What is machine learning / ai? How to learn machine learning in practice? Neural Networks (often referred to as deep learning) are particular interesting. But there are a few questions. To answer these questions and give beginners a guide to really understand them, I created this interesting course.

In order to answer some of these questions, the OECD held a conference last week on AI. Government and industry representatives, AI academics and others met to review the state of AI and pose the question of what governments could and should do, in creating policies to take advantage of the benefits of AI whilst minimising the risks. The first thing that became clear is that the focus of discussion was mainly on machine learning and in particular, deep learning. Deep learning software learns to be able to recognise patterns from data. Google, for example, is using it to recognise pets by their faces.

First announced back in September as a Microsoft and Facebook project, Amazon Web Services has now got onboard too. Called Open Neural Network Exchange (ONNX), the platform is an "open source model representation" that makes it possible to combine different components of the AI ecosystem into a single application. This makes it easier to get started on a new service and improves interoperability between frameworks. Developers can focus on creating new original functionality, instead of having to adjust their models to suit each new framework they work on. "ONNX is an open source model representation for interoperability and innovation in the AI ecosystem that Microsoft co-developed," explained Microsoft in its announcement post.

If there are any doubts in regards to the popularity of Keras among the Data Scientist/Engineer community and the mindshare it commands, you just need to look at the support it has been receiving from all major AI and Cloud players. Currently the official Keras release already supports Google's TensorFlow and Microsoft's CNTK deep learning libraries besides supporting other popular libraries like Theano. Last year Amazon Web Services announced its support for Apache MXNet, another powerful Deep Learning library and few weeks ago support for Keras was added to the MXNet's next release candidate. As of now MXNet only seems to support Keras v1.2.2 and not the current Keras release 2.0.5. Although it is possible to deploy Keras models in production with any of the supported backends, developers and solution architects should keep in mind that Keras, by nature of being a high-level API for the different DL frameworks, doesn't yet support tweaking of all underlying parameters offered by the individual libraries.

The result of more than 10 years of research at List, the flexible algorithm dubbed DeepManta combines a native multi-task architecture with enhancements to conventional deep-learning algorithms, allowing the AI system to extract different types and levels of information simultaneously and in real-time. To be demonstrated at CES 2018, DeepManta is able to selectively recognize and extract objects from a video stream, for example to identify vehicles, their type and position and counting them. At CES, global supplier of advanced automotive technology Valeo will partner with List to demonstrate DeepManta's support for autonomous driving. The demonstration setup includes a video stream captured by a stationary camera and displayed live on a screen. Different objects, such as miniature cars, move into the camera's field of view, where the AI algorithm selectively recognizes them, automatically generating visual annotations, labelling the cars with the logo of their make together with model information.

One of the greatest benefits of artificial intelligence (AI) to humankind is its influence on the medical field. "Powered by some of the most sophisticated technology, AI is assisting in improving medical diagnosis," says Anton Jacobs, managing director at African value-added technology distributor, Networks Unlimited. From an AI doctor and chatbot to AI's powerful applications, machine learning and deep learning, a world that used to be all about coding, is transitioning into using computer programming to assist in life-changing health issues such as early cancer detection. A massive advantage is that AI has the power to pool knowledge from the best specialists worldwide and provide it to patients anywhere geographically. "Imagine what this could mean to patients living in rural areas. They'd finally have the same access to knowledge as patients in top medical facilities," adds Jacobsz.

This paper explains why deep learning can generalize well, despite large capacity and possible algorithmic instability, nonrobustness, and sharp minima, effectively addressing an open problem in the literature. Based on our theoretical insight, this paper also proposes a family of new regularization methods. Its simplest member was empirically shown to improve base models and achieve competitive performance on MNIST and CIFAR-10 benchmarks. Moreover, this paper presents both data-dependent and data-independent generalization guarantees with improved convergence rates. Our results suggest several new open areas of research.

Machine learning is more tactical in nature. It imbeds intelligence into business processes to reach decisions more quickly. For example, it can analyze data to learn when to reorder more raw materials based on factors such as inventory, manufacturing productivity or market demand. It can evaluate all of these factors at the same time, much like a human with years of experience predicting when you need to reorder materials -- but can do so in less than a second. The interest in machine learning stems from the number of business applications there are for the technology, and its ability to make AI more practical for enterprises. Deep learning, also known as deep neural networking, takes it a step further and focuses on a narrower subset of AI.

Announcing Deep Learning World: The call-for-speakers for the inaugural Deep Learning World, June 3-7, 2018 in Las Vegas is open. Agenda now posted for Predictive Analytics World, Las Vegas – June 3-7, 2018.

Machine learning is the subfield of computer science that gives computers the ability to learn without being explicitly programmed. It explores the study and construction of algorithms that can learn from and make predictions on data. The R language is widely used among statisticians and data miners to develop statistical software and data analysis. Machine Learning is a cross-functional domain that uses concepts from statistics, math, software engineering, and more. In this course, you'll get to know the advanced techniques for Machine Learning with R, such as hyper-parameter turning, deep learning, and putting your models into production through solid, real-world examples.