Are the NYU Deep Learning course taken down? I want to re-watch some of the videos, but it seems like they taken down.

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Artificial intelligence startups are emerging as a magnet for venture capital investment. Today, it was the San Francisco-based Skymind Inc. that took the spotlight after raising 3 million in funding from Chinese web giant Tencent Holdings Ltd., Y Combinator and three other early-stage backers. The group joins an already impressive list of investors that includes Lookout Security Inc. Deepmind will use the capital to spread the word about its open-source Deeplearning4j framework, which promises to speed up the development of artificial intelligence applications. The project distinguishes itself from the numerous free alternatives out there by providing a number of value-added features specifically geared towards the requirements of large enterprises.

As if quantum computing wasn't mind-bending enough, one of D-Wave Systems' founders is now pursuing another futuristic idea: using artificial intelligence and high-tech exoskeleton suits to allow humans--and, at least according to one description of the technology, monkeys--to control and train an army of intelligent robots. Geordie Rose is a cofounder and chief technology officer of D-Wave, the Canadian company selling machines that it claims exploit quantum mechanical effects to solve certain problems hundreds of millions times faster than traditional computers. Now an IEEE Spectrum investigation has discovered that Rose is also CEO of Kindred Systems (aka Kindred AI), a stealthy startup he founded with others in 2014 dedicated to delivering advanced teleoperated and autonomous robots. The goal is making programming robots faster and less costly–and possibly revolutionize the world of work. Kindred has so far received well over US 10 million in funding, according to Data Collective, the venture capital firm that led one of the rounds.

We alluded to the possibility of Deep Learning and IoT previously where we said that Deep learning algorithms play an important role in IoT analytics because Machine data is sparse and / or has a temporal element to it. Devices may behave differently at different conditions. Hence, capturing all scenarios for data pre-processing/training stage of an algorithm is difficult. Deep learning algorithms can help to mitigate these risks by enabling algorithms learn on their own. This concept of machines learning on their own can be extended to machines teaching other machines.

Tensorflow is the obvious choice, but I found MXNet to be another viable option. Is there any reason one should not go for MXNet in favour of Theano/Tensorflow?

This reference is a part of a new series of DSC articles, offering selected tutorials on subjects such as deep learning, machine learning, data science, deep data science, artificial intelligence, Internet of Things, algorithms, and related topics. It is designed for the busy reader who does not have a lot of time digging into long lists of advanced publications.

A novel variational autoencoder is developed to model images, as well as associated labels or captions. The Deep Generative Deconvolutional Network (DGDN) is used as a decoder of the latent image features, and a deep Convolutional Neural Network (CNN) is used as an image encoder; the CNN is used to approximate a distribution for the latent DGDN features/code. The latent code is also linked to generative models for labels (Bayesian support vector machine) or captions (recurrent neural network). When predicting a label/caption for a new image at test, averaging is performed across the distribution of latent codes; this is computationally efficient as a consequence of the learned CNN-based encoder. Since the framework is capable of modeling the image in the presence/absence of associated labels/captions, a new semi-supervised setting is manifested for CNN learning with images; the framework even allows unsupervised CNN learning, based on images alone.

Last March, a computer built by a team of Google engineers beat one of the world's top players at the ancient game of Go. The match between AlphaGo and Korean grandmaster Lee Sedol was so exhilarating, so upsetting, and so unexpectedly powerful, we turned it into a cover story for the magazine. On a Friday in late April, we were about an hour away from sending this story to the printer when I got an email. According to the email, Lee had won all five matches--and all against top competition--since his loss to AlphaGo. Even as it surpasses human talents, AI can also pull humans to new heights--a theme that ran through our magazine story.

The talks at the Deep Learning School on September 24/25, 2016 were amazing. I clipped out individual talks from the full live streams and provided links to each below in case that's useful for people who want to watch specific talks several times (like I do). Please check out the official website (http://www.bayareadlschool.org) Having read, watched, and presented deep learning material over the past few years, I have to say that this is one of the best collection of introductory deep learning talks I've yet encountered. Full Day Live Streams: Day 1: https://youtu.be/eyovmAtoUx0