I recently started a new newsletter focus on AI education. TheSequence is a no-BS( meaning no hype, no news etc) AI-focused newsletter that takes 5 minutes to read. The goal is to keep you up to date with machine learning projects, research papers and concepts. Deep reinforcement learning(DRL) is one of the fastest areas of research in the deep learning space. Responsible for some of the top milestones in the recent years of AI such as AlphaGo, Dota2 Five or Alpha Star, DRL seems to be the discipline that approximates human intelligence the closest.

Machine Learning Crash Course for Executives - by Deloitte Data Analytics, Data Analysis, Data Science, Big Data, Artificial Intelligence, Deep Learning, Neural Networks, AI New What you'll learn Description Deloitte's crash course on AI, Machine Learning and Deep Learning Programme is provides short, one stop learning opportunity for everybody that has an interest to understand AI, Machine Learning and Deep Learning beyond the buzzwords. After completing this course, participants will be able to prioritise, lead and manage AI initiatives.

What if I tell you that an entire short sci-fi film has been written by an AI bot built on LSTM recurrent neural network, and it has even received positive reviews and critics, Surprised?! well, I'm sure you are because that's what I felt watching "Sunspring" for the first time, I mean I know it can't be compared to Steven Spielberg's or Alex Garland's screenwriting quality but no wonder if in the next few years AI bots will compete against them in the Academy Awards. Indeed, we should no longer be surprised by what artificial intelligence is capable of in order to flip our world upside down, making it a better, "easier", and most comfortable place to live in. From all of the AI subfields, in my opinion, NLP has the coolest and most exciting applications. One of them is text generation that we should have a deep look at it. In this article, I will briefly explain how RNN and LSTM work and how we can generate texts using LSTM in Python.

In a previous post, we had a look at the top python libraries for data science, data visualization, and machine learning. This time, we look at the top libraries for deep learning, natural language processing, and computer vision. These categories really don't need any further clarification. This separation and classification is arbitrary, in some instances more than others, but we have done our best to group tools together by intended use case, hoping this is most useful for readers. Clearly not all NLP and CV work these days is performed using deep learning techniques, but as the trends move toward such techniques for state of the art results, we stand by this otherwise arbitrary categorization logic.

EPFL researchers are at the forefront of developing some of the cutting-edge technology for the European Space Agency's first mission to remove space debris from orbit. How do you measure the pose – that is the 3D rotation and 3D translation – of a piece of space junk so that a grasping satellite can capture it in real time in order to successfully remove it from Earth's orbit? What role will deep learning algorithms play? And, what is real time in space? These are some of the questions being tackled in a ground-breaking project, led by ClearSpace, a spin-off from the EPFL Space Center (eSpace), to develop technologies to capture and deorbit space debris. With more than 34,000 pieces of junk orbiting around the Earth, their removal is becoming a matter of safety.

Researchers in China have developed a deep-learning-based algorithm that could help radiologists detect potentially life-threatening cerebral aneurysms on CT angiography images. Cerebral aneurysms are weak spots in blood vessels in the brain, which can balloon out and fill with blood. If such a bulging aneurysm leaks or ruptures, it can cause serious symptoms and sometimes be fatal. The risk of rupture depends on the size, shape and location of the aneurysm, making detection and characterization of cerebral aneurysms vital. CT angiography, which uses X-ray CT to visualize blood vessels following injection of contrast into the bloodstream, is usually the first-line imaging exam for detecting cerebral aneurysms.

So, you've decided you want to purchase a machine dedicated to training machine learning models. Or, rather, you work in an organization where the buzzwords of this guide are constantly thrown around and you simply want to know a bit more about what they mean. This isn't a terribly simple topic, so I've decided to write this guide. You can discuss those terms from various angles, and this guide will tackle one of them. I'm Nir Ben-Zvi, a Deep Learning researcher and a hardware enthusiast from early middle school days, where I would tear computers apart while friends were playing basketball (tried that too, went back to hardware pretty fast). In the past few years I got to consult some friends on building deep learning machines for companies of various sizes, and ultimately decided to put that knowledge into this guide. Today I work for trigo, doing some Deep Learning and Data. A lot of the knowledge for this guide came from the decisions made towards building our first deep learning machines. Some parts of this guide are kept despite being way out of date.

The researchers from Massachusetts Institute of Technology (MIT) have developed a system that could bring deep learning neural networks to new -- and much smaller -- places, like the tiny computer chips in wearable medical devices, household appliances, and the 250 billion other objects that constitute the IoT. The system, called MCUNet, designs compact neural networks that deliver unprecedented speed and accuracy for deep learning on IoT devices, despite limited memory and processing power. The technology could facilitate the expansion of the IoT universe while saving energy and improving data security. MCUNet has two components needed for "tiny deep learning" -- the operation of neural networks on microcontrollers. One component is TinyEngine, an inference engine that directs resource management, akin to an operating system.

Modern machine learning research has demonstrated remarkable achievements. Today, we can train machines to detect objects in images, extract meaning from text, stop spam emails, drive cars, discover new drug candidates, and beat top players in Chess, Go, and countless other games. A lot of these advancements are powered by deep learning, in particular deep neural networks. Yet, the theory behind deep neural networks remains poorly understood. Sure, we understand the math of what individual neurons are doing, but we're lacking a mathematical theory of the emergent behavior of entire network.

BEGIN ARTICLE PREVIEW: By Jessica Kent October 29, 2020 – A deep learning tool can offer more information about SARS-CoV-2 proteins to accelerate COVID-19 drug discovery, according to a study published in Chemical Science. For more coronavirus updates, visit our resource page, updated twice daily by Xtelligent Healthcare Media. Researchers from Michigan State University (MSU) Foundation repurposed deep learning models to focus on a specific SARS-CoV-2 protein called its main protease. The main protease is a cog in the virus’s protein machinery that’s critical to how the pathogen makes copies of itself. Drugs that disable the main protease could stop the virus from replicating. Dig Deeper The main protease is distinct from all known human proteases, which isn’t always the case. Drugs that attack the viral protease are therefore less likely to disrupt people’s natural biochemistry. The SARS-CoV-2 main protease is also almost identic