In this Meetup, we learn the most fundamental yet important concepts in deep learning deeply. All of these concepts are used to build new models and improve the existing ones and every deep learning enthusiast should 100% fully understand these concepts. So the following concepts are discussed. All the concepts have the theory, math and python implementations for deep understanding. This Meetup is especially for the people who are interested in research so I presume that the participant have good understanding of deep learning, (may not have deep).

FPGAs or GPUs, that is the question. Since the popularity of using machine learning algorithms to extract and process the information from raw data, it has been a race between FPGA and GPU vendors to offer a HW platform that runs computationally intensive machine learning algorithms fast and efficiently. As Deep Learning has driven most of the advanced machine learning applications, it is regarded as the main comparison point. Even though GPU vendors have aggressively positioned their hardware as the most efficient platform for this new era, FPGAs have shown a great improvement in both power consumption and performance in Deep Neural Networks (DNNs) applications, which offer high accuracies for important image classification tasks and are therefore becoming widely adopted [1]. As there are various tradeoffs to consider, it is hard to answer with just a "Yes" or "No".

Artificial Intelligence these days has become a new key driver of economic growth. It is a significant field in technology right now. While several countries are racing towards AI supremacy, Canada is attracting the world's tech giants that are pouring mammoth amounts in the region. The country is currently in the midst of the AI boom as companies like Microsoft, Facebook, Google, Huawei, among others are spending huge capital on research hubs in Quebec, Ontario and Alberta. Canada is a world research leader and home to extraordinary AI-driven businesses, and has played a vital role in the advancement of AI.

Southwest Research Institute, a leading innovator of machine learning technologies, has developed a motion prediction system that enhances pedestrian detection for automated vehicles. The computer vision tool uses a novel deep learning algorithm to predict motion by observing real-time biomechanical movements with the pelvic area being a key indicator for changes. "For instance, if a pedestrian is walking west, the system can predict if that person will suddenly turn south," said SwRI's Samuel E. Slocum, a senior research analyst who led the internally funded project. "As the push for automated vehicles accelerates, this research offers several important safety features to help protect pedestrians." Recent accidents involving automated vehicles have heightened the call for improved detection of pedestrians and other moving obstacles.

This article is part of our reviews of AI research papers, a series of posts that explore the latest findings in artificial intelligence. Fighting fake news has become a growing problem in the past few years, and one that begs for a solution involving artificial intelligence. Verifying the near-infinite amount of content being generated on news websites, video streaming services, blogs, social media, etc. is virtually impossible There has been a push to use machine learning in the moderation of online content, but those efforts have only had modest success in finding spam and removing adult content, and to a much lesser extent detecting hate speech. Fighting fake news is a much more complicated challenge. But they have limited reach.

Deep learning has achieved human-level performance on reading radiology scans, describing images with idiomatic sentences, playing complex games, and more. Deep learning is however compute-intensive, and in the development of deep learning frameworks something of a trade-off has emerged: increasing usability tends to negatively affect speed, and vice versa. Popular frameworks Caffe, Tensorflow and Theano provide quick computing performance but at the cost of ease of use and flexibility. Then there's PyTorch, developed primarily by Facebook AI and introduced in 2016. A new paper from original PyTorch developers Adam Paszke, Sam Gross, Soumith Chintala, Gregory Chanan and 17 other researchers explores the inspiration behind the library, and makes the case for its unique marriage of speed and usability.

Deep learning has the potential to transform health care; however, substantial expertise is required to train such models. We sought to evaluate the utility of automated deep learning software to develop medical image diagnostic classifiers by health-care professionals with no coding--and no deep learning--expertise.

In it you will find popular algorithms and architectural solutions that are intuitive to any novice developer. The book covers important topics: from learning neural networks, language processing, feedback neural networks, to mathematical prerequisites and the history of artificial intelligence.

Molecular dynamics simulations use statistical mechanics at the atomistic scale to enable both the elucidation of fundamental mechanisms and the engineering of matter for desired tasks. The behavior of molecular systems at the microscale is typically simulated with differential equations parameterized by a Hamiltonian, or energy function. The Hamiltonian describes the state of the system and its interactions with the environment. In order to derive predictive microscopic models, one wishes to infer a molecular Hamiltonian that agrees with observed macroscopic quantities. From the perspective of engineering, one wishes to control the Hamiltonian to achieve desired simulation outcomes and structures, as in self-assembly and optical control, to then realize systems with the desired Hamiltonian in the lab. In both cases, the goal is to modify the Hamiltonian such that emergent properties of the simulated system match a given target. We demonstrate how this can be achieved using differentiable simulations where bulk target observables and simulation outcomes can be analytically differentiated with respect to Hamiltonians, opening up new routes for parameterizing Hamiltonians to infer macroscopic models and develop control protocols.

How perception and reasoning arise from neuronal network activity is poorly understood. This is reflected in the fundamental limitations of connectionist artificial intelligence, typified by deep neural networks trained via gradient-based optimization. Despite success on many tasks, such networks remain unexplainable black boxes incapable of symbolic reasoning and concept generalization. Here we show that a simple set of biologically consistent organizing principles confer these capabilities to neuronal networks. To demonstrate, we implement these principles in a novel machine learning algorithm, based on concept construction instead of optimization, to design deep neural networks that reason with explainable neuron activity. On a range of tasks including NP-hard problems, their reasoning capabilities grant additional cognitive functions, like deliberating through self-analysis, tolerating adversarial attacks, and learning transferable rules from simple examples to solve problems of unencountered complexity. The networks also naturally display properties of biological nervous systems inherently absent in current deep neural networks, including sparsity, modularity, and both distributed and localized firing patterns. Because they do not sacrifice performance, compactness, or training time on standard learning tasks, these networks provide a new black-box-free approach to artificial intelligence. They likewise serve as a quantitative framework to understand the emergence of cognition from neuronal networks.