We present the Video Ladder Network (VLN) for efficiently generating future video frames. VLN is a neural encoder-decoder model augmented at all layers by both recurrent and feedforward lateral connections. At each layer, these connections form a lateral recurrent residual block, where the feedforward connection represents a skip connection and the recurrent connection represents the residual. Thanks to the recurrent connections, the decoder can exploit temporal summaries generated from all layers of the encoder. This way, the top layer is relieved from the pressure of modeling lower-level spatial and temporal details. Furthermore, we extend the basic version of VLN to incorporate ResNet-style residual blocks in the encoder and decoder, which help improving the prediction results. VLN is trained in self-supervised regime on the Moving MNIST dataset, achieving competitive results while having very simple structure and providing fast inference.

Year after year, data science techniques mature and deliver outstanding results with successful implementations. There have been various developments in the field of data science and related technologies. We saw growth in Data Scientists from all fields of profession and study. Most consulting enterprises established Analytics and Data Science as one of their key offerings, with many niche startups mushrooming to grab a space in this area. The advantage that we have is the increased contribution of the Data Scientists to open source development communities, laying out new thought processes in the analytics industry and bringing out innovative ways to solve business problems.

There is a lot of confusion these days about Artificial Intelligence (AI), Machine Learning (ML) and Deep Learning (DL). There certainly is a massive uptick of articles about AI being a competitive game changer and that enterprises should begin to seriously explore the opportunities. The distinction between AI, ML and DL are very clear to practitioners in these fields. AI is the all encompassing umbrella that covers everything from Good Old Fashion AI (GOFAI) all the way to connectionist architectures like Deep Learning. ML is a sub-field of AI that covers anything that has to do with the study of learning algorithms by training with data.

The Holographic Principle is a theory (see: Thin Sheet of Reality) that explains how quantum mechanics and gravity interact to construct the reality that we are in. The motivations for this theory comes from the paradox that Hawking created when he theorized that black holes would emanate energy. The fundamental concept that had been violated by Hawking's theory was that information was destroyed. As a consequence of this paradox, through several decades of research and experimentation, physicists have brought forth a unified theory of the universe that is based on information theoretic principles. The entire universe is a projection of a hologram.

Neuroscience and artificial intelligence experts from Rice University and Baylor College of Medicine have taken inspiration from the human brain in creating a new "deep learning" method that enables computers to learn about the visual world largely on their own, much as human babies do. In tests, the group's "deep rendering mixture model" largely taught itself how to distinguish handwritten digits using a standard dataset of 10,000 digits written by federal employees and high school students. In results presented this month at the Neural Information Processing Systems (NIPS) conference in Barcelona, Spain, the researchers described how they trained their algorithm by giving it just 10 correct examples of each handwritten digit between zero and nine and then presenting it with several thousand more examples that it used to further teach itself. In tests, the algorithm was more accurate at correctly distinguishing handwritten digits than almost all previous algorithms that were trained with thousands of correct examples of each digit. "In deep-learning parlance, our system uses a method known as semisupervised learning," said lead researcher Ankit Patel, an assistant professor with joint appointments in neuroscience at Baylor and electrical and computer engineering at Rice. "The most successful efforts in this area have used a different technique called supervised learning, where the machine is trained with thousands of examples: This is a one. "Humans don't learn that way," Patel said. "When babies learn to see during their first year, they get very little input about what things are.

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.

GPU technology has been around for decades, but only recently has it gained traction among enterprises. It was traditionally used to enhance computer graphics, as the name suggests. But as deep learning and artificial intelligence have grown in prominence, the need for fast, parallel computation to train models has increased. "A couple years ago, we wouldn't be looking at special hardware for this," said Adrian Bowles, founder of analyst firm STORM Insights Inc. in Boston. "But with [deep learning], you have a lot of parallel activities going on, and GPU-based tools are going to give you more cores."

Over the past several years, Jack Gallant's neuroscience lab has produced a string of papers that sound absurd. In 2011, the lab showed it was possible to recreate movie clips just from observing the brain activity of people watching movies. Using a computer to regenerate the images of a film just by scanning the brain of a person watching one is, in a sense, mind reading. Similarly, in 2015, Gallant's team of scientists predicted which famous paintings people were picturing in their minds by observing the activity of their brains. This year, the team announced in the journal Nature that they had created an "atlas" of where 10,000-plus individual words reside in the brain -- just by having study participants listen to podcasts. How did they do all this?

Last March, Google's computers roundly beat the world-class Go champion Lee Sedol, marking a milestone in artificial intelligence. The winning computer program, created by researchers at Google DeepMind in London, used an artificial neural network that took advantage of what's known as deep learning, a strategy by which neural networks involving many layers of processing are configured in an automated fashion to solve the problem at hand. Unknown to the public at the time was that Google had an ace up its sleeve. You see, the computers Google used to defeat Sedol contained special-purpose hardware--a computer card Google calls its Tensor Processing Unit. Norm Jouppi, a hardware engineer at Google, announced the existence of the Tensor Processing Unit two months after the Go match, explaining in a blog post that Google had been outfitting its data centers with these new accelerator cards for more than a year.

We have seen the likes of Google, Facebook, Amazon and many more come out in open and acknowledge the impact machine learning and deep learning had on their business. Last week, I published top videos on deep learning from 2016. I was blown away by the response. I could understand the response to some degree -- I found these videos extremely helpful. So, I decided to do a similar article on top videos on machine learning from 2016.