This article was posted by Xiu-Shen Wei. Xiu-Shen Wei is a 2nd-year Ph.D. candidate of Department of Computer Science and Technology in Nanjing University and a member of LAMDA Group. Deep Neural Networks, especially Convolutional Neural Networks (CNN), allows computational models that are composed of multiple processing layers to learn representations of data with multiple levels of abstraction. These methods have dramatically improved the state-of-the-arts in visual object recognition, object detection, text recognition and many other domains such as drug discovery and genomics. In addition, many solid papers have been published in this topic, and some high quality open source CNN software packages have been made available.

I often get pitched with a superior deep learning solution for Natural Language Understanding (NLU). After all, deep learning is the disruptive new force in AI. A better NLU AI entices many useful advancements, ranging from smarter chat bots and virtual assistants to news categorization, with an ultimate promise of better language comprehension. Lets assume this superior deep learning (DL) "product" is called "(dot)AI". Their pitch deck will invariably have a bar chart that looks something like this -- the claim being that the new DL topic classifier/tagger of (Dot)AI is better than state of the art methods.

Anima Anandkumar pioneered the research of finding global optimal in non-convex problems, a big pain point in machine learning. Our protagonist for this week's Techie Tuesdays, Anima is an academician who represents the best of both worlds--industry and academia. She has contributed significantly to major AI and ML projects at Amazon. This will be a treat for all machine learning enthusiasts. In my two hours of conversation with Anima Anandkumar, Principal Scientist at Amazon Web Services, I've had the most potent dose of technical knowledge ever injected. Not that I didn't expect it while talking to an ex-faculty of UC Irvine (soon to be an endowed professor at Caltech), known for her research on non-convex problems (in deep learning). Our Techie Tuesdays protagonist of the week, Anima has worked towards establishing a strong collaboration between academia and industry. She follows an unconventional style of teaching, the one she would have loved as a student.

This talk aims to gently bridge the divide by demonstrating how deep learning operates on core machine learning concepts and getting attendees started coding deep neural networks using Google's TensorFlow library.

Google announced a new open source system Monday that could speed the process for creating and training machine learning models within the firm's TensorFlow library. Tensor2Tensor (T2T), unveiled via a blog post, is geared toward creating deep learning models in particular, and can be used for a variety of purposes. T2T can be used to build models for processes such as text translation or parsing, as well as image captioning, the post said. It also allows users to create these models and explore their ideas "much faster than previously possible," the post noted. One of the main goals of T2T seems to be lowering the barrier to entry for users looking to experiment with deep learning and compare their findings against other work in the field.

Time is one of the biggest obstacles to the adoption of deep learning. It can take days to train one of these systems even if you have massive computing power at your disposal -- on more modest hardware, it can take weeks. Google might just fix that. It's releasing an open source tool, Tensor2Tensor, that can quickly train deep learning systems using TensorFlow. In the case of its best training model, you can achieve previously cutting-edge results in one day using a single GPU.

Andrew Beam does a great job showing that small datasets are not off limits for current neural net methods. If you use the regularisation methods at hand – ANNs is entirely possible to use instead of classic methods. Let's see how this holds up on up on some benchmark datasets. Let's start with the iris dataset that you nicely can pull with the pandas read_csv function right of the internets. We create a feature matrix X and a target y from the Pandas dataframe.

Sci-fi level Artificial Intelligence (AI) like HAL 9000 it was promised since 1960s, but PCs and robots kept dumb until recently. Now, tech giants and startups are announcing the AI revolution: self-driving cars, robo doctors, robo investors, etc. "AI" it's the 2017 buzzword, like "dot com" it was in 1999. Is this a marketing bubble or real? Machine learning (ML), a subset of AI, make machines learn from experience, from examples of the real world: the more the data, the more it learns. A software is said to learn from experience with respect to a task, if its performance at doing the task improves with experience. Artificial Neural Networks (ANN) is only one approach to ML, others include decision trees, regression etc. Deep learning is an ANN with many levels of abstraction. Despite the "deep" hype, popular ML methods are "shallow" too.

Editor's Note: Many TOPBOTS readers have asked us for advice on learning modern techniques like deep learning and getting jobs in AI. No one knows more about this subject than Rachel Thomas, deep learning researcher and co-founder of Fast.ai. Fast.ai is committed to democratizing practical AI education globally and offers popular MOOCs to get you ramped up fast. Without further ado, I'll let Rachel share her wisdom about starting a career in AI without a machine learning PhD. I was recently asked questions by two readers with diametrically opposed premises: one was excited that machine learning is now "automated" by services like Google Cloud, the other was concerned that machine learning takes too many years of prerequisite study, citing a popular Hacker News thread as his source.

In late May at the Future of Go Summit in Wuzhen, China, DeepMind's Go-playing artificial intelligence program AlphaGo won a three-game match over grandmaster Ke Jie and continued its dominance over human players. The Chinese Weiqi Association awarded AlphaGo 9-dan professional status, the highest possible rank for Go players, and Google subsidiary DeepMind announced AlphaGo's retirement, assigning its project team to other AI projects. AlphaGo was one of the biggest AI breakthroughs in recent memory because it beat the best human players at a game much more complex than chess and other board games. DeepMind relied on deep learning to program AlphaGo to learn Go in a similar manner to a human. But what makes deep learning deep?