With the rise of Machine Learning came the rise of developer tools and libraries. What are they good for and what are the top ones that every data scientist and ML engineer should know. This article sheds some light on those questions.

A deep learning framework is an interface, library or a tool which allows us to build deep learning models more easily and quickly, without getting into the details of underlying algorithms. They provide a clear and concise way for defining models using a collection of pre-built and optimized components.

Predicting the stock market is one of the most difficult things to do given all the variables. There are numerous factors involved – physical factors vs. psychological, rational and irrational behavior, etc. All these aspects combine to make share prices volatile and very difficult to predict accurately.

In this article, we will work with historical data about the stock prices of a publicly listed company. We will implement a mix of machine learning algorithms to predict the future stock price of this company, starting with simple algorithms like averaging and linear regression, and then move on to advanced techniques like Auto ARIMA and LSTM.

TensorFlow 2.0 has arrived, with a focus on ease of use, developer productivity, and scalability.

Now there’s a contest to show off your TF2 chops: The #PoweredByTF 2.0 Challenge.

Here’s a synopsis:

Developers of all ages, backgrounds, and skill levels are encouraged to submit projects. Teams may have between 1 and 6 participants. Participants are encouraged to expand the scope of an existing TensorFlow 1.x project, to migrate and continue work on a historic TensorFlow 1.x project; or to create an entirely new software solution using TensorFlow 2.0.

Keras and eager execution . Robust model deployment in production on any platform. […]

According to a new report by Indeed, there’ s a new top job in America and it’s a pretty cool gig if I do say so myself.

Machine learning engineer is the best job of 2019, according to an Indeed report released Thursday. With an average base salary of $146,085 and an impressive 344% growth in job postings, machine learning engineers are expected to continue on this growth track in the coming years, the report found, [Read more on TechRepublic]

The MIT Technology Review has an interesting article on one specific way that quantum computing can revolutionize machine learning.

Feature matching is a technique that converts data into a mathematical representation that lends itself to machine-learning analysis. The resulting machine learning depends on the efficiency and quality of this process. Using a quantum computer, it should be possible to perform this on a scale that was hitherto impossible.

Jerry Chi, Data Science Manager at SmartNews, has compiled a list of “mind-blowing” ML/AI breakthroughs of the last year or two.  I have to say that I agree with most of his choices.

Compared to other fields, machine learning / artificial intelligence seems to have a much higher frequency of super-interesting developments these days. Things that make you say “wow” or even “what a time to be alive!” (as the creator of Two Minute Papers always says)

A recent post on the math needed to do machine learning got me thinking and, when I get to thinking, I get to searching. I found this course on YouTube on Linear Algebra. In it, you’ll learn what linear algebra is and how it relates to vectors and matrices. Then look through what vectors and matrices are and how to work with them, including the knotty problem of eigenvalues and eigenvectors, and how to use these to solve problems. Finally, learn at how to use these to do fun things with datasets – like how to rotate images of faces and how to extract eigenvectors to look at how the Pagerank algorithm works.

Towards the end of the course, you’ll be able to write code blocks and encounter Jupyter notebooks in Python.

Normally, I say the best way to get started with machine learning is to do machine learning. So, strictly speaking, there’s no “required math” to get started and follow along with online tutorials. However, if you want to take your AI game to the next level, then it’s time to get deep into the math.

Data Science Central outlines specifics around the subjects to tackle and where to find them online.

  1. Linear Algebra — Professor Strang’s textbook and MIT Open Courseware course are recommended for good reason. Khan Academy also has some great resources, and there is a helpful set of review notes from Stanford.
  2. Multivariate Calculus — Again, MIT Open Courseware has good courses, and so does Khan Academy.
  3. Probability — Stanford’s CS 229, a course I’ve mentioned later, has an awesome probability review worth checking out.