About this course: If you want to break into cutting-edge AI, this course will help you do so. Deep learning engineers are highly sought after, and mastering deep learning will give you numerous new career opportunities. Deep learning is also a new "superpower" that will let you build AI systems that just weren't possible a few years ago. In this course, you will learn the foundations of deep learning. When you finish this class, you will: - Understand the major technology trends driving Deep Learning - Be able to build, train and apply fully connected deep neural networks - Know how to implement efficient (vectorized) neural networks - Understand the key parameters in a neural network's architecture This course also teaches you how Deep Learning actually works, rather than presenting only a cursory or surface-level description.

The culmination of our Deep Learning blogs, we're very proud to announce our new ebook, Defining Deep Learning And How It Applies to Marketing. It's a little longer than our usual guides, but this ebook answers everything that you've been asking about deep learning – as well as some things you haven't thought to ask just yet, but you'll be oh so pleased we answered them anyway! We don't want to give it all away, but we cover: This is deeper than we've ever gone before.

Scale of data and scale of computation infrastructures together enable the current deep learning renaissance. However, training large-scale deep architectures demands both algorithmic improvement and careful system configuration. In this paper, we focus on employing the system approach to speed up large-scale training. Via lessons learned from our routine benchmarking effort, we first identify bottlenecks and overheads that hinter data parallelism. We then devise guidelines that help practitioners to configure an effective system and fine-tune parameters to achieve desired speedup. Specifically, we develop a procedure for setting minibatch size and choosing computation algorithms. We also derive lemmas for determining the quantity of key components such as the number of GPUs and parameter servers. Experiments and examples show that these guidelines help effectively speed up large-scale deep learning training.

This paper addresses the task of set prediction using deep learning. This is important because the output of many computer vision tasks, including image tagging and object detection, are naturally expressed as sets of entities rather than vectors. As opposed to a vector, the size of a set is not fixed in advance, and it is invariant to the ordering of entities within it. We define a likelihood for a set distribution and learn its parameters using a deep neural network. We also derive a loss for predicting a discrete distribution corresponding to set cardinality. Set prediction is demonstrated on the problem of multi-class image classification. Moreover, we show that the proposed cardinality loss can also trivially be applied to the tasks of object counting and pedestrian detection. Our approach outperforms existing methods in all three cases on standard datasets.

Starcraft II has been a target for Alphabet's DeepMind AI research for a while now – the UK AI company took on Blizzard's sci-fi strategy game starting last year, and announced plans to create an open AI research environment based on the game to make it possible for others to contribute to the effort of creating a virtual agent who can best the top human StarCraft players in the world. Now, DeepMind and Blizzard are opening the doors to that environment, with new tools including a machine learning API, a large game replay dataset, an open source DeepMind toolset and more. The new release of the StarCraft II API on the Blizzard side includes a Linux package made to be able to run in the cloud, as well as support for Windows and Mac. It also has support for offline AI vs. AI matches, and those anonymized game replays from actual human players for training up agents, which is starting out at 65,000 complete matches, and will grow to over 500,000 over the course of the next few weeks. StarCraft II is such a useful environment for AI research basically because of how complex and varied the games can be, with multiple open routes to victory for each individual match.

The Keras library provides a way to calculate and report on a suite of standard metrics when training deep learning models. In addition to offering standard metrics for classification and regression problems, Keras also allows you to define and report on your own custom metrics when training deep learning models. This is particularly useful if you want to keep track of a performance measure that better captures the skill of your model during training. In this tutorial, you will discover how to use the built-in metrics and how to define and use your own metrics when training deep learning models in Keras. Metrics and How to Use Custom Metrics for Deep Learning with Keras in Python Photo by Indi Samarajiva, some rights reserved.

Is PyTorch better than TensorFlow for general use cases? I use PyTorch at home and TensorFlow at work. The other way around would be also great. There are two "general use cases" for either one. Each of them has its own challenges, but if you have only training (students and researchers) or mostly inference and implementation (developers), you start focusing on different things. TensorFlow is built around a concept of Static Computational Graph (SCG).

The University of British Columbia created a computer program called DeepLoco that uses deep learning to teach itself to walk, run, and even dribble a soccer ball. It could help video games become much more realistic and even aid robots in the real world.

How can artificial neural nets help in understanding our brain's neural net? On the weekend of March 24–26, YCombinator-backed startup DeepGram hosted a deep learning hackathon. The weekend-long event included speakers and judges from Google Brain, NVIDIA, and Baidu. My colleague, Dr. Matt Rubashkin, also participated and you can read about his project here. I chose to work on one of the datasets suggested by DeepGram: EEG readings from a Stanford research project that predicted which category of images their test subjects were viewing using linear discriminant analysis.

Novice here: I really want to try this Starcraft API but I don't know how to start. I believe this uses more reinforcement learning and agent-based models (which honestly I am not familiar with yet) What are good papers to get started on this?