Originally published in SAP Blogs, October 16, 2019. For today's leading deep learning methods and technology, attend the conference and training workshops at Deep Learning World Las Vegas, May 31-June 4, 2020. Machine learning algorithms, especially deep learning neural networks often produce models that improve the accuracy of prediction. But the accuracy comes at the expense of higher computation and memory consumption. A deep learning algorithm, also known as a model, consists of layers of computations where thousands of parameters are computed in each layer and passed to the next, iteratively.

A shorter version of this article was originally published by Scientific American. With articles like these, the press will have you believe that machine learning can reliably predict whether you're gay, whether you'll develop psychosis, whether you'll have a heart attack, and whether you're a criminal – as well as other ambitious predictions such as when you'll die and whether your unpublished book will be a bestseller. Machine learning can't confidently tell such things about each individual. In most cases, these things are simply too difficult to predict with certainty. Researchers report high "accuracy," but then later reveal – buried within the details of a technical paper – that they were actually misusing the word "accuracy" to mean another measure of performance related to accuracy but in actuality not nearly as impressive.

Economic disruption is a reality which has been a gradual development over the last several decades. Artificial intelligence (AI) has simply accelerated this process. Virtually every industry has been impacted by AI and certainly data science is no exception. Yet, we may also inquire how does machine learning fit within this overall discussion. The explosion of literature on these topics over the last several years is a testament to the popularity of both topics.

Python is one of the most widely used languages in data science, and an incredibly popular general programming language on its own. Many prospective data scientists are first faced with the issue of which programming language might be their choice when diving into data science. This is further complicated if you don't already bring a set of existing programming skills on which to rely. Even better would be a thorough understanding of Python as you shift to data science (substitute another language if it is to be your preferred data science programming tool), but many newcomers to the field find themselves either starting from relative scratch when it comes to either programming in general, or Python more specifically. This is a collection of 10 interesting resources in the form of articles and tutorials for the aspiring data scientist new to Python, meant to provide both insight and practical instruction when starting on your journey.

For today's leading deep learning methods and technology, attend the conference and training workshops at Deep Learning World, June 16-19, 2019, in Las Vegas. Marvin Minsky engineered the first known artificial neural network, in which "rats" represented as lights learned to solve a maze. As an undergraduate at Harvard in the late 1940s and in his first year of grad school at Princeton in 1950, Marvin Minsky pondered how to build a machine that could learn. At both universities, Minsky studied mathematics, but he was curious about the human mind--what he saw as the most profound mystery in science. He wanted to better understand intelligence by recreating it.

I'm a physicist who works at a YC startup. Our job is to help new grads get hired into their first machine learning jobs. Some time ago, I wrote about the things you should do to get hired into your first machine learning job. I said in that post that one thing you should do is build a portfolio of your personal machine learning projects. But I left out the part about how to actually to do that, so in this post, I'll tell you how. If you don't have a track record, personal projects are the closest substitute.)

Breaking into the world of Data Science can be tricky, but writing a killer resume gives you a better chance of landing a job in this highly competitive field. There are a few simple steps you can take to build a resume that gets noticed. If you haven't heard of an Applicant Tracking System (ATS), it's software used by companies that receive lots of job applications, and it chooses which applications to forward to the hiring manager and which applications are automatically responded to with a rejection letter. There has been a movement lately to create these gorgeously designed resumes. You'll see people "Tableau-ize" their resume (ie -- creating a resume using Tableau), include logos, or include charts that are subjective graphs of their level of knowledge in certain skill sets.

For today's leading deep learning methods and technology, attend the conference and training workshops at Predictive Analytics World for Healthcare, June 16-19, 2019 in Las Vegas. Moving patient data online has been a great boon for the practice of medicine. Patient records, formerly pieces of paper in a folder, are transitioning to electronic health records, or EHRs. While this has done wonders for transferring records between offices and aiding in connecting technology like wearables and providing big data for machine learning, the quantity also raises questions of patient privacy and data security. The start of this story is in the volume of data.

Bias is an overloaded word. It has multiple meanings, from mathematics to sewing to machine learning, and as a result it's easily misinterpreted. When people say an AI model is biased, they usually mean that the model is performing badly. But ironically, poor model performance is often caused by various kinds of actual bias in the data or algorithm. Machine learning algorithms do precisely what they are taught to do and are only as good as their mathematical construction and the data they are trained on.

As most data science practitioners know, artificial intelligence (AI) is not new and has been explored by academia back as far back as the fifties. The real core of AI is the branch of mathematics related to neural nets which have been explored both by academia as well as data science practitioners. A number of practitioners including myself familiarized ourselves with these techniques which became one more item within the data scientist toolkit. For those of us involved in using predictive analytics to predict consumer behaviour related to marketing and risk, logistic regression and decision trees in many cases performed at about the same level as neural nets. In some cases such as fraud where there were typically a much larger volume of records, neural nets did exceed the more traditional type of modelling techniques.