Work with me or attend my 2 day workshop! Here's a quick example of reading a shape file into R as simple features via st_read(), then plotting those features (in this case, North Carolina counties) using each one of the four mapping approaches plotly provides. You might be wondering, "What can plotly offer over other interactive mapping packages such as leaflet, mapview, mapedit, etc?". One big feature is the linked brushing framework, which works best when linking plotly together with other plotly graphs (i.e., only a subset of brushing features are supported when linking to other crosstalk-compatible htmlwidgets). Another is the ability to leverage the plotly.js

Microsoft today launched two new courses in its online education program for developers: an entry-level software development class and an AI course for more advanced developers who want to expand their knowledge of machine learning. It's no secret that there aren't enough data scientists and machine learning developers available to fulfill the current demand. It's no surprise, then, that a number of large companies have started to teach the fundamentals of these disciplines to their existing employees; starting today, anybody can take the AI courses that Microsoft first developed for its own employees. The Microsoft Professional Program for Artificial Intelligence is available for free on edX.org, though you can also opt to pay for a certificate. Each course runs three months and starts at the beginning of the quarter. Unsurprisingly, there's a bit of a focus on Azure and Microsoft's Cognitive Services here (and you need an Azure account), but otherwise the course is agnostic to the operating system you run.

Click here to register using the code UNDERWOOD and save $300 on an All Access Pass or $200 off a Conference Pass.] As machine learning algorithms are transparently weaved into business intelligence tools, automated decision-making processes, and the fabric of our day-to-day lives, it is becoming more important for everyone to understand the fundamentals. Machine learning is susceptible to a wide variety of bias types and a myriad of other issues if applied improperly. It also has amazing untapped potential when implemented correctly.

One of the things that's so fundamental in software development that it's easy to overlook is the idea of a repository of shared code. As programmers, libraries immediately make us more effective. In a sense, they change the problem solving process of programming. When using a library, we often think of programming in terms of building blocks -- or modules -- that can be tied together. How might a library look for a machine learning developer?

This article is intended for practitioners who might not necessarily be statisticians or statistically-savvy. The mathematical level is kept as simple as possible, yet I present an original, simple approach to test for randomness, with an interesting application to illustrate the methodology. This material is not something usually discussed in textbooks or classrooms (even for statistical students), offering a fresh perspective, and out-of-the-box tools that are useful in many contexts, as an addition or alternative to traditional tests that are widely used. This article is written as a tutorial, but it also features an interesting research result in the last section. The example used in this tutorial shows how intuiting can be wrong, and why you need data science.

The expected number of submissions is at least 5 for half day workshops and at least 8 for full day ones. Workshops that do not reach the suggested target might not be activated. Workshop schedule: each workshop will be assigned a number of slots (from 1 to 3 slots) during the conference days. A slot can vary from 1 to 2.5 hours. The distribution of slots will take into account the accepted papers and workshop organization.

Deep learning is the technology driving today's artificial intelligence revolution. Dimensionality reduction is one of the most crucial tools in a data scientists' toolbox, and modern tools can yield truly magical results. ODSC Europe 2017 is a unique collection of over 70 insightful presentations on data science modeling, tools, and languages, and topics delivered by top experts in the field. Topics include deep learning, quant finance and AI for business and more. Data visualisation offers a brilliant way of bringing the raw numbers to life.

When the Arizona Daily Star began experimenting with chatbots in 2016, readers seemed excited…and a little confused. They were fascinated by the new technology, but often responded to the bot in ways that hinted they were unsure what, or who, was on the other end. "A lot of users feel like they're talking to a person," says Daily Star Product Manager Becky Pallack, who helped test one bot targeted at local parents and another for super shoppers. "They'll say thank you and send emojis." Bots are everywhere now, helping people hail Lyfts, order pizza, and choose lipstick--and the experience can range from simple and easy to befuddling and unpleasant.

We present a neurosymbolic approach to the lifelong learning of algorithmic tasks that mix perception and procedural reasoning. Reusing highlevel concepts across domains and learning complex procedures are two key challenges in lifelong learning. We show that a combination of gradientbased learning and symbolic program synthesis can be a more effective response to these challenges than purely neural methods. Concretely, our approach, called HOUDINI, represents neural networks as strongly typed, end-to-end differentiable functional programs that use symbolic higher-order combinators to compose a library of neural functions. Our learning algorithm consists of: (1) a program synthesizer that performs a type-directed search over programs in this language, and decides on the library functions that should be reused and the architectures that should be used to combine them; and (2) a neural module that trains synthesized programs using stochastic gradient descent. We evaluate our approach on three algorithmic tasks. Our experiments show that our type-directed search technique is able to significantly prune the search space of programs, and that the overall approach transfers high-level concepts more effectively than monolithic neural networks as well as traditional transfer learning.

Natural language processing tasks, such as caption generation and machine translation, involve generating sequences of words. Models developed for these problems often operate by generating probability distributions across the vocabulary of output words and it is up to decoding algorithms to sample the probability distributions to generate the most likely sequences of words. In this tutorial, you will discover the greedy search and beam search decoding algorithms that can be used on text generation problems. How to Implement Beam Search Decoder for Natural Language Processing Photo by See1,Do1,Teach1, some rights reserved. In natural language processing tasks such as caption generation, text summarization, and machine translation, the prediction required is a sequence of words.