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10 Free Programming Courses by MIT, IBM, Google, Microsoft, and Apple

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You will learn about variables, conditional execution, repeated execution and how we use functions. Once a student completes this course, they will be ready to take more advanced programming courses. This course covers Python 3. 4. Programming for the Web with JavaScript Course by University of Pennsylvania The basics of how the World Wide Web allows browsers to send and retrieve web content; Web browser internals, the Document Object Model (DOM), and jQuery; How to create dynamic, interactive web pages using JavaScript; Techniques for creating data-driven websites using modern web technologies; Client-side JavaScript libraries and frameworks; Server-side JavaScript application architecture, middleware, HTTP, and RESTful API design 5. Python Basics for Data Science This Python course provides a beginner-friendly introduction to Python for Data Science. Practice through lab exercises, and you'll be ready to create your first Python scripts on your own! 6. Introduction to Computer Science and Programming Using Python An introduction to computer science as a tool to solve real-world analytical problems using Python 3.5.


Everything So Far In CVPR 2020 Conference

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Computer Vision and Pattern Recognition (CVPR) conference is one of the most popular events around the globe where computer vision experts and researchers gather to share their work and views on the trending techniques on various computer vision topics, including object detection, video understanding, visual recognition, among others. This year, the Computer Vision (CV) researchers and engineers have gathered virtually for the conference from 14 June, which will last till 19 June. In this article, we have listed down all the important topics and tutorials that have been discussed on the 1st and 2nd day of the conference. In this tutorial, the researchers presented the latest developments in robust model fitting, recent advancements in new sampling and local optimisation methods, novel branch-and-bound and mathematical programming algorithms in the global methods as well as the latest developments in differentiable alternative to Random Sample Consensus Algorithm or RANSAC. To know what a RANSAC is and how it works, click here.


How to Implement Bayesian Optimization from Scratch in Python

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Many methods exist for function optimization, such as randomly sampling the variable search space, called random search, or systematically evaluating samples in a grid across the search space, called grid search. More principled methods are able to learn from sampling the space so that future samples are directed toward the parts of the search space that are most likely to contain the extrema. A directed approach to global optimization that uses probability is called Bayesian Optimization. Take my free 7-day email crash course now (with sample code). Click to sign-up and also get a free PDF Ebook version of the course.


Lecture Notes: Optimization for Machine Learning

arXiv.org Machine Learning

Lecture notes on optimization for machine learning, derived from a course at Princeton University and tutorials given in MLSS, Buenos Aires, as well as Simons Foundation, Berkeley.


Introduction to Online Convex Optimization

arXiv.org Machine Learning

It was written as an advanced text to serve as a basis for a graduate course, and/or as a reference to the researcher diving into this fascinating world at the intersection of optimization and machine learning. Such a course was given at the Technion in the years 2010-2014 with slight variations from year to year, and later at Princeton University in the years 2015-2016. The core material in these courses is fully covered in this book, along with exercises that allow the students to complete parts of proofs, or that were found illuminating and thought-provoking. Most of the material is given with examples of applications, which are interlaced throughout different topics. These include prediction from expert advice, portfolio selection, matrix completion and recommendation systems, SVM training and more.


Game-Related Examples of Artificial Intelligence

AAAI Conferences

The field of artificial intelligence needs to attract new researchers to the field to continue current explorations and look for novel approaches to tomorrow's problems. One approach involves providing students with learning tools that excite their imagination and help them obtain an appreciation for what artificial intelligence can do. The tools described here are used in an undergraduate course at Sam Houston State University. They include heuristic-driven search in a potential game's terrain map, reinforcement learning in a tank battle game, and game tree search techniques in tictac-toe.


How to Implement a Beam Search Decoder for Natural Language Processing - Machine Learning Mastery

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Natural language processing tasks, such as caption generation and machine translation, involve generating sequences of words. Models developed for these problems often operate by generating probability distributions across the vocabulary of output words and it is up to decoding algorithms to sample the probability distributions to generate the most likely sequences of words. In this tutorial, you will discover the greedy search and beam search decoding algorithms that can be used on text generation problems. How to Implement Beam Search Decoder for Natural Language Processing Photo by See1,Do1,Teach1, some rights reserved. In natural language processing tasks such as caption generation, text summarization, and machine translation, the prediction required is a sequence of words.


Overview of Udacity Artificial Intelligence Engineer Nanodegree, Term 1

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After finishing Udacity Deep Learning Foundation I felt that I got a good introduction to Deep Learning, but to understand things, I must dig deeper. Besides I had a guaranteed admission to Self-Driving Car Engineer, Artificial Intelligence, or Robotics Nanodegree programs.



A step-by-step guide to building a simple chess AI – freeCodeCamp

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Using these libraries will help us focus only on the most interesting task: creating the algorithm that finds the best move. We'll start by creating a function that just returns a random move from all of the possible moves: Although this algorithm isn't a very solid chess player, it's a good starting point, as we can actually play against it: Now let's try to understand which side is stronger in a certain position. With the evaluation function, we're able to create an algorithm that chooses the move that gives the highest evaluation: The only tangible improvement is that our algorithm will now capture a piece if it can. Next we're going to create a search tree from which the algorithm can chose the best move. This is done by using the Minimax algorithm.