Fabio Mardero is a data scientist from Italy. He graduated in physics and statistical and actuarial sciences. He is currently working at a well-known Italian insurance company as a data scientist and Non-Life technical provisions evaluator. Linear Algebra and Mathematical Foundation: This course covers machine learning key elements, vector space, matrices, linear independence and basis and linear maps. Analytic Geometry: This course covers Lengths and Distances, Angles and Orthogonality, Orthogonal Projections and Rotations.

Take an adapted version of this course as part of the Stanford Artificial Intelligence Professional Program. Professor Christopher Manning Thomas M. Siebel Professor in Machine Learning, Professor of Linguistics and of Computer Science Director, Stanford Artificial Intelligence Laboratory (SAIL) To follow along with the course schedule and syllabus, visit: http://web.stanford.edu/class/cs224n/... To get the latest news on Stanford's upcoming professional programs in Artificial Intelligence, visit: http://learn.stanford.edu/AI.html To view all online courses and programs offered by Stanford, visit: http://online.stanford.edu

This is lecture 1 of course 6.S094: Deep Learning for Self-Driving Cars taught in Winter 2017. Links to individual lecture videos for the course: Lecture 1: Introduction to Deep Learning and Self-Driving Cars https://youtu.be/1L0TKZQcUtA Lecture 2: Deep Reinforcement Learning for Motion Planning https://youtu.be/QDzM8r3WgBw Lecture 3: Convolutional Neural Networks for End-to-End Learning of the Driving Task https://youtu.be/U1toUkZw6VI Lecture 4: Recurrent Neural Networks for Steering through Time https://youtu.be/nFTQ7kHQWtc Lecture 5: Deep Learning for Human-Centered Semi-Autonomous Vehicles https://youtu.be/ByZF8_-OJNI

Lecture 1 of Tim Roughgarden's Algorithmic Game Theory class at Stanford (Autumn 2013) Class description: Topics at the interface of computer science and game theory such as: algorithmic mechanism design; combinatorial auctions; computation of Nash equilibria and relevant complexity theory; congestion and potential games; cost sharing; game theory and the Internet; matching markets; network formation; online learning algorithms; price of anarchy; prior-free auctions; selfish routing; sponsored search.

Lecture 1 introduces the concept of Natural Language Processing (NLP) and the problems NLP faces today. The concept of representing words as numeric vectors is then introduced, and popular approaches to designing word vectors are discussed. This lecture series provides a thorough introduction to the cutting-edge research in deep learning applied to NLP, an approach that has recently obtained very high performance across many different NLP tasks including question answering and machine translation. It emphasizes how to implement, train, debug, visualize, and design neural network models, covering the main technologies of word vectors, feed-forward models, recurrent neural networks, recursive neural networks, convolutional neural networks, and recent models involving a memory component. For additional learning opportunities please visit: http://stanfordonline.stanford.edu/

Tomas G 10,107 views Getting Started with Spring Boot - Duration: 1:29:16.

Tom provides an overview of how to build a simple neural net in this introductory tutorial. This screencast shows how to build a Linear Classifier using Deeplearning4j.

Professor Ng provides an overview of the course in this introductory meeting. This course provides a broad introduction to machine learning and statistical pattern recognition. Topics include supervised learning, unsupervised learning, learning theory, reinforcement learning and adaptive control. Recent applications of machine learning, such as to robotic control, data mining, autonomous navigation, bioinformatics, speech recognition, and text and web data processing are also discussed.