Also: 37 Reasons why your Neural Network is not working; Machine Learning vs. Statistics: The Texas Death Match of Data Science; Understanding overfitting: an inaccurate meme in Machine Learning; Recommendation System Algorithms: An Overview; The Ultimate Guide to Basic Data Cleaning

Maybe you were wondering how you can place funny objects on faces in real-time video chats or detect emotions? I'll show you one possible approach here utilizing deep learning as well as skim over one older approach. A challenging task in the past was detection of faces and their features like eyes, nose, mouth and even deriving emotions from their shapes. This task can be now "magically" solved by deep learning and any talented teenager can do it in a few hours. I will show you such an approach in this post.

This article is part of Demystifying AI, a series of posts that (try) to disambiguate the jargon and myths surrounding AI. I was nine years old when I had my first taste of programming, and fell in love with the art (yes, I believe programming is as much art as it is science). I quickly became fascinated with how I could control the flow of my programs by setting logical rules and conditions, if…else statements, switches, loops and more. In later years, I learned to remove clutter from my code by creating modules and abstracting pieces of code into functions and classes. I enhanced my software development skills with object oriented analysis and design (OOA/D). I learned code reuse and design patterns.

In the first part of this blog series, we gave you simple and elaborative definitions of what is artificial intelligence (AI), machine learning and deep learning. This is the second part of the series; here we are elucidating our readers with – What is the difference between AI, machine learning, and deep learning. You can think of artificial intelligence (AI), machine learning and deep learning as a set of a matryoshka doll, also known as a Russian nesting doll. Deep learning is a subset of machine learning, which is a subset of AI. Artificial intelligence is any computer program that does something smart.

Over the last couple years, I've spent an increasing amount of time diving into the possibilities Deep Learning (DL) offers in terms of what we can do with Artificial Intelligence (AI). Some of these possibilities have already been realized (more on this later in the post). And, I could not be more excited to see them out in the world. Through it all, I've felt there are a handful of breath-taking realities that most people are not grasping when it comes to an AI-Powered world. Why the implications are far deeper for humanity than we imagine. Why in my areas of expertise, marketing, sales, customer service and analytics, the impact will be deep and wide. Why is this not yet another programmatic moment. Why the scale at which we can (/have to) solve the problems is already well beyond the grasp of the fundamental strategy most companies follow: We have a bigger revenue opportunity, but we don't know how to take advantage? Let's buy more hamster wheels, hire more hamsters and train them to spin faster!

Deep Learning A-Z: Hands-On Artificial Neural Networks by Kirill Eremenko and Hadelin de Ponteves will teach you Deep Learning with Artificial Neural Networks. You will work with Tensorflow and Pytorch to build several different types of Neural Networks. Data Science: Deep Learning in Python by Lazy Programmer Inc. will teach you build Neural Networks from scratch in Python, numpy & TensorFlow. You will learn about the various types and terms associated to neural networks. Natural Language Processing with Deep Learning in Python by Lazy Programmer Inc. will teach you everything about deriving and implementing word2vec, GLoVe, word embeddings, and sentiment analysis with recursive nets.

To learn how humans and AI systems can best live together, we may need to kill a whole lot of Zerg. DeepMind, the AI-focused unit of Alphabet, and the games company Blizzard Entertainment are releasing a set of tools that will let will programmers unleash all sorts of AI algorithms inside the space-themed game StarCraft. The game is more challenging than most of those tackled by AI programs to date. Not only is StarCraft extremely complex, it also requires planning far ahead and trying to second-guess what your opponent is up to. This means developing AI programs capable of matching humans ought to help researchers explore new facets of humanlike intelligence with machines.

Today IBM announced availability of the beta version of its Distributed Deep Learning software it says has demonstrated "a leap forward in deep learning performance." Deep learning is a form of AI that relies on the application of "artificial neural networks" inspired by the biological neural networks of human and animal brains. Its focus is on giving computers the ability to "understand" the contents of digital images, videos, audio recordings and the like in much the same way that people do. Much of the potential for deep learning remains unfulfilled, however, because the logistics of processing the great amount of data required for a system's "deep level training" makes it a slow process that can take days or even weeks. Accuracy of the results is another issue contributing to the time factor, as the system needs to be taught multiple times in order to gain the desired results.

With advances in machine learning and the deployments of neural networks, logistic regression-powered models are expanding their uses throughout PayPal. PayPal's deep learning system is able to filter out deceptive merchants and crack down on sales of illegal products. Kutsyy explained the machines can identify "why transactions fail, monitoring businesses more efficiently," avoiding the need to buy more hardware for problem solving. The AI Podcast is available through iTunes, DoggCatcher, Google Play Music, Overcast, PlayerFM, Podbay, Pocket Casts, PodCruncher, PodKicker, Stitcher and Soundcloud.

The next time you don't recognize a transaction listed on your monthly PayPal statement, rest assured: AI will likely identify the culprit and help ensure it won't happen again. "AI has been used at PayPal for a long time, it's at the core of the company, distinguishing the good customers from the bad customers," Vadim Kutsyy, a data scientist at the online payments company, told host Michael Copeland on this week's edition of the AI Podcast. With advances in machine learning and the deployments of neural networks, logistic regression-powered models are expanding their uses throughout PayPal. PayPal's deep learning system is able to filter out deceptive merchants and crack down on sales of illegal products. Additionally, the models are optimizing operations.