
Train neural networks to play video games, control AI agents and approach unsupervised learning problems using Python, Tensorflow and OpenAI Gym.
The workshop is meant to introduce you to unsupervised deep learning and reinforcement learning. You will learn:
Train neural networks to play video games using Deep Q-Learning
Reduce the dimensionality of your data using autoencoders
Improve the efficiency of your algorithms with generative adversarial networks
Train AI agents to interact in an environment using OpenAI Gym and Universe
Train a Word2Vec model to encode natural language
The workshop is conceived to maximize the learning experience for everyone and includes 50% theory and 50% hands-on practice.
Is lunch provided
Yes! Lunch is included.
Are there any prerequisites?
Previous experience programming in Python or in other languages is advised to make best use of the workshop. Additionally, familiarity with machine learning and deep learning is necessary.
In the last 2 years Python has become a de-facto standard in data science and is widely adopted by most major companies. Reasons for this success include:
large set of mature data science libraries => most needs covered
worldwide community of enthusiasts => get help when you need it
easy to learn, read and write => start contributing immediately
supports both functional and object oriented coding => versatile and powerful
full stack programming language => easier interaction between data scientists and software engineers
There are many open source Deep Learning libraries. Tensorflow is backed by Google and is quickly becoming one of the most used libraries in the fields. It has a large and growing community of users and it is versatile and easy to learn. Highlights include
largest community of developers
state of the art models and nodes
high scalability, can be distributed on many GPUs
production performance and deployment tools
very versatile and powerful for distributed high performance computing beyond neural networks
The course is lead by Francesco Mosconi. Ph.D. in Physics and Data Scientist at Catalit LLC, he was formerly co-founder and Chief Data Officer at Spire, a YC-backed company that invented the first consumer wearable device capable of continuously tracking respiration and physical activity. Machine Learning and python expert he also served as Data Science lead instructor at General Assembly and The Data incubator.
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Terms & Conditions
In certain cases, we may need to cancel this workshop due to circumstances beyond our control or otherwise. If this happens, we will refund all registration fees for those who signed up. We are not responsible for any related expenses incurred by registered attendees (including but not limited to travel and hotel expenses).
More than 1 week before course: full refund
Less than 1 week before course: no refund available.
All public workshops come with a no-questions-asked money-back guarantee. If you are unhappy for any reason after attending the class, you can ask for a full refund.