Machine Learning is the practice of using algorithms to parse data, learn from it, and then make a determination or prediction about something in the world. This is at its most basic. Rather than hand-coding software routines with a specific set of instructions to accomplish a particular task, the machine is "trained". By using large amounts of data and algorithms that give it the ability to learn how to perform the task. Deep Learning enables many practical applications of Machine Learning and by extension the overall field of AI.

For science fiction fans like me, Artificial Intelligence (AI) has always fired up the imagination. As a field of study, AI has been a part of academia since the mid-1950s. Since then, AI has been hyped as the key to our civilization's future, and panned as nothing more than entertainment for nerds. Over the past few years though, AI has started to gain real traction. A lot of this has to do with the availability of powerful, cheaper and faster computing capability, the emergence of the Internet of Things, and the explosion of data generated as images, text, messages, documents, transactions, mapping and other data.

Publications like The Wall Street Journal, Forbes and Fortune have all called 2017 "The Year of AI." AI beat professional gamers and poker players. Access to deep learning education widened through several online programs. The speech recognition accuracy record was broken multiple times. And research universities like Oxford and Massachusetts General Hospital invested in their own supercomputers. These are a few of many milestones in 2017.

Machine learning and deep learning are increasingly disrupting all sectors of society, making it possible to improve artificial intelligence, halt the spread of malware, among many other benefits. However, scientists are not the only ones interested in it. As part of a series of blog posts, CMF Trends met with Google engineer Damien Henry during the Google I/O 2016 developers conference. Damien Henry leads the Cultural Institute Experiment Team (CILEx), part of the Google Cultural Institute. This team uses modern tools -- including machine learning -- for artistic purposes.

Integrating a voice or chatbot interface into a product used to require a Natural Language Understanding (NLU) cloud service. Today, we are open sourcing Snips NLU, a Private by Design, GDPR compliant NLU engine. It can run on the Edge or on a server, with minimal footprint, while performing as good or better than cloud solutions. From the 60 million messages Facebook bots process every day, to the tens of millions of users now talking to an Alexa or Google-powered device, natural language has become a preferred mode of interaction between people and machines. A new skill is being added to the Amazon Alexa skill store every 90 minutes, making voice assistants grow faster than smartphone app stores did.

In this 5th part on Deep Learning from first Principles in Python, R and Octave, I solve the MNIST data set of handwritten digits (shown below), from the basics. To do this, I construct a L-Layer, vectorized Deep Learning implementation in Python, R and Octave from scratch and classify the MNIST data set. The MNIST training data set contains 60000 handwritten digits from 0-9, and a test set of 10000 digits. MNIST, is a popular dataset for running Deep Learning tests, and has been rightfully termed as the'drosophila' of Deep Learning, by none other than the venerable Prof Geoffrey Hinton. The'Deep Learning from first principles in Python, R and Octave' series, so far included Part 1, where I had implemented logistic regression as a simple Neural Network. Part 2 implemented the most elementary neural network with 1 hidden layer, but with any number of activation units in that layer, and a sigmoid activation at the output layer.

Mark Zuckerberg, the founder and C.E.O. of Facebook, admitted recently his company knew, in 2015, that the data firm Cambridge Analytica, which assisted with Donald Trump's election campaign, had improperly acquired information on 50 million Facebook users. "This was a breach of trust," Zuckerberg said, in a Facebook post. "We need to fix that." But that's not the only thing Facebook needs to fix. "The problem with Facebook is not just the loss of your privacy and the fact that it can be used as a totalitarian panopticon," said François Chollet, an artificial intelligence and machine learning software engineer at Google DeepMind, in a tweet yesterday.

ZhuSuan is a python probabilistic programming library for Bayesian deep learning, which conjoins the complimentary advantages of Bayesian methods and deep learning. Unlike existing deep learning libraries, which are mainly designed for deterministic neural networks and supervised tasks, ZhuSuan provides deep learning style primitives and algorithms for building probabilistic models and applying Bayesian inference. Variational inference with programmable variational posteriors, various objectives and advanced gradient estimators (SGVB, REINFORCE, VIMCO, etc.). ZhuSuan is still under development. Before the first stable release (1.0), please clone the repository and run This will install ZhuSuan and its dependencies automatically.

Many state of the art deep learning models have hefty compute, storage and power consumption requirements which make them impractical or difficult to use on resource-constrained devices. In this tech talk, you'll learn why Apache MXNet, an open Source library for Deep Learning, is IoT-friendly in many ways. In addition, you'll learn how services like AWS Lambda and AWS Greengrass make it easy to deploy MXNet models on edge devices.

In the past couple of years, large companies including Google, Facebook, Microsoft, and Amazon have been releasing libraries, frameworks, and services that enable other businesses to build machine learning (ML)models. What's great about these frameworks is that it's now cheaper and faster to run a machine learning experiment for your business. Building useful machine learning models often takes a lot of data -- thousands of examples -- as well as a lot of time to prep the data in a format that is appropriate for the system. The content needs to be carefully curated and high quality. This isn't always easy to come by.