Many articles have been written about the top machine learning algorithms: click here and here for instance. Most of them seem to define top as oldest, and thus most used, ignoring modern, efficient algorithms fit for big data, such as indexation, attribution modeling, collaborative filtering, or recommendation engines used by companies such as Amazon, Google, or Facebook. I received this morning and advertisement for a (self-published) book called Master Machine Learning Algorithms, and I could not resist to post the author's list of top 10 machine learning algorithms:: Some of these techniques such as Naive Bayes (variables are almost never uncorrelated), Linear Discriminant Analysis (clusters are almost never separated by hyperplanes), or Linear Regression (numerous model assumptions - including linearity - are almost always violated in real data) have been so abused that I would hesitate teaching them. This is not a criticism of the book; most textbooks mention pretty much the same algorithms, and in this case, even skipping all graph-related algorithms. Even k Nearest Neighbors have modern, fast implementations not covered in traditional books - we are indeed working on this topic and expect to have an article published shortly about it.

The presidential campaign made clear that chauvinist attitudes toward women remain stubbornly fixed in some parts of society. It turns out we're inadvertently teaching artificial-intelligence systems to be sexist, too. New research shows that subtle gender bias is entrenched in the data sets used to teach language skills to AI programs. As these systems become more capable and widespread, their sexist point of view could have negative consequences--in job searches, for instance. The problem results from the way machines are being taught to read and talk.

Today's developers often hear about leveraging machine learning algorithms in order to build more intelligent applications, but many don't know where to start. One of the most important aspects of developing smart applications is to understand the underlying machine learning models, even if you aren't the person building them. Whether you are integrating a recommendation system into your app or building a chat bot, this guide will help you get started in understanding the basics of machine learning. This introduction to machine learning and list of resources is adapted from my October 2016 talk at ACT-W, a women's tech conference. Machine learning studies computer algorithms for learning to do stuff.

The Machine Learning Canvas is a template for developing new (or documenting existing) intelligent systems based on data and machine learning. It is a visual chart with elements describing the key aspects of such systems: the value proposition, the data to learn from (to create predictive models), the utilization of predictions (to create proposed value), requirements and measures of performance. It assists teams of data scientists, software engineers, product and business managers, in aligning their activities. This tutorial will help you get into the right mindset to go beyond the current hype around machine learning, beyond proofs of concept, and to clearly see how this technology can have an actual impact in your domain. I'll present the general structure of the Canvas, the different boxes it is composed of and the associated questions to answer. We'll see how to fill it in iteratively on a churn prevention example.

The developments in Google's Cloud Computing segment, especially the Cloud Machine Learning service, have been so rapid that Google calls it one of its fastest growing product areas. Google has been ramping up their Cloud Platform quite aggressively in recent months. Just a few weeks ago, the Google Cloud Platform opened its newest zone in Tokyo, increasing the total number of regions they are present in to six - three in the US and one each in Belgium and Taiwan and Tokyo. Not long ago, the company announced its acquisition of Orbitera, a cloud commerce company. The developments in Google's Cloud Computing segment, especially the Cloud Machine Learning service, have been so rapid that Google calls it one of its fastest growing product areas.

Think back to what you ate for breakfast this morning. Did you picture yourself in your kitchen and visualize the plate in front of you to remember exactly what you ate? That's called an episodic memory – a memory of a particular event that happened at a specific time and place, as opposed to a semantic memory, which refers to more general knowledge or rules that someone understands. Cognitive scientists have long thought that humans were the only animals capable of traveling down memory lane by having episodic memories. Dogs, for example, were known to commit things to semantic memory. When repeatedly trained to sit, stay, or lie down, they learn a rule. But they, like other nonhuman animals, were thought to live exclusively in the here and now – until now.

Why did I bother writing this? Well, here is one of the most trivial yet life-changing insights and worldly wisdoms from my former professor that has become my mantra ever since: "If you have to do this task more than 3 times just write a script and automate it." By now, you may have already started wondering about this blog. I haven't written anything for more than half a year! Okay, musings on social network platforms aside, that's not true: I have written something – about 400 pages to be precise. This has really been quite a journey for me lately. And regarding the frequently asked question "Why did you choose Python for Machine Learning?"

Google DeepMind's artificial intelligence team, alongside researchers at the University of California, Berkeley, has trained AI machines to interact with objects in order to evaluate their properties without any prior awareness of physical laws. The research project drew inspiration from child development and sought to train AI to mirror human capacity to interact with physical objects and infer properties such as mass, friction, and malleability. The study, entitled Learning to perform physics experiments via deep reinforcement learning, explained that while recent advances in AI have achieved'superhuman performance' in complex control problems and other processing tasks, the machines still lack a common sense understanding of our physical world – 'it is not clear that these systems can rival the scientific intuition of even a young child.' Lead researcher Misha Denil and his team set about various trials in different virtual environments in which the AI was faced with a series of blocks and tasked with assessing their properties. In the first simulation, called Which is Heavier, the AI was given a set of four blocks which were the same size but varied in mass.

Why did I bother writing this? Well, here is one of the most trivial yet life-changing insights and worldly wisdoms from my former professor that has become my mantra ever since: "If you have to do this task more than 3 times just write a script and automate it." By now, you may have already started wondering about this blog. I haven't written anything for more than half a year! Okay, musings on social network platforms aside, that's not true: I have written something – about 400 pages to be precise. This has really been quite a journey for me lately. And regarding the frequently asked question "Why did you choose Python for Machine Learning?"

During its wheeled treks on the Red Planet, NASA's Spirit rover may have encountered a potential signature of past life on Mars, report scientists at Arizona State University (ASU). To help make their case, the researchers have contrasted Spirit's study of "Home Plate" -- a plateau of layered rocks that the robot explored during the early part of its third year on Mars -- with features found within active hot spring/geyser discharge channels at a site in northern Chile called El Tatio. The work has resulted in a provocative paper: "Silica deposits on Mars with features resembling hot spring biosignatures at El Tatio in Chile." As reported online last week in the journal Nature Communications, field work in Chile by the ASU team -- Steven Ruff and Jack Farmer of the university's School of Earth and Space Exploration -- shows that the nodular and digitate silica structures at El Tatio that most closely resemble those on Mars include complex sedimentary structures produced by a combination of biotic and abiotic processes. "Although fully abiotic processes are not ruled out for the Martian silica structures, they satisfy an a priori definition of potential biosignatures," the researchers wrote in the study.