Machine learning is one of the fastest-growing and most exciting fields out there, and deep learning represents its true bleeding edge. In this course, you'll develop a clear understanding of the motivation for deep learning, and design intelligent systems that learn from complex and/or large-scale datasets. We'll show you how to train and optimize basic neural networks, convolutional neural networks, and long short term memory networks. Complete learning systems in TensorFlow will be introduced via projects and assignments. You will learn to solve new classes of problems that were once thought prohibitively challenging, and come to better appreciate the complex nature of human intelligence as you solve these same problems effortlessly using deep learning methods.

You can find many additional references here (Python, Excel, Spark, R, Deep Learning, AI, SQL, NoSQL, Graph Databses, Visualization, etc.) as well as here, here, and here.

Today we can collect lots and lots of performance data. We build beautiful dashboards and even have fancy query languages to access and transform the data. Still performance data is a secret language only a couple of people understand. The more business becomes digital the more stakeholders are interested in this data including how it relates to business. Some of these people have never used a monitoring tool before.

S. Somasegar is a venture partner at Madrona Venture Group and the former head of Microsoft's Developer Division. More posts by this contributor: Escaping the trough of disillusionment for virtual and augmented reality The intelligent app ecosystem (is more than just bots!) How to join the network Daniel Li is an investor with Madrona Venture Group. More posts by this contributor: The intelligent app ecosystem (is more than just bots!) How to join the network You can hardly talk to a technology executive or developer today without talking about artificial intelligence, machine learning or bots. While everyone agrees on the importance of machine learning to their company and industry, few companies have adequate expertise to do what they wanted the technology to do. Here are some insights into what we can expect in the coming years around ML and AI.

Now if these are not dominant steps toward a world of AI I don't know what is. Front this technology with a robot body and it becomes an intelligent being. Google Assistant, Amazon Echo, Apple Siri and the other personal virtual assistants are bringing all daily rituals, habits and requests together via one central point. You want to control the lights, heating, music, TV, or order the shopping, these tools are evolving to allow you to do any of the above firstly from an initial request, but then by learning what your habits are and when you require things. We are surrounded by AI, is it just a matter of time?

Over the past few decades, artificial intelligence, or AI, has morphed from science fiction into an integral part of 21st century life. And what we've seen so far is just the beginning. Experts expect its use to skyrocket in coming years, and market researcher IDC forecasts that by 2020 spending on AI will rise nearly 500 percent to $47 billion from current levels. As Goldman Sachs (GS) noted in a recent report to clients, AI's potential appears boundless. IBM's (IBM) Jeopardy-playing supercomputer Watson may be the technology's best-known example.

One of the first questions I have asked no one in particular is: what will change when artificial intelligence is part of the fabric of day to day life? It sounds like a simple question, but it had a logical fallacy in that for the last 30 years, artificial intelligence (AI) projects were science experiments with high expectations and low delivery rates. The AI systems of the past were nowhere near as commonplace as the possibilities that were presented to us. We weren't even nearing the feats of the "The Engine," described in Jonathan Swift's Gulliver's Travels almost 300 years ago. Although Swift may have been skewering purposeless-science, The Engine actually sounds pretty useful right now. But before we get there, we have some things to solve.

Guest blog by Sebastian Raschka, originally posted here. If we tackle a supervised learning problem, my advice is to start with the simplest hypothesis space first. I.e., try a linear model such as logistic regression. If this doesn't work "well" (i.e., it doesn't meet our expectation or performance criterion that we defined earlier), I would move on to the next experiment. I would say that random forests are probably THE "worry-free" approach - if such a thing exists in ML: There are no real hyperparameters to tune (maybe except for the number of trees; typically, the more trees we have the better).

Let's punch through the noise around machine learning. The tech ecosystem is well acquainted with buzzwords. From "Web 2.0" to "cloud computing" to "mobile first" to "on-demand," it seems as though each passing year heralds the advent and popularization of new catchphrases to which fledgling companies attach themselves. But while the trends these phrases represent are real, and category-defining companies will inevitably give weight to newly coined buzzwords, so too will derivative startups seek to take advantage of concepts that remain ill-defined by experts and little-understood by everyone else. "It's clear that 9 of 10 investors have very little idea what AI is so if you're a founder raising money, you should sprinkle some AI into your pitch deck. Use of'artificial intelligence,' 'AI,' 'chatbot,' or'bot' are winners right now and might get you a little valuation bump or get the process to move quicker. If you want to drive home that you're all about that AI, use terms like machine learning, neural networks, image recognition, deep learning, and NLP. Then sit back and watch the funding roll in."

What do you get if you cross aspirin with ibuprofen? Harvard chemistry professor Alán Aspuru-Guzik isn't sure, but he's trained software that could give him an answer by suggesting a molecular structure that combines properties of both drugs. The AI program could help the search for new drug compounds. Pharmaceutical research tends to rely on software that exhaustively crawls through giant pools of candidate molecules using rules written by chemists, and simulations that try to identify or predict useful structures. The former relies on humans thinking of everything, while the latter is limited by the accuracy of simulations and the computing power required.