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Reinforcement Learning

#artificialintelligence

Reinforcement Learning is a subfield of Machine Learning, but is also a general purpose formalism for automated decision-making and AI. This course introduces you to statistical learning techniques where an agent explicitly takes actions and interacts with the world. Understanding the importance and challenges of learning agents that make decisions is of vital importance today, with more and more companies interested in interactive agents and intelligent decision-making. This course introduces you to the fundamentals of Reinforcement Learning. When you finish this course, you will: - Formalize problems as Markov Decision Processes - Understand basic exploration methods and the exploration/exploitation tradeoff - Understand value functions, as a general-purpose tool for optimal decision-making - Know how to implement dynamic programming as an efficient solution approach to an industrial control problem This course teaches you the key concepts of Reinforcement Learning, underlying classic and modern algorithms in RL.


A Complete Reinforcement Learning System (Capstone)

#artificialintelligence

In this final course, you will put together your knowledge from Courses 1, 2 and 3 to implement a complete RL solution to a problem. This capstone will let you see how each component---problem formulation, algorithm selection, parameter selection and representation design---fits together into a complete solution, and how to make appropriate choices when deploying RL in the real world. This project will require you to implement both the environment to stimulate your problem, and a control agent with Neural Network function approximation. In addition, you will conduct a scientific study of your learning system to develop your ability to assess the robustness of RL agents. To use RL in the real world, it is critical to (a) appropriately formalize the problem as an MDP, (b) select appropriate algorithms, (c) identify what choices in your implementation will have large impacts on performance and (d) validate the expected behaviour of your algorithms.


Machine Learning and Reinforcement Learning in Finance

#artificialintelligence

This course aims at providing an introductory and broad overview of the field of ML with the focus on applications on Finance. Supervised Machine Learning methods are used in the capstone project to predict bank closures. Simultaneously, while this course can be taken as a separate course, it serves as a preview of topics that are covered in more details in subsequent modules of the specialization Machine Learning and Reinforcement Learning in Finance. The goal of Guided Tour of Machine Learning in Finance is to get a sense of what Machine Learning is, what it is for and in how many different financial problems it can be applied to. The course is designed for three categories of students: Practitioners working at financial institutions such as banks, asset management firms or hedge funds Individuals interested in applications of ML for personal day trading Current full-time students pursuing a degree in Finance, Statistics, Computer Science, Mathematics, Physics, Engineering or other related disciplines who want to learn about practical applications of ML in Finance Experience with Python (including numpy, pandas, and IPython/Jupyter notebooks), linear algebra, basic probability theory and basic calculus is necessary to complete assignments in this course.


Cutting-Edge AI: Deep Reinforcement Learning in Python

#artificialintelligence

Created by Lazy Programmer Inc. English [Auto-generated] Created by Lazy Programmer Inc. This is technically Deep Learning in Python part 11 of my deep learning series, and my 3rd reinforcement learning course. Deep Reinforcement Learning is actually the combination of 2 topics: Reinforcement Learning and Deep Learning (Neural Networks). While both of these have been around for quite some time, it's only been recently that Deep Learning has really taken off, and along with it, Reinforcement Learning. The maturation of deep learning has propelled advances in reinforcement learning, which has been around since the 1980s, although some aspects of it, such as the Bellman equation, have been for much longer.


Artificial Intelligence: Reinforcement Learning in Python

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Udemy Coupon - Artificial Intelligence: Reinforcement Learning in Python Complete guide to Artificial Intelligence, prep for Deep Reinforcement Learning with Stock Trading Applications BESTSELLER 4.5 (5,676 ratings) Created by Lazy Programmer Inc.  English [Auto-generated], Portuguese [Auto-generated], 1 more Preview this Course - GET COUPON CODE 100% Off Udemy Coupon . Free Udemy Courses . Online Classes


Artificial Intelligence for Business

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Udemy Coupon - Solve Real World Business Problems with AI Solutions Created by Hadelin de Ponteves, Kirill Eremenko, SuperDataScience Team English [Auto-generated], French [Auto-generated], 5 more Students also bought Artificial Intelligence: Reinforcement Learning in Python Data Science: Natural Language Processing (NLP) in Python Recommender Systems and Deep Learning in Python Cluster Analysis and Unsupervised Machine Learning in Python Natural Language Processing with Deep Learning in Python Preview this Course GET COUPON CODE Description Structure of the course: Part 1 - Optimizing Business Processes Case Study: Optimizing the Flows in an E-Commerce Warehouse AI Solution: Q-Learning Part 2 - Minimizing Costs Case Study: Minimizing the Costs in Energy Consumption of a Data Center AI Solution: Deep Q-Learning Part 3 - Maximizing Revenues Case Study: Maximizing Revenue of an Online Retail Business AI Solution: Thompson Sampling Real World Business Applications: With Artificial Intelligence, you can do three main things for any business: Optimize Business Processes Minimize Costs Maximize Revenues We will show you exactly how to succeed these applications, through Real World Business case studies. And for each of these applications we will build a separate AI to solve the challenge. In Part 1 - Optimizing Processes, we will build an AI that will optimize the flows in an E-Commerce warehouse. In Part 2 - Minimizing Costs, we will build a more advanced AI that will minimize the costs in energy consumption of a data center by more than 50%! Just as Google did last year thanks to DeepMind.


Artificial Intelligence 2018: Build the Most Powerful AI

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Free Coupon Discount - Artificial Intelligence 2018: Build the Most Powerful AI, Learn, build and implement the most powerful AI model at home. Created by Hadelin de Ponteves, Kirill Eremenko, SuperDataScience Team Students also bought Artificial Intelligence Masterclass The Complete Neural Networks Bootcamp: Theory, Applications TensorFlow 2.0 Practical Modern Reinforcement Learning: Deep Q Learning in PyTorch Deep Reinforcement Learning 2.0 TensorFlow 2.0 Practical Advanced Preview this Udemy Course GET COUPON CODE Description Two months ago we discovered that a very new kind of AI was invented. The kind of AI which is based on a genius idea and that you can build from scratch and without the need for any framework. We checked that out, we built it, and... the results are absolutely insane! This game-changing AI called Augmented Random Search, ARS for short.


3 ways to get into reinforcement learning

#artificialintelligence

When I was in graduate school in the 1990s, one of my favorite classes was neural networks. Back then, we didn't have access to TensorFlow, PyTorch, or Keras; we programmed neurons, neural networks, and learning algorithms by hand with the formulas from textbooks. We didn't have access to cloud computing, and we coded sequential experiments that often ran overnight. There weren't platforms like Alteryx, Dataiku, SageMaker, or SAS to enable a machine learning proof of concept or manage the end-to-end MLops lifecycles. I was most interested in reinforcement learning algorithms, and I recall writing hundreds of reward functions to stabilise an inverted pendulum.


Artificial Intelligence Masterclass

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Free Coupon Discount - Artificial Intelligence Masterclass, Enter the new era of Hybrid AI Models optimized by Deep NeuroEvolution, with a complete toolkit of ML, DL & AI models Created by Hadelin de Ponteves, Kirill Eremenko, SuperDataScience Team Students also bought Artificial Intelligence 2018: Build the Most Powerful AI Introduction to Artificial Intelligence ( AI) for Managers Artificial Intelligence for Business Artificial Intelligence Introduction Deep Reinforcement Learning 2.0 A Beginner's Guide to Artificial Intelligence Preview this Udemy Course GET COUPON CODE Description Today, we are bringing you the king of our AI courses...: The Artificial Intelligence MASTERCLASS Are you keen on Artificial Intelligence? Do want to learn to build the most powerful AI model developed so far and even play against it? Sounds tempting right... Then Artificial Intelligence Masterclass course is the right choice for you. This ultimate AI toolbox is all you need to nail it down with ease. You will get 10 hours step by step guide and the full roadmap which will help you build your own Hybrid AI Model from scratch.


Building Intelligent Autonomous Navigation Agents

arXiv.org Artificial Intelligence

Breakthroughs in machine learning in the last decade have led to `digital intelligence', i.e. machine learning models capable of learning from vast amounts of labeled data to perform several digital tasks such as speech recognition, face recognition, machine translation and so on. The goal of this thesis is to make progress towards designing algorithms capable of `physical intelligence', i.e. building intelligent autonomous navigation agents capable of learning to perform complex navigation tasks in the physical world involving visual perception, natural language understanding, reasoning, planning, and sequential decision making. Despite several advances in classical navigation methods in the last few decades, current navigation agents struggle at long-term semantic navigation tasks. In the first part of the thesis, we discuss our work on short-term navigation using end-to-end reinforcement learning to tackle challenges such as obstacle avoidance, semantic perception, language grounding, and reasoning. In the second part, we present a new class of navigation methods based on modular learning and structured explicit map representations, which leverage the strengths of both classical and end-to-end learning methods, to tackle long-term navigation tasks. We show that these methods are able to effectively tackle challenges such as localization, mapping, long-term planning, exploration and learning semantic priors. These modular learning methods are capable of long-term spatial and semantic understanding and achieve state-of-the-art results on various navigation tasks.