The Gluon API specification is an effort to improve speed, flexibility, and accessibility of deep learning technology for all developers, regardless of their deep learning framework of choice. The Gluon API offers a flexible interface that simplifies the process of prototyping, building, and training deep learning models without sacrificing training speed. The Gluon specification has already been implemented in Apache MXNet, so you can start using the Gluon interface by following these easy steps for installing the latest master version of MXNet. We recommend using Python version 3.3 or greater and implementing this example using a Jupyter notebook. Setup of Jupyter is included in the MXNet installation instructions.

In the video presentation below (courtesy of Yandex) – "Deep Learning: Theory, Algorithms, and Applications" – Naftali Tishby, a computer scientist and neuroscientist from the Hebrew University of Jerusalem, provides evidence in support of a new theory explaining how deep learning works. Tishby argues that deep neural networks learn according to a procedure called the "information bottleneck," which he and two collaborators first described in purely theoretical terms in 1999. The idea is that a network rids noisy input data of extraneous details as if by squeezing the information through a bottleneck, retaining only the features most relevant to general concepts. Striking new computer experiments by Tishby and his student Ravid Shwartz-Ziv reveal how this squeezing procedure happens during deep learning, at least in the cases they studied. The Berlin workshop aims at bringing together leading scientists in deep learning and related areas within machine learning, artificial intelligence, mathematics, statistics, and neuroscience.

TensorFlow is a Python library for fast numerical computing that was designed specifically for machine learning. It was open-sourced by Google with the hope of putting deep learning capabilities in the hands of a lot more researchers and developers around the world. The official tutorials can be somewhat confusing to a beginner, I recommend starting with this series to get what the writer calls the gentlest introduction to Tensorflow. Once installed it provides multiple APIs for training ML models. The higher level APIs built on top of what is called the TensorFlow Core (the lowest level API with most control) are the easiest to learn and should be where you start. It's counterintuitive to include a full TensorFlow tutorial within this post when there already exists countless resources online that do it perfectly well... start with the official one: While TensorFlow is the most popular machine learning library, there're several great alternatives like Torch (used by Facebook), Caffe (deep learning framework by Berkeley AI Research) and many more.

This is a reference implementation of Video Frame Interpolation via Adaptive Separable Convolution [1] using PyTorch. Given two frames, it will make use of adaptive convolution [2] in a separable manner to interpolate the intermediate frame. Should you be making use of our work, please cite our paper [1]. To build the implementation and download the pre-trained networks, run bash install.bash After successfully completing this step, run python run.py to test it.

Any form of classification is supervised and not unsupervised[1][2]. You are probably interested in unsupervised image segmentation, where the algorithm attempts to determine which pixels are related and groups them into certain categories. This can be done by using traditional partitional clustering algorithms, such as K-means/EM[3], or advanced deep learning methods such as convolutional autoencoders[4], bayesian methods[5] and so on. You may read this survey research paper on the evaluation of such techniques - Image segmentation evaluation: A survey of unsupervised methods.

Enroll in the course for free at: https://bigdatauniversity.com/courses... Deep Learning with TensorFlow Introduction The majority of data in the world is unlabeled and unstructured. Shallow neural networks cannot easily capture relevant structure in, for instance, images, sound, and textual data. Deep networks are capable of discovering hidden structures within this type of data. In this TensorFlow course you'll use Google's library to apply deep learning to different data types in order to solve real world problems. Traditional neural networks rely on shallow nets, composed of one input, one hidden layer and one output layer.

NVIDIA GPUs have been on the forefront of accelerated neural network processing and are the de facto standard for accelerated neural network research and development (R&D) plus deep learning training. At the NVIDIA GPU Technology Conference (GTC) in Beijing China earlier this week, the company maneuvered to also become the de facto standard for accelerated neural network inference deployment. At GTC Beijing, NVIDA lined up the major Chinese cloud companies for AI computing: Alibaba Cloud, Baidu Cloud, and Tencent Cloud. At GTC-Beijing, it announced inference designs with Alibaba Cloud, Tencent, Baidu Cloud, JD.com, and iFlytek.

Subscribe to stay notified about part 2 on backpropagation: http://3b1b.co/subscribe Typo correction: At 14:45, the last index on the bias vector is n, when it's supposed to in fact be a k. Thanks for the sharp eyes that caught that! For those who want to learn more, I highly recommend the book by Michael Nielsen introducing neural networks and deep learning: https://goo.gl/Zmczdy There are two neat things about this book.

Neural networks, and particularly deep learning research, have obtained many breakthroughs recently in the field of computer vision and other important fields in computer science. Among many different application, one technology that is currently on the rising is self-driving cars. Everybody has heard of them, all the major company seem to invest heavily on this new-millenium gold rush. AI-powered cars that can take you anywhere while you spend your time, well, not driving. In this post I will show you how to train a neural network to steer autonomously using only images of the road ahead.

It seems like every day brings a new AI more capable than the last. This was recently apparent with AlphaGo--it was pretty great at beating Breakout, then Google got involved and soon it was capable of beating the world's leading Go champion. To do this, AlphaGo uses what is known as'deep reinforcement learning'. For example, in Breakout, it will take raw image frames of the game as it's being played. Whether or not the ball is hitting the bricks in those frames will decide whether or not positive reinforcement is registered.