I have been actively focusing on specialising Deep Learning for the last 2 years. My personal interest towards Deep learning started around 2015 when Google open sourced Tensorflow. Tried quickly couple of examples from the Tensorflow documentation and left with a feeling that Deep learning is difficult, partly because the framework was new and required better hardware and tons of patience. 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

Deep learning is not a beginner-friendly subject -- even for experienced software engineers and data scientists. If you've been Googling this subject, you may have been confused by the resources you've come across. To find the best resources, we surveyed engineers on their favorite sources for deep learning, and these are what they recommended. These educational resources include online courses, in-person courses, books, and videos. All are completely free and designed by leading professors, researchers, and industry professionals like Geoffrey Hinton, Yoshua Bengio, and Sebastian Thrun.

Deep learning is not a beginner-friendly subject -- even for experienced software engineers and data scientists. If you've been Googling this subject, you may have been confused by the resources you've come across. To find the best resources, we surveyed engineers on their favorite sources for deep learning, and these are what they recommended. These educational resources include online courses, in-person courses, books, and videos. All are completely free and designed by leading professors, researchers, and industry professionals like Geoffrey Hinton, Yoshua Bengio, and Sebastian Thrun.

Deep learning is not a beginner-friendly subject -- even for experienced software engineers and data scientists. If you've been Googling this subject, you may have been confused by the resources you've come across. To find the best resources, we surveyed engineers on their favorite sources for deep learning, and these are what they recommended. These educational resources include online courses, in-person courses, books, and videos. All are completely free and designed by leading professors, researchers, and industry professionals like Geoffrey Hinton, Yoshua Bengio, and Sebastian Thrun.

In fact, with all the hype around AI in the last few years ("sexiest job"?!?), I feel the only industry that has seen an AI boom is the MOOC industry! My own experience of learning ML, and broadly DS related topics, online has been mostly positive so far, though there have been the odd half baked courses designed to purely make some quick bucks. Here, I'm just listing down my all time favourite MOOCs / books that I thoroughly enjoyed: R4DS by Hadley Wickham: Without trying to start any language war, let me just say that R is my first love! The tidyverse, (and tidymodels) set of packages share a common design philosophy that makes it very easy to work with any kind of data. The only problem is its speed, R is optimized for humans, not machines.