TensorFlow has been open to the public for about a year now and the buzz is real. Everyone is building models using TensorFlow, and we even have some state of the art examples such as: Parsey McParseface. As a data scientist at Old Street Labs(OSL) one of the most common questions I'm asked is how we deploy models and manage those in a production environment. TensorFlow Serving makes it simple and quick to deploy your models into a production environment. It also works with TensorFlow models out of the box without much modification. The purpose of this tutorial is to provide a brief introduction into TensorFlow Serving by walking through the process of deploying a TensorFlow model inside a Docker container.

Glancing around school classrooms in 2016, it's easy to miss just how far technology has transformed learning over the last decade. The desks, whiteboards and rows of chairs are the same, but so much else has changed that can't be seen. A third of Britain's schools are asking students to bring their own tablets and laptops into the classroom now, coding has been on the national curriculum for three years, and more and more education is happening outside school through apps and digital services. But these changes are just the start. Artificial intelligence (AI) is the next giant leap in learning and, according to those working in the field of education and technology, we haven't seen anything yet.

Did you have a good, relaxing break over the summer? Are you refreshed and re-energised, looking forward to a new start, a new you and brushing up on your data analysis skills? If so, I've thrown together a collection of a few excellent (and free!) statistics eBooks for your Kindle to sharpen up your stats while you're on the long commute to work. Just try not to read them while driving! These books require different levels of existing knowledge, and while some are for early-stage data scientists others are for more hard-core physicists and mathematicians.

This complimentary business course is designed to help demystify artificial intelligence, provide an overview of a wide range of cognitive technologies, and offer a framework to help you understand the business implications of AI and cognitive technologies.

Decision Trees are a graphic and intuitive method of predicting the outcome of a given input. They attach a weightage to the input variables and help you clearly detect what really influences your outcome. Building a Decision Tree is a tedious procedure, as they have the tendency to overfit. That's where Random Forests come into the picture. Random Forests use an ensemble of Decision Trees, this reduces the complexities without compromising on the advantages.

If you're a software engineer (or someone who's learning the craft), chances are that you've heard about deep learning (which we'll sometimes abbreviate as "DL"). It's an interesting and rapidly developing field of research that's now being used in industry to address a wide range of problems, from image classification and handwriting recognition, to machine translation and, infamously, beating the world champion Go player in four games out of five. A lot of people think you need a PhD or tons of experience to get a job in deep learning, but if you're already a decent engineer, you can pick up the requisite skills and techniques pretty quickly. Important point: You need to have motivation and be able to code and problem solve well. Here at Deepgram we're using deep learning to tackle the problem of speech search.

Python is one of the fastest-growing platforms for applied machine learning. In this mini-course, you will discover how you can get started, build accurate models and confidently complete predictive modeling machine learning projects using Python in 14 days. This is a big and important post. You might want to bookmark it. Python Machine Learning Mini-Course Photo by Dave Young, some rights reserved.

Python is one of the fastest-growing platforms for applied machine learning. In this mini-course, you will discover how you can get started, build accurate models and confidently complete predictive modeling machine learning projects using Python in 14 days. This is a big and important post. You might want to bookmark it. Python Machine Learning Mini-Course Photo by Dave Young, some rights reserved.

Now we can use the function f f(x), which we call the target function, as the proxy for the conditional distribution.

First--try some of the introductory tutorial/competitions. Those get your feet wet. Then just jump head first into a competition. Try and be active on the forums. I have found that the best way to learn is just struggle with it (in most anything--I faked my way into a DB engineer once, 2 years later I was teaching the course on SQL at a Fortune 100 company--I had my share of run-ins with the Admin though--we were on a first name basis)).