If you were miffed about Britain's National Health Service (NHS) giving your sensitive data away to Google's DeepMind, how you respond to today's news is probably a crapshoot. The NHS has announced that it will begin anonymizing said data that's been used to analyze blood test results and to detect risk of acute kidney injuries and other ailments. To be clear, these are separate events (the data use and today's announcement), but one led to the other. In 2016, the NHS and DeepMind caught their fair share of criticism over how data was shared with implied -- not explicit -- consent from 1.6 million patients. "The new de-identification process (known as De-ID) will protect patient privacy by de-identifying a person's records in a consistent way," according to a statement from the NHS.

It's another win for Amazon Web Services in the ongoing cloud wars, with Formula 1 signing up as an AWS customer to bolster its race strategies, data tracking systems, and digital broadcasts. The auto racing organization said it's migrating the vast majority of its on-premises infrastructure to AWS, and standardizing on on AWS' machine learning and data analytics services as part of its cloud and digital transformation strategy. Formula 1 is also using SageMaker, Amazon's end-to-end machine learning service, to train deep learning models with more than 60 years of historical race data stored in Amazon DynamoDB and Glacier. The data will help the company glean race performance statistics that can be used to predict outcomes. AWS' serverless computing service Lamda is also on tap to help uncover race metrics, along with AWS Elemental Media Services, which will power Formula 1's video asset workflows.

Formula 1 will work with AWS to enhance its race strategies, data tracking systems, and digital broadcasts through a wide variety of AWS services -- including Amazon SageMaker, a fully managed machine learning service that enables everyday developers and scientists to easily build and deploy machine learning models, AWS Lambda, AWS's pioneering event-driven serverless computing service, and AWS analytics services -- to uncover never-before-seen metrics that will change the way fans and teams enjoy, experience, and participate in racing. Formula 1 has also selected AWS Elemental Media Services to power its video asset workflows, enhancing the viewing experience for its 500 million plus fans worldwide. Using Amazon SageMaker, Formula 1's data scientists are training deep learning models with more than 65 years of historical race data, stored in both Amazon DynamoDB and Amazon Glacier. With this information, Formula 1 can extract critical race performance statistics to make race predictions and give fans insight into the split-second decisions and strategies adopted by teams and drivers. For example, Formula 1 data scientists can predict when the window of opportunity is opening and closing for teams to pit their cars for maximum advantage, as well as determine the best timing for changing tires.

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Artificial intelligence is the future of the technological football and now some of the world's most advanced artificial intelligence and machine learning can be developed in hours on a personal computer with open-source frameworks, AI will become more pervasive and generalized than it is. Software will get smarter and more multi-capable as the latest in natural language processing, computer vision, recommending systems and more became much as easy to develop as a CMS. Artificial intelligence in now a days plays a significant role in our day to day life; from our smartphones to other electronic devices, technology given us the game changing opportunities that will assist you in your work as well as in your lifestyle too. Few of the popular si-fi movies like Terminator, Transformers and latest in this era was Avtar-in 2009 are the best examples of Artificial Intelligence, Machine Learning & Deep Learning. The theory & development of computer systems that able to perform tasks that normally require human intelligence, such as visual perception, speech recognition, decision-making and translation between languages.

In the last few years, we've seen an amazing rise of software tools for Machine Learning (ML). Google's TensorFlow is currently "the king" of all ML frameworks, based on GitHub stars and Google searches. What makes TensorFlow so special and successful? What are its downsides compared to other competitors, and will it manage to stay on top? To explain this, we'll have to go way back to the basics.

It happens all the time. One minute, you're Googling, "what is a differential?" The next minute, you're looking at toasters on Amazon or exercise equipment on eBay, and all you see everywhere are pictures of axle girdles and Trac-Lot rebuild kits. If the internet is going to flood your senses with ads anyway, they might as well be ads for things you're interested in. But how do they do it?

Hasktorch is a library for tensors and neural networks in Haskell. It is an independent open source community project which leverages the core C libraries shared by Torch and PyTorch. Note that this project is in early development and should only be used by contributing developers. Expect substantial changes to the library API as it evolves. Contributions and PRs are welcome (see details below).

Major tech companies have actively reoriented themselves around AI and machine learning: Google is now "AI-first," Uber has ML running through its veins and internal AI research labs keep popping up. They're pouring resources and attention into convincing the world that the machine intelligence revolution is arriving now. They tout deep learning, in particular, as the breakthrough driving this transformation and powering new self-driving cars, virtual assistants and more. Despite this hype around the state of the art, the state of the practice is less futuristic. Software engineers and data scientists working with machine learning still use many of the same algorithms and engineering tools they did years ago.

This is a follow up to the Introduction to Machine Learning video.