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Machine Learning with Python

Working... ► Play all Machine Learning with Python sentdex47 videos88,270 viewsLast updated on Jul 21, 2016 Play all. Featured Learning Paths - Big Data University. MetaAcademy. ConvNetJS: Deep Learning in your browser. ConvNetJS is a Javascript library for training Deep Learning models (Neural Networks) entirely in your browser.

ConvNetJS: Deep Learning in your browser

Open a tab and you're training. No software requirements, no compilers, no installations, no GPUs, no sweat. Description The library allows you to formulate and solve Neural Networks in Javascript, and was originally written by @karpathy (I am a PhD student at Stanford). However, the library has since been extended by contributions from the community and more are warmly welcome. Common Neural Network modules (fully connected layers, non-linearities) Classification (SVM/Softmax) and Regression (L2) cost functions Ability to specify and train Convolutional Networks that process images An experimental Reinforcement Learning module, based on Deep Q Learning.

Head over to Getting Started for a tutorial that lets you get up and running quickly, and discuss Documentation for all specifics. Code Discussion Group. Companies. Start Here. Get Started and Get Good at Applied Machine Learning Hi, Jason here.

Start Here

I’m the guy behind Machine Learning Mastery. My goal is to help you get started, make progress and kick butt with machine learning. I teach a top-down and results-first approach designed for developers and engineers. This is unlike most academic textbooks and university courses. Access my best free tutorials on the blog or take the next step with my paid training material. You may be feeling overwhelmed. Take your time. Table of Contents What do you need help with? How Do I Get Started? The most common question I’m asked is: “how do I get started?” My best advice for getting started in machine learning is broken down into a 5-step process: For more on this top-down approach, see:

Deep learning - A Visual Introduction. Deep Learning: AlchemyAPI Tutorial. Deep Learning: Intelligence from Big Data. A Visual Introduction to Machine Learning. Finding better boundaries Let's revisit the 240-ft elevation boundary proposed previously to see how we can improve upon our intuition.

A Visual Introduction to Machine Learning

Clearly, this requires a different perspective. By transforming our visualization into a histogram, we can better see how frequently homes appear at each elevation. While the highest home in New York is ~240 ft, the majority of them seem to have far lower elevations. Your first fork A decision tree uses if-then statements to define patterns in data. For example, if a home's elevation is above some number, then the home is probably in San Francisco. In machine learning, these statements are called forks, and they split the data into two branches based on some value. That value between the branches is called a split point. Tradeoffs Picking a split point has tradeoffs. Look at that large slice of green in the left pie chart, those are all the San Francisco homes that are misclassified.

The best split Recursion. A Tour of Machine Learning Algorithms. In this post, we take a tour of the most popular machine learning algorithms.

A Tour of Machine Learning Algorithms

It is useful to tour the main algorithms in the field to get a feeling of what methods are available. It is useful to tour the main algorithms in the field to get a feeling of what methods are available. There are so many algorithms available and it can feel overwhelming when algorithm names are thrown around and you are expected to just know what they are and where they fit. Machine Learning Tutorial. Machine Learning Tutorial. Interview with Google's AI and Deep Learning 'Godfather' Geoffrey Hinton. Simple machine learning: bot detection. SlideRule Foundations of Data Science. Machine Learning. Probability Primer. Information Theory. Welcome to Game Theory. Introduction to Strategic Thinking. Starts June 22, 2015 This is a short interdisciplinary course on strategic thinking and some of its most powerful tools.

Introduction to Strategic Thinking

Strategic thinking is not exclusive to business or military applications. The skills taught in this course can be used by everyone. Young professionals can use the knowledge to effectively plan their careers, stay-at-home mothers can use it to improve how they communicate with their children, and entrepreneurs can use it to better position their business in the marketplace. Anyone who wants to learn how to think about and solve social, business, personal, environmental, or other problems in smarter and more creative ways will benefit from this course. We will draw lessons and use concepts from economics, game theory, scenario planning, behavioral sciences, and futures studies, as well as philosophy and linguistics. This course is designed to be a starting point for people who are just beginning to learn about strategic thinking.

"Fascinating course. Sandjar Kozubaev.