Having learnt of the remarkable theorem of Bely ˇ ı [1] which relates the existence of algebraic models of Riemann surfaces to that of analytic properties of rational functions thereon, Grothendieck launched an entire programme [2] by pictorially representing 1 this structure as bipartite graphs (the dessin) drawn on the Riemann surface. He hypothesised dessins d'enfants in their current form as a conceptual representation of the absolute Galois group over the rationals, one the most mysterious and least understood objects in number theory. Subsequently, he developed a generalisation of Bely ˇ ı's theorem which extends the surfaces considered in the mapping to more general Riemann surfaces. Properties of the mapping are identified with combinatorial invariants of the dessin d'enfant graphs [2] (q.v.

Techniques based on Reinforcement Learning (RL) are increasingly being used to design control policies for robotic systems. RL fundamentally relies on state-based reward functions to encode desired behavior of the robot and bad reward functions are prone to exploitation by the learning agent, leading to behavior that is undesirable in the best case and critically dangerous in the worst. On the other hand, designing good reward functions for complex tasks is a challenging problem. In this paper, we propose expressing desired high-level robot behavior using a formal specification language known as Signal Temporal Logic (STL) as an alternative to reward/cost functions. We use STL specifications in conjunction with model-based learning to design model predictive controllers that try to optimize the satisfaction of the STL specification over a finite time horizon. The proposed algorithm is empirically evaluated on simulations of robotic system such as a pick-and-place robotic arm, and adaptive cruise control for autonomous vehicles.

Yann LeCun's deep learning course -- Deep Learning DS-GA 1008 -- at NYU Centre for Data Science has been made free and accessible online for all. The course will be led by Yann LeCun himself, along with Alfredo Canziani, an assistant professor of computer science at NYU, in Spring 2020. This deep learning course will focus on the latest techniques in deep learning and representation learning. It will also focus on an in-depth understanding of supervised and unsupervised deep learning, embedding methods, metric learning, and convolutional and recurrent nets. The course will further talk about the applications to computer vision, natural language understanding, and speech recognition.

A large portion of the field of statistics and statistical methods is dedicated to data where the distribution is known. Samples of data where we already know or can easily identify the distribution of are called parametric data. Often, parametric is used to refer to data that was drawn from a Gaussian distribution in common usage. Data in which the distribution is unknown or cannot be easily identified is called nonparametric. In the case where you are working with nonparametric data, specialized nonparametric statistical methods can be used that discard all information about the distribution.

This Friday the 13th of November at 8pm UTC (3pm EDT/12pm PDT), Robotics Today are hosting an online seminar with Professor Davide Scaramuzza from the University of Zurich. "Robotics Today – A series of technical talks" is a virtual robotics seminar series. The goal of the series is to bring the robotics community together during these challenging times. The seminars are open to the public. The format of the seminar consists of a technical talk live captioned and streamed via Web and Twitter, followed by an interactive discussion between the speaker and a panel of faculty, postdocs, and students that will moderate audience questions.

The AI area of Natural Language Processing, or NLP, throughout its gigantic language models -- yes, GPT-3, I'm watching you -- presents what it's perceived as a revolution in machines' capabilities to perform the most distinct language tasks. Due to that, the perception of the public as a whole is split: some perceive that these new language models are going to pave the way to a Skynet type of technology, while others dismiss them as hype-fueled technologies that will live in dusty shelves, or HDD drives, in little to no time. Motivated by this, I'm creating this series of stories that will approach NLP from scratch in a friendly way. To join me, you'll need to have little experience with Python and Jupyter Notebooks, and for the most part, I won't even ask you to have anything installed on your machine. This series will differ dramatically from the Stanford course in terms of the depth that we'll approach statistics and calculus.

The motive behind Creating this repo is to feel the fear of mathematics and do what ever you want to do in Machine Learning, Deep Learning and other fields of AI . So, try this Code in your python notebook which is provided in edx Course. In this Repo you will also learn the Libraries which are essential like numpy, pandas, matplotlib... I am going to upload new material when i find those material useful, you can also help me in keeping this repo fresh. Selecting the right algorithm which includes giving considerations to accuracy, training time, model complexity, number of parameters and number of features.

Machine Learning foners Second Edition has been written and designed for absolute beginners. This means plain-English explanations and no coding experience required. Where core algorithms are introduced, clear explanations and visual examples are added to make it easy and engaging to follow along at home. This major new edition features many topics not covered in the First Edition, including Cross Validation, Data Scrubbing and Ensemble Modeling.

Best Data science andArtificial intelligenceTraining Hub Data science with Ai Data science with AI Master’s program Welcome to hai hub Data science with Artificial Intelligence Join our Data science course certification course which includes statistics, R, Python, Machine Learning, Deep Learning and Artificial Intelligence. Get a chance to join the internship program right after the … Home Read More »

In this blog post, you will learn about and implement AlphaZero, an exciting and novel Reinforcement Learning Algorithm, used to beat world-champions in games like Go and Chess. You will use it to master a pen-and-pencil game (Dots and Boxes) and deploy it into a web app, entirely in JavaScript. AlphaZero's key and most exciting aspect is its ability to gain superhuman behavior in board games without relying on external knowledge. AlphaZero learns to master the game by playing against itself (self-play) and learning from those experiences. We will leverage a "simplified, highly flexible, commented, and easy to understand implementation" Python version of AlphaZero from Surag Nair available in Github. You can go ahead and play the game here. The WebApp and JavaScript implementation are available here. This code was ported from this Python implementation.