If you are looking for an answer to the question What is Artificial Intelligence? and you only have a minute, then here's the definition the Association for the Advancement of Artificial Intelligence offers on its home page: "the scientific understanding of the mechanisms underlying thought and intelligent behavior and their embodiment in machines."
However, if you are fortunate enough to have more than a minute, then please get ready to embark upon an exciting journey exploring AI (but beware, it could last a lifetime) …
Link: Machine Learning A-Z: Hands-On Python & R In Data Science coupon code udemy Machine Learning A-Z: Hands-On Python & R In Data Science 4.5 (107,137 ratings) Course Ratings are calculated from individual students' ratings and a variety of other signals, like age of rating and reliability, to ensure that they reflect course quality fairly and accurately. Bestseller by Kirill Eremenko, Hadelin de Ponteves, SuperDataScience Team, SuperDataScience Support What you'll learn Master Machine Learning on Python & R Have a great intuition of many Machine Learning models Make accurate predictions Make powerful analysis Make robust Machine Learning models Create strong added value to your business Use Machine Learning for personal purpose Handle specific topics like Reinforcement Learning, NLP and Deep Learning Handle advanced techniques like Dimensionality Reduction Know which Machine Learning model to choose for each type of problem Build an army of powerful Machine Learning models and know how to combine them to solve any problem Description Interested in the field of Machine Learning? Then this course is for you! This course has been designed by two professional Data Scientists so that we can share our knowledge and help you learn complex theory, algorithms and coding libraries in a simple way. We will walk you step-by-step into the World of Machine Learning.
Link: Cutting-Edge AI: Deep Reinforcement Learning in Python udemy code coupon What you'll learn. Understand a cutting-edge implementation of the A2C algorithm (OpenAI Baselines) Understand and implement Evolution Strategies (ES) for AI. Understand and implement DDPG (Deep Deterministic Policy Gradient) Highest Rated by Lazy Programmer Inc. What you'll learn Understand a cutting-edge implementation of the A2C algorithm (OpenAI Baselines) Understand and implement Evolution Strategies (ES) for AI Understand and implement DDPG (Deep Deterministic Policy Gradient) Description Welcome to Cutting-Edge AI! This is technically Deep Learning in Python part 11 of my deep learning series, and my 3rd reinforcement learning course.
The past few years have witnessed breakthroughs in reinforcement learning (RL). From the first successful use of RL by a deep learning model for learning a policy from pixel input in 2013 to the OpenAI Dexterity program in 2019, we live in an exciting moment in RL research. Consequently, we need, as RL researchers, to create more and more complex environments and Unity helps us to do that. Unity ML-Agents toolkit is a new plugin based on the game engine Unity that allows us to use the Unity Game Engine as an environment builder to train agents. From playing football, learning to walk, to jump big walls, to train a cute doggy to catch sticks, Unity ML-Agents Toolkit provides a ton of amazing pre-made environment.
Facebook has recently created an algorithm that enhances an AI agent's ability to navigate an environment, letting the agent determine the shortest route through new environments without access to a map. While mobile robots typically have a map programmed into them, the new algorithm that Facebook designed could enable the creation of robots that can navigate environments without the need for maps. According to a post created by Facebook researchers, a major challenge for robot navigation is endowing AI systems with the ability to navigate through novel environments and reaching programmed destinations without a map. In order to tackle this challenge, Facebook created a reinforcement learning algorithm distributed across multiple learners. The algorithm was called decentralized distributed proximal policy optimization (DD-PPO).
Reinforcement learning, which spurs AI to complete goals using rewards or punishments, is a form of training that's led to gains in robotics, speech synthesis, and more. Unfortunately, it's data-intensive, which motivated research teams -- one from Google Brain (one of Google's AI research divisions) and the other from Alphabet's DeepMind -- to prototype more efficient means of executing it. In a pair of preprint papers, the researchers propose Adaptive Behavior Policy Sharing (ABPS), an algorithm that allows the sharing of experience adaptively selected from a pool of AI agents, and a framework -- Universal Value Function Approximators (UVFA) -- that simultaneously learns directed exploration policies with the same AI, with different trade-offs between exploration and exploitation. The teams claim ABPS achieves superior performance in several Atari games, reducing variance on top agents by 25%. As for UVFA, it doubles the performance of base agents in "hard exploration" in many of the same games while maintaining a high score across the remaining games; it's the first algorithm to achieve a high score in Pitfall without human demonstrations or hand-crafted features.
Over the past decade, we have witnessed notable breakthroughs in Artificial Intelligence (AI), thanks in large part to the development of deep learning approaches. Healthcare, finance, human resources, retail, there is no field in which AI has not proven to be a game-changer. Who would have said just a few years ago that there would be autonomous vehicles on public roads, that large-scale facial recognition would no longer be science fiction, or that fake news could have such an impact socially, economically, and politically? Some statistics related to AI are dizzying. According to Forbes, 75 countries are currently using AI technology for surveillance purposes via smart city platforms, facial recognition systems, and smart policing.
The first GANs paper had just come out two years before we started working on the second edition, but we weren't sure of its relevance. However, GANs have evolved into one of the hottest and most widely used deep learning techniques. People use them for creating artwork, colorizing and improving the quality of photos, and to recreate old video game textures in higher resolutions. It goes without saying that an introduction to GANs was long overdue. Another important machine learning topic not included in previous editions is reinforcement learning, which has received a massive boost in attention recently.
From microelectronics to mechanics and machine learning, the modern-day robots are a marvel of multiple engineering disciplines. They use sensors, image processing and reinforcement learning algorithms to move the objects around and move around the obstacles as well. However, this is not the case when it comes to handling objects such as glass. The surface properties of glass are transparent, and non-uniform light reflection makes it difficult for the sensors mounted on the robot to understand how to engage in a simple pick and place operation. To address this problem, researchers at Google AI along with Synthesis AI and Columbia University devised a novel machine-learning algorithm called ClearGrasp, that is capable of estimating accurate 3D data of transparent objects from RGB-D images.
"Generalization" is an AI buzzword these days for good reason: most scientists would love to see the models they're training in simulations and video game environments evolve and expand to take on meaningful real-world challenges -- for example in safety, conservation, medicine, etc. One concerned research area is deep reinforcement learning (DRL), which implements deep learning architectures with reinforcement learning algorithms to enable AI agents to learn the best actions possible to attain their goals in virtual environments. DRL has been widely applied in games and robotics. Such DRL agents have an impressive track record on Starcraft II and Dota-2. But because they were trained in fixed environments, studies suggest DRL agents can fail to generalize to even slight variations of their training environments.
Sometimes these notebooks find their way into production, but their code and structure are often far from ideal. In this session, we cover some best practices around creating and operationalising notebooks. We will talk about structure, code style, refactoring in notebooks, unit testing, reproducibility and more. Nikolay Manchev is a machine learning enthusiast and speaker. His area of expertise is Machine Learning and Data Science, and his research interests are in neural networks with emphasis on biological plausibility. Nikolay was a Senior Data Scientist and Developer Advocate at IBM [masked]) and currently acts as the Principal Data Scientist for EMEA at Domino Data Lab. Talk 3: Generative Deep Learning - The Key To Unlocking Artificial General Intelligence by David Foster Generative modelling is one of the hottest topics in AI. It's now possible to teach a machine to excel at human endeavours such as painting, writing, and composing music. In this talk, we will cover: - A general introduction to Generative Modelling - A walkthrough of one of the most utilised generative deep learning models - the Variational Autoencoder (VAE) - Examples of state-of-the-art output from Generative Adversarial Networks (GANs) and Transformer based architectures.