Google Colaboratory is a free online cloud-based Jupyter notebook environment that allows us to train our machine learning and deep learning models on CPUs, GPUs, and TPUs. Here's what I truly love about Colab. It does not matter which computer you have, what it's configuration is, and how ancient it might be. You can still use Google Colab! All you need is a Google account and a web browser.

Are you ready to start your new AI project? In the below map, you'll find a curated list of the most advanced and innovative open source projects to be considered in you data science initiative for 2020. Drop a comment below or submit a pull-request here, if you believe a relevant project was left behind. AllenNLP – An open-source NLP research library, built on PyTorch. Zeppelin – Web-based notebook that enables data-driven, interactive data analytics.

With more board configurations than there are atoms in the universe, the ancient Chinese game of Go has long been considered a grand challenge for artificial intelligence. On March 9, 2016, the worlds of Go and artificial intelligence collided in South Korea for an extraordinary best-of-five-game competition, coined The DeepMind Challenge Match. Hundreds of millions of people around the world watched as a legendary Go master took on an unproven AI challenger for the first time in history. Directed by Greg Kohs with an original score by Academy Award nominee, Hauschka, AlphaGo chronicles a journey from the halls of Oxford, through the backstreets of Bordeaux, past the coding terminals of DeepMind in London, and ultimately, to the seven-day tournament in Seoul. As the drama unfolds, more questions emerge: What can artificial intelligence reveal about a 3000-year-old game?

During the past decade, deep learning has seen groundbreaking developments in the field of AI (Artificial Intelligence). But what is this technology? And why is it so important? Well, let's first get a definition of deep learning. Here's how Kalyan Kumar, who is the Corporate Vice President & Chief Technology Officer of IT Services at HCL Technologies, describes it: "Have you ever wondered how our brain can recognize the face of a friend whom you had met years ago or can recognize the voice of your mother among so many other voices in a crowded marketplace or how our brain can learn, plan and execute complex day-to-day activities? The human brain has around 100 billion cells called neurons. These build massively parallel and distributed networks, through which we learn and carry out complex activities. Inspired from these biological neural networks, scientists started building artificial neural networks so that computers could eventually learn and exhibit intelligence like humans."

Right now, a minivan with no one behind the steering wheel is driving through a suburb of Phoenix, Arizona. And while that may seem alarming, the company that built the "brain" powering the car's autonomy wants to assure you that it's totally safe. Waymo, the self-driving unit of Alphabet, is the only company in the world to have fully driverless vehicles on public roads today. That was made possible by a sophisticated set of neural networks powered by machine learning about which very is little is known -- until now. For the first time, Waymo is lifting the curtain on what is arguably the most important (and most difficult-to-understand) piece of its technology stack. The company, which is ahead in the self-driving car race by most metrics, confidently asserts that its cars have the most advanced brains on the road today. Anyone can buy a bunch of cameras and LIDAR sensors, slap them on a car, and call it autonomous. But training a self-driving car to behave like a human driver, or, more importantly, to drive better than a human, is on the bleeding edge of artificial intelligence research.

Today machines with artificial intelligence (AI) are becoming more prevalent in society. Across many fields, AI has taken over numerous tasks that humans used to do earlier. As the reference is to human intelligence, artificial intelligence is being modified into what humans can do. However, the technology has not yet matched the level of utmost wisdom possessed by humans and it seems like it is not going to achieve the milestone any time sooner. To replace human beings at most jobs, machines need to exhibit what we intuitively call "common sense".

AI has gotten something of a bad rap in recent years, but the Covid-19 pandemic illustrates how AI can do a world of good in the race to find a vaccine. AI is playing two important supporting roles in this quest: suggesting components of a vaccine by understanding viral protein structures, and helping medical researchers scour tens of thousands of relevant research papers at an unprecedented pace. Over the last few weeks, teams at the Allen Institute for AI, Google DeepMind, and elsewhere have created AI tools, shared datasets and research results, and shared them freely with the global scientific community. Oren Etzioni is the CEO of the nonprofit Allen Institute for AI, and a professor of computer science at the University of Washington. Nicole DeCario is Senior Assistant to the CEO at the Allen Institute for AI. Vaccines imitate an infection, causing the body to produce defensive white-blood cells and antigens.

An Australian-German collaboration has demonstrated fully-autonomous SPM operation, applying artificial intelligence and deep learning to remove the need for constant human supervision. The new system, dubbed DeepSPM, bridges the gap between nanoscience, automation and artificial intelligence (AI), and firmly establishes the use of machine learning for experimental scientific research. "Optimising SPM data acquisition can be very tedious. This optimization process is usually performed by the human experimentalist, and is rarely reported," says FLEET Chief Investigator Dr. Agustin Schiffrin (Monash University). "Our new AI-driven system can operate and acquire optimal SPM data autonomously, for multiple straight days, and without any human supervision."

Researchers at Google have open-sourced a new framework that can scale up artificial intelligence model training across thousands of machines. It's a promising development because it should enable AI algorithm training to be performed at millions of frames per second while reducing the costs of doing so by as much as 80%, Google noted in a research paper. That kind of reduction could help to level the playing field a bit for startups that previously haven't been able to compete with major players such as Google in AI. Indeed, the cost of training sophisticated machine learning models in the cloud is surprisingly expensive. One recent report by Synced found that the University of Washington racked up $25,000 in costs to train its Grover model, which is used to detect and generate fake news.

Mazenet's Artificial Intelligence & Deep Learning with TensorFlow is for aspiring Data Scientists who want to have rich hands-on training in various deep learning projects. Deep Learning is an AI function that emulates the human brain in creating patterns and processing information for decision making. Learning NLP or Natural Language Processing identifies and separates words, builds fake news classifiers, and extracts topics in a text. The basic libraries like the NLTK use deep learning to solve common NLP issues. Your employee can get the foundation to process and parse text with Python learning.