Deep Learning is a new area of Machine Learning research, which has been introduced with the objective of moving Machine Learning closer to one of its original goals: Artificial Intelligence. See these course notes for a brief introduction to Machine Learning for AI and an introduction to Deep Learning algorithms. Deep Learning is about learning multiple levels of representation and abstraction that help to make sense of data such as images, sound, and text. The tutorials presented here will introduce you to some of the most important deep learning algorithms and will also show you how to run them using Theano. Theano is a python library that makes writing deep learning models easy, and gives the option of training them on a GPU.

Deep learning is not a beginner-friendly subject -- even for experienced software engineers and data scientists. If you've been Googling this subject, you may have been confused by the resources you've come across. To find the best resources, we surveyed engineers on their favorite sources for deep learning, and these are what they recommended. These educational resources include online courses, in-person courses, books, and videos. All are completely free and designed by leading professors, researchers, and industry professionals like Geoffrey Hinton, Yoshua Bengio, and Sebastian Thrun.

This course is the next logical step in my deep learning, data science, and machine learning series. I've done a lot of courses about deep learning, and I just released a course about unsupervised learning, where I talked about clustering and density estimation. So what do you get when you put these 2 together? In these course we'll start with some very basic stuff - principal components analysis (PCA), and a popular nonlinear dimensionality reduction technique known as t-SNE (t-distributed stochastic neighbor embedding). Next, we'll look at a special type of unsupervised neural network called the autoencoder.

Featured will be 24-hour content from our annual CX conference, SCORE, as well as a handful of live webcasts--serving CX professionals responsible for driving superior customer experiences with innovative, yet proven, strategies. Learn how to differentiate your brand with a fast, simple purchasing process saving your customers time and effort. You'll transform your buying experience to create loyal, raving fans, while empowering your reps to spend more time selling. Reach people on the phone, via live chat, email, through social media, and even in person. Use visitor tracking and email analytics to know what your customers are seeing.

Kaggle is a community and site for hosting machine learning competitions. Competitive machine learning can be a great way to develop and practice your skills, as well as demonstrate your capabilities. In this post, you will discover a simple 4-step process to get started and get good at competitive machine learning on Kaggle. How to Get Started with Kaggle Photo by David Mulder, some rights reserved. I took my last response to this question and decided to turn it into this blog post.I hope you find it useful.

Webpack is the premier build tool for React and Angular 2 applications. In this course you;ll master each major feature of Webpack and learn how to optimize it for your own app. Webpack has a wealth of fancy features, but each requires in depth knowledge of how they work.

The first step is often the hardest to take, and when given too much choice in terms of direction it can often be debilitating. This post aims to take a newcomer from minimal knowledge of machine learning in Python all the way to knowledgeable practitioner in 7 steps, all while using freely available materials and resources along the way. The prime objective of this outline is to help you wade through the numerous free options that are available; there are many, to be sure, but which are the best? What is the best order in which to use selected resources? It would probably be helpful to have some basic understanding of one or both of the first 2 topics, but even that won't be necessary; some extra time spent on the earlier steps should help compensate.

The Microsoft Azure ML team recently announced the availability of 3 ML templates on the Azure ML Studio – for online fraud detection, retail forecasting and text classification. These templates demonstrate industry best practices and common building blocks used in an ML solution for a specific domain, starting from data preparation, data processing, feature engineering, model training to model deployment (as a web service) . The goal for Azure ML templates is to make data scientists more productive and faster in building and deploying their custom ML solutions on the cloud. Templates include a collection of pre-configured Azure ML modules as well as custom R scripts in the Execute R Script modules to enable an end-to-end solution. We'll walk through these templates in detail in this and future webinars.

Anyone can teach themselves to have a memory the size of a champion, a study shows. Scans found ordinary members of the public had brains as sharp as the world's greatest memorisers after a simple brain training course using'memory palaces'. It means the ability to perform astonishing feats - such as remembering lists of several dozen words - can be learned, say scientists. After 40 days of daily 30-minute training sessions individuals who had typical memory skills at the start and no previous practise more than doubled their capacity. In this study, the learning strategy scientists chose was loci training, also known as creating a'memory palace'.