Alex, one big data solution definitely worth mentioning is the HPCC systems from LexisNexis. Many uses of big data have a measurable positive impact on outcomes and productivity. Areas such as record linkage, graph analytics deep learning and machine learning have demonstrated being critical to help fight crime, reduce fraud, waste and abuse in the tax and healthcare systems, combat identity theft and fraud, and many other aspects that help society as a whole. It is worth mentioning the HPCC Systems open source offering which provides a single platform that is easy to install, manage and code. Their built-in analytics libraries for Machine Learning and integration tools with Pentaho for great BI capabilities make it easy for users to analyze Big Data.

This is an introductory tutorial on using Theano, the Python library. I'm going to start from scratch and assume no previous knowledge of Theano. However, understanding how neural networks work will be useful when getting to the code examples towards the end. I recently gave this tutorial as a talk in University of Cambridge and it turned out to be way more popular than expected. In order to give more people access to the material, I'm now writing it up as a blog post. I do not claim to know everything about Theano, and I constantly learn new things myself.

Welcome to the first lesson in our Practicing Deep Learning Series. Thoughtly is writing a multi-part tutorial series focused on understanding the foundations of Deep Learning, specifically as they apply to Natural Language Processing. This series, like our previous series, is targeted towards practitioners of machine learning. Now we are looking to provide information for developers who wish to cultivate a working familiarity with neural networks (NN) and deep learning (DL). Our goal is to help ML students, amateurs and professionals move from an awareness of neural networks to a working familiarity with the tools and workflows necessary to accomplish real-world tasks with a neural network.

Businesses increasingly use machine-learning models to recognize patterns in big data and to implement data-driven decision-making. In this webinar, you will learn how Domino serves as a platform for experimentation and collaboration, and facilitates the creation and distribution of machine-learning models. We will give you an introduction on how to use Plotly--an interactive data visualization tool--to share the results from your models more effectively. We will also show you how to use Plotly's API libraries in Domino Data Lab to build insightful graphs, charts and data visualizations in Python and in R. Chelsea is a core developer of Plotly, the data visualization platform, and plotly.js, the open-source Javascript library. She is also responsible for maintaining Plotly's interactive, browser-based charting libraries and documentation for R and Python.

I'm really hoping for some feedback or improvements to what I've written as I'm trying to make a complete tutorial series that will introduce a novice to machine learning and set them up with the skills needed to produce their own Neural Networks I've never tried writing anything like this before, so any constructive feedback that I can apply to the next entry in the series would be much appreciated

Machine Learning is the art of writing programs that get better at performing a task as they gain experience, without being explicitly programmed to do so. Feed your program more data, and it will get smarter at handling new situations. Some machine learning algorithms use fairly advanced math, but simple approaches can be surprisingly effective. In this session, we'll take a classic Machine Learning challenge from Kaggle.com, automatically recognizing hand-written digits (http://www.kaggle.com/c/digit-recognizer), So bring your laptop, and let's see how smart we can make our machines!

Prerequisites: No prerequisites, knowledge of some undergraduate level mathematics would help but is not mandatory. Working knowledge of Python would be helpful if you want to run the source code that is provided. Taught by a Stanford-educated, ex-Googler and an IIT, IIM – educated ex-Flipkart lead analyst. This team has decades of practical experience in quant trading, analytics and e-commerce. The course is shy but confident: It is authoritative, drawn from decades of practical experience -but shies away from needlessly complicating stuff.

The web is full of good explanations of machine learning algorithms. And every second applicant for a data science position has finished the Coursera course on machine learning. Theory will not help you choose good values for the 16 parameters a standard implementation of a random forest takes. The default values are good to get started, but which parameters should you modify depending on your data? Choosing the right features, algorithms and parameters is an art.

Prerequisites: No prerequisites, knowledge of some undergraduate level mathematics would help but is not mandatory. Working knowledge of Python would be helpful if you want to run the source code that is provided. Taught by a Stanford-educated, ex-Googler and an IIT, IIM - educated ex-Flipkart lead analyst. This team has decades of practical experience in quant trading, analytics and e-commerce. The course is shy but confident: It is authoritative, drawn from decades of practical experience -but shies away from needlessly complicating stuff.

When we first began working on Leada, we sought to better understand the data science industry by interviewing professionals in the field. As students simply wanting to learn more about data science, we ultimately created a free resource to inform both undergraduates and professionals about the data science industry. We accomplished this by having Q & A interviews with experts such as Mike Olsen, Hal Varian, Tom Davenport, and data scientists at LinkedIn, Facebook, Yelp, and more. The Data Analytics Handbook was not only instrumental in giving us the understanding we needed to feel confident in what we were creating; but was downloaded over 25,000 times, gave us dozens of contacts, and an immediate group of early adopters. Some experts took longer to contact than others (I emailed Hal Varian over 8 times) but you would be surprised who you can get 25 minutes of time to help inform others.