Fast forward to 2017 I have spent 100's of hours working on Deep learning projects and the technology has become more and more accessible due to several advancements in software(ease of usage -- Keras, PyTorch), hardware(GPU becoming commercially viable for someone like me sitting in India -Not still cheap), availability of data, good books and MOOCS. After completing the 3 most popular MOOCS in deep learning from Fast.ai, deeplearning.ai/Coursera In this post I talk about 5 aspects of each course which will help you decide. I came across this course when reading an article in kddnudgets . For the first time I heard about Jeremy Howard, searched about him in Wikipedia and was impressed .

Amazon is happy to announce that Alexa, the brain that powers Amazon Echo, is coming to India, along with three Alexa-enabled devices: Amazon Echo, Echo Plus, and Echo Dot. Customers who want to help further the development of Alexa and the Echo family of devices may request an invitation to purchase devices beginning today. This opens up new opportunities for developers in India and worldwide. Starting today, you can build for voice with Alexa and reach customers in India with the Alexa Skills Kit (ASK), our collection of self-service APIs, tools, documentation, and code samples. Hardware manufacturers can start developing Alexa-enabled products for Indian customers with the Alexa Voice Service (AVS) by participating in a developer preview.

Not that advertising is especially fickle, but even if you're looking to fly without a degree, you'll still have to figure out a few basics--compiling a portfolio, say, or understanding which awards are actually worth pursuing in a creative career. Thankfully, we have bots now. Bot Ad School (or BAS for short) is the labor of Daniel Liakh of BBH London, Kostia Liakhov and Kate Harrison of R/GA Sydney, and Sam Cable of Leo Burnett Sydney. Best experienced via mobile, the bot provides a crash course in everything from portfolio building and website creation help to information on awards (including student ones) and making the most of an ad internship. All in just seven bite-sized chapters, stuffed with GIFs.

Summary: If you are mid-career and thinking about switching into data science here are some things to think about in planning your journey. We get lots of inquiries from readers asking for career advice and many of these identify as mid-career looking to switch into data science. If you're in this group you face some of the same challenges beginners do but also some that are unique to your circumstance. Here are some thoughts and observations that may be valuable. When folks self-identify as mid-career they usually cite 10 or 20 years experience. By my way of thinking that makes you most likely 30 or 40 years old.

A well-calibrated estimator for the conditional probabilities should obey this equation. Once we have derived a statistical classifier, we need to validate it on some test data. This data should be different from that used to train the classifier, otherwise skill scores will be unduly optimistic. This is known as cross-validation. The confusion matrix expresses everything about the accuracy of a discrete classifier over a given database and you can use it to compose any possible skill score. Here, we are going to cover two that are rarely seen in the literature, but are nonetheless important for reasons that will become clear.

About this course: Experienced Computer Scientists analyze and solve computational problems at a level of abstraction that is beyond that of any particular programming language. This two-part class is designed to train students in the mathematical concepts and process of "Algorithmic Thinking", allowing them to build simpler, more efficient solutions to computational problems. In part 2 of this course, we will study advanced algorithmic techniques such as divide-and-conquer and dynamic programming. As the central part of the course, students will implement several algorithms in Python that incorporate these techniques and then use these algorithms to analyze two large real-world data sets. The main focus of these tasks is to understand interaction between the algorithms and the structure of the data sets being analyzed by these algorithms.

Drivers over 65 were responsible for 965 deadly accidents across Japan -- more than a quarter of the total -- in 2016, according to the National Police Agency. In one of the most shocking cases, an 87-year-old crashed his truck into a group of schoolchildren, killing a 6-year-old and injuring others, prompting demands for action on the issue. In a tranquil countryside setting outside the town of Kanuma, Tochigi Prefecture, on a track surrounded by rice paddies and mountains, elderly drivers are taking public safety into their own hands and completing refresher courses behind the wheel. Emiko Takahashi, a 73-year-old taking the course, admitted she had "no confidence" in her driving as she got older. "That's why I came here," she said, adding that she has no choice but to drive her ailing husband, seven years her senior, to a hospital every day.

One of the most consistent problems surfacing in our discussions about IoT is the actual scope of "what exactly is IoT and what and who does it involve". To technical people, IoT is what they perceive it to be from their active role. It might be a cloud offering, some sensor data, a connectivity solution, a robotics solution or Artificial Intelligence application. They would all be correct but they lack the appropriate context in which to provide valid inputs. To business people, their interpretation may be "it's a security nightmare" or a "huge opportunity".

Last week, experts from the AI world came together for the Artificial Intelligence Conference at San Francisco to discuss insights, opportunities, challenges and trends related to the rapidly expanding field of AI. The conference included hands-on trainings, tutorials, startup showcase (which was won by PipelineAI), keynotes, sessions, expo, and social events. Here is my report on Key Takeaways from AI Conference in San Francisco 2017 – Day 1. Michael Jordan, Distinguished Professor, UC Berkeley gave his keynote on "How to escape saddle points efficiently". We are in a great time with regards to AI and Machine Learning, due to immense interest and the pace of technological advances. However, the theories and our understanding is lagging to keep up with the challenges.

Data Scientists enjoy one of the top-paying jobs, with an average salary of $120,000 according to Glassdoor and Indeed. If you've got some programming or scripting experience, this course will teach you the techniques used by real data scientists in the tech industry – and prepare you for a move into this hot career path. This comprehensive course includes 68 lectures spanning almost 9 hours of video, and most topics include hands-on Python code examples you can use for reference and for practice. I'll draw on my 9 years of experience at Amazon and IMDb to guide you through what matters, and what doesn't. The topics in this course come from an analysis of real requirements in data scientist job listings from the biggest tech employers.