Twenty years ago, open source was merely a new buzz phrase. Some scoffed at it, many misunderstood it, and only a small subset of people believed it could change the world. Today, open source software lies at the heart of the most exciting technology developments. The term may go back to the late '90s, but open sharing of software source code dates back even further to the '50s, when mainframe companies shared operating system source code with others. The movement rapidly gathered force with the success of the World Wide Web and now drives a significant portion of the economy.Open source software has touched everything from word processing applications to databases and has recently enjoyed success in a major growth area: the Internet of Things (IoT).

If you love Andrew Ng's first Coursera course on machine learning as much as I do, you were equally hyped when you heard that deeplearning.ai Since everybody's on a tight schedule, let's try the impossible and finish a course that is laid out to last one month in one week. Let's not rush through though, but actually understand the material. And of course, we'll do it while continuing our 40h/week job. What are the advantages of finishing the course quickly you ask?

Chad Juliano is a senior solutions architect for Kinetica. Machine learning is undergoing a revolution because of new technologies and methods. Machine learning is a process of using a program to develop capabilities--like the ability to tell spam from desirable email--by analyzing data instead of programming the exact steps, freeing the user from needing to make every decision about how the algorithm functions. Machine learning is a powerful tool, not only because over a million people focus on tedious programming steps every day, but also because it sometimes finds better solutions than humans engaged in manual effort. Machine learning has applications in most industries, where it presents a great opportunity to improve upon existing processes.

The buzzword right now is artificial intelligence. Within artificial intelligence, the sub-sector that is gaining the most traction is deep learning. You hear it everywhere and the talk is that it will disrupt all major industries. But, what really is Deep Learning? Deep learning is a method of artificial intelligence which is used to help machines do something that is natural to humans- think and take decisions logically.

Neural networks and deep learning is a great website that goes step by step into neural network architectures, loss functions used etc. It should be very easy to read if you have a bit of math background. CS231N lecture notes from here: CS231n Convolutional Neural Networks for Visual Recognition (notes), and CS231n: Convolutional Neural Networks for Visual Recognition (lecture slides) Once you are familiar with the basics and the terminology (which is not much I should say), you can read tutorials of deep learning software such as Caffe (Caffe Deep Learning Framework, read through the Example section), and Theano (Deep Learning Tutorials). If you are feeling adventurous, you can also look through the proceedings and submitted papers for conferences such as ICLR and NIPS. Deep learning being a fast growing area, once you understand the basics it should not be much of an effort to understand what is being talked about in most of the papers.

Neural networks exists in all shapes and sizes, and are often characterized by their input and output data type. For instance, image classifiers are built with Convolutional Neural Networks. They take images as inputs, and output a probability distribution of the classes. Autoencoders (AE) are a family of neural networks for which the input is the same as the output*. They work by compressing the input into a latent-space representation, and then reconstructing the output from this representation. A really popular use for autoencoders is to apply them to images.

Tensor Comprehensions (TC) is a tool that lowers the barrier for writing high-performance code. It generates GPU code from a simple high-level language and autotunes the code for specific input sizes. We highly recommend reading the Tensor Comprehensions blogpost first. If you ran into any of the following scenarios, TC is a useful tool for you. Tensor Comprehensions are seamless to use in PyTorch, interoperating with PyTorch Tensors and nn Variables.

Who is leading in AI research among big players like IBM, Google, Facebook, Apple and Microsoft? Firstly, my response contains some bias, because I work at Google Brain and I really like it there. My opinions are my own, and I do not speak for the rest of my colleagues or Alphabet as a whole. I rank "leaders in AI research" among tech companies as follows: I would say Deepmind is probably #1 right now, in terms of AI research. Their publications are highly respected within the research community, and span a myriad of topics such as Deep Reinforcement Learning, Bayesian Neural Nets, Robotics, transfer learning, and others.

During the Windows Developer Day event yesterday, Microsoft revealed the Windows AI platform which will allow developers to build intelligent apps on Windows 10. One of the highlighted features of WinML APIs is the support for pre-trained machine learning models. Windows ML will efficiently use hardware for any given artificial intelligence (AI) workload and intelligently distributes work across multiple hardware types including CPU, GPU and Intel's Vision Processing Units (VPU). The Intel VPU is a purpose-built chip for accelerating AI workloads on client devices. Myriad X is world's first system-on-chip (SOC) shipping with a dedicated Neural Compute Engine for accelerating deep learning inferences at the edge.

Artificial intelligence is a hot topic right now--but whether or not it is going live up to what some are calling the new healthcare reform is still up for discussion. Mayo Clinic Chief Information officer Christopher Ross and PricewaterhouseCooper Managing Director James Golden, tackled questions about the future of AI at HIMSS18. "In some ways [AI] could not be more hyped than it is. There is an enormous expectation for this stuff," Golden said. AI is already proving to be helpful in studies. The pair cited a research study out of Stanford where researchers worked with Google DeepMind to identify cancerous spots in medical images.