Start Here Get Started and Get Good at Applied Machine Learning Hi, Jason 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: Many of my students have used this approach to go on and do well in Kaggle competitions and get jobs as Machine Learning Engineers and Data Scientists. Applied Machine Learning Process For a good summary of this process, see the posts: R Machine Learning
What Big Data, Data Science, Deep Learning software goes together? We analyze the associations between top Data Science tools, Commercial vs Free/Open Source, rank tools on R vs Python bias, find tools more associated with Big Data, those more associated with Deep Learning, and uncover strong regional differences. Last week, I reported the results of 2016 KDnuggets Software Poll: R, Python Duel As Top Analytics, Data Science software. This post looks a little deeper and examines the associations between different tools, their relationship to Big Data and Deep Learning, and regional patterns. At the end of the post there is a link to anonymized dataset, so that you can do your own analysis (and let me know about the results in comments below). The question asked in KDnuggets Poll was What software you used for Analytics, Data Mining, Data Science, Machine Learning projects in the past 12 months? First, we looked at associations between the top 10 tools. Lift (X & Y) = pct (X & Y) / ( pct (X) * pct (Y) ) where pct(X) is the percent of users who selected X.
ConvNetJS: Deep Learning in your browser ConvNetJS is a Javascript library for training Deep Learning models (Neural Networks) entirely 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). 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 The code is available on Github under MIT license and I warmly welcome pull requests for new features / layers / demos and miscellaneous improvements. Discussion Group
Which are the best known machine learning algorithms? Infographic - Think Big Data Which are the most important machine learning algorithms? Anyone who has been part of this domain must have faced or posed this question at some point of time. I too am asked this often. First things first – there are no winning algorithms. Most of us familiar with the subject would recall that in 2006, IEEE Conference on Data Mining identified the top 10 machine learning algorithms. For suggestions and improvements – please add your comments below. Best Known Machine Learning Algorithms by Application Type Anubhav is a data scientist who works and writes on new big data decision sciences models and their application in key business areas across industries.
Introduction to Strategic Thinking Starts June 22, 2015 This is a short interdisciplinary course on strategic thinking and some of its most powerful tools. Strategic thinking is not exclusive to business or military applications. 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. "The course was very enlightening! "Thank you for the stimulus and providing a completely different approach from what I was expecting from this class." "I come from a background where I learned, used and taught military doctrine and decision making and I therefore confidently entered into this course expecting to have my expertise confirmed and (possibly!) Sandjar Kozubaev Economist & Strategist Sandjar Kozubaev is an economist and a strategist.
Le Deep Learning pas à pas : les concepts (1/2) Des labos de R&D à la vie quotidienne L’image ci-contre vous rappelle bien quelque chose ? On dirait…, mais oui, c’est la Nuit étoilée de Van Gogh ? Une Nuit étoilée où le Golden Gate Bridge remplace cependant le village bucolique de Saint Remy-de-Provence. En 2012, Google a surpris la communauté du Machine Learning en démontrant que son Google Brain était capable de découvrir, par lui-même, des concepts de haut niveau tel que des visages, des corps humains ou des images de chats, ceci en épluchant des millions d’images glanées sur YouTube. Le point commun entre ces deux applications du Machine Learning ? Cet article comprend deux parties. Cet article présuppose une connaissance rudimentaire des concepts du Machine Learning. Flashback L’idée de construire des réseaux de neurones (RN) artificiels n’est pas neuve, elle remonte à la fin des années 50. Figure 1 : (a) un RN organisé en 4 couches, (b) le mécanisme d’activation d’un neurone Figure 2 : fonction d’activation sigmoïde
Simple machine learning: bot detection Machine Learning: The Bigger Picture, Part I This article is featured in the new DZone Guide to Big Data Processing, Volume III. Get your free copy for more insightful articles, industry statistics, and more. Abstract In the past few decades, computer systems have achieved a whole lot. They have managed to organize and catalog the information produced by our civilization as a whole. They have relieved us from stringent cognitive tasks and increased our productivity significantly. The classic computer system can be seen as a big information switching network that directs information to where it needs to go. Although impressive, we cannot say the SAGE system was very intelligent. The problem here is that if you ask someone how they recognizes a face they will simply tell you they don’t know, they just do. The second problem is that what defines a face is very difficult to describe. The third problem is that things tend to change over time. Machine Learning Machine Learning By Example The Discriminative Classifier f(x) = sign[a · x + b]
How to Learn a Machine Learning Algorithm The question of how to learn a machine learning algorithm has come up a few times on the email list. In this post I’ll share with you the strategy I have been using for years to learn and build up a structured description of an algorithm in a step-by-step manner that I can add to, refine and refer back to again and again. I even used it to write a book. This was just a strategy I used personally and I’ve been really surprised by the positive feedback. Algorithm Descriptions are Broken Learning a machine learning algorithm can be overwhelming. Implement a Machine Leaning AlgorithmPhoto by Nic’s events, some rights reserved If you are really really lucky you might find some suggested ways to configure the method for different situations. A fact you learn quickly when you want to implement a method from research papers is that algorithms are almost never described in sufficient detail for you to reproduce them. Get your FREE Algorithms Mind Map Download it, print it and use it. Download For Free