Machine Learning (ML) is becoming a universal concept across industries. However, the manufacturing industry trails behind other industries in widespread implementation of machine learning techniques, as the industry remains heavily dependent on individual experience-based decision making. Integrating Big Data analytics with Machine Learning can deliver insights that immensely benefit the manufacturers. It can help them improve process efficiencies and cut operational costs, and ultimately enhance customer experience. Consider the scenario of a stamping plant in the plant shop floor of an automotive OEM.

In the ongoing battle against cyber attacks, a man-machine collaboration could offer a new path to security. To keep up with cyber threats, the cybersecurity industry has turned to assistance from unsupervised artificial intelligence systems that operate independently from human analysts. But the Computer Science and Artificial Intelligence Laboratory (CSAIL) at the Massachusetts Institute of Technology in Cambridge, Mass., in partnership with the machine-learning startup PatternEx, is offering a fresh approach. Their new program, AI2, draws on what humans and machines each do best: It allows human analysts to build upon the large scale pattern recognition and learning capabilities of artificial intelligence. The industry standard right now is unsupervised machine learning, CSAIL research scientist Kalyan Veeramachaneni, who helped develop the program, says in a phone interview with The Christian Science Monitor.

Business agility is becoming a strategic necessity. Companies cannot be competitive if they're not staying ahead of their customers' expectations. You can see the effect of this when Apple introduced Siri in 2011 with the release of iPhone 4S, changing the customer experience. Since then, Google, Microsoft, and Amazon have all come up with their own A.I. concierge services to assure that they are meeting the customer expectations. Look at the current robotic interactive voice response (IVR) systems that require you to navigate through layers of menus to retrieve a simply answer: "Has my claim been paid?"

This is an example of using the new Google's TensorFlow library on monolingual translation going from modern English to Shakespeare based on research from Wei Xu. Use the example BASH script to train the model. This saves the check points in the --train_dir directory. If you run it again, the training process continues from the check point. To restart with fresh parameters, simply delete/rename the check points.

In this blog post, I'll talk about the Visual Question Answering problem, and I'll also present neural network based approaches for same. The source code for this blog post is written in Python and Keras, and is available on Github. An year or so ago, a chatbot named Eugene Goostman made it to the mainstream news, after having been reported as the first computer program to have passed the famed Turing Test in an event organized at the University of Reading. While the organizers hailed it as a historical achievement, most of the scientific community wasn't impressed. This leads us to the question: Is the Turing Test, in its original form, a suitable test for AI in the modern day?

Open source tools are increasingly important in the data science workflow. Github has become the de facto open source software clearinghouse, hosting all imaginable types of projects. Given the growing adoption of deep learning in academia, research, and hobby, and its increasing role in data science, we are exploring the top deep learning projects available on Github.*

Distance metrics are widely used in the machine learning literature. Traditionally, practicioners would choose a standard distance metric (Euclidean, City-Block, Cosine, etc.) using a priori knowledge of the domain. Distance metric learning (or simply, metric learning) is the sub-field of machine learning dedicated to automatically constructing optimal distance metrics. This package contains efficient Python implementations of several popular metric learning algorithms.

Forget about the meaning of words, forget about grammar, forget about syntax, forget even the very concept of a word. Now let the machine learn everything by itself.

Just knowing about techniques is akin to knowing the animals in a zoo -- you can name them, describe their properties, perhaps identify them in the wild. Understanding when to use them, formulating, building, testing, and deploying working mathematical models within an application area while avoiding the pitfalls --- these are the skills that distinguish, in my opinion. The emphasis should be on the science, applying a systematic, scientific approach to business, industrial, and commercial problems. But this requires skills broader than data mining & machine learning, as Robin Bloor argues persuasively in "A Data Science Rant". Application areas: learn about various application areas close to your interest, or that of your employer. The area is often less important than understanding how the model was built and how it was used to add value to that area.

Many people have gotten jobs in machine learning just by completing that MOOC. There're other similar online courses that help; for example the John Hopkins Data Science specialization. Participating in Kaggle or other online machine learning competitions has also helped people gain experience. Kaggle has a community with online discussions from which you can learn practical skills. Attending local meetups or academic conferences (if you can afford it) and talking to more experienced people will also help.