In this post, we share with you a selection of sources of useful information about Data Science from the co-founder and CTO of DAGsHub, a community and web platform for data version control and collaboration between data scientists and machine learning engineers. The selection includes a variety of sources, from Twitter accounts to full-fledged engineering blogs, which are targeted at those who know exactly what they are looking for. Details under the cut.
From the author:
You are what you eat, and as a knowledge worker, you need a good informational diet. I want to share sources of information about Data Science, artificial intelligence and related technologies that I find most useful or attractive. I hope this helps you too!
A YouTube channel that is well suited to keep up to date with the latest events. The channel is updated frequently and the host has an infectious enthusiasm and positivity in all topics covered. Expect coverage of interesting work not only on AI, but also on computer graphics and other visually appealing topics.
On his YouTube channel, Yannick explains significant research in deep learning in technical detail. Instead of reading a study on your own, it's often quicker and easier to watch one of its videos to gain a deeper understanding of important articles. The explanations convey the essence of the articles without neglecting the math or getting lost in three pines. Yannick also shares his views on how studies fit together, how seriously results should be taken, broader interpretations, and so on. Beginners (or non-academic practitioners) find it more difficult to come to these discoveries on their own.
In their own words:
Machine learning research needs to be clear, dynamic, and vibrant. And Distill was created to help in research.
Distill is a unique machine learning research publication. Articles are promoted with stunning visualizations to give the reader a more intuitive understanding of the topics. Spatial thinking and imagination tend to work very well in helping you understand Machine Learning and Data Science topics. Traditional publication formats, on the other hand, tend to be rigid in their structure, static and dry, and sometimes . Chris Olah, co-creator of Distill, also maintains an amazing personal blog at . It hasn't been updated for a long time, but still remains a collection of the best deep learning explanations ever written. In particular, it helped me a lot LSTM!
Sebastian Ruder writes a very informative blog and newsletter, primarily about the intersection of neural networks and natural language text analysis. He also gives a lot of advice to researchers and conference speakers, which can be very helpful if you're in academia. Sebastian's articles tend to take the form of reviews, summarizing and explaining the state of the art in research and methods in a given area. This means that the articles are extremely useful for practitioners who want to get their bearings quickly. Sebastian also writes in .
Andrei Karpaty needs no introduction. In addition to being one of the most famous deep learning researchers on earth, he creates widely used tools such as as side projects. Countless people entered this realm through his Stanford course. , and it will be useful for you to know it neural network training. I also recommend watching it about the real problems that Tesla must overcome when trying to apply machine learning on a massive scale in the real world. Speech is informative, impressive and sobering. Besides articles about ML itself, Andrey Karpaty gives for . Read Andrew at and .
The Uber engineering blog is really impressive in terms of scale and breadth of coverage, covering a lot of topics, in particular . What I especially like about Uber's engineering culture is their tendency to release very interesting and valuable open source at a breakneck pace. Here are some examples:
- And the list goes on and onβ¦Hats off, Uber
Controversy aside, the OpenAI blog is undeniably great. From time to time, the blog posts content and insights about deep learning that can only come at the scale of OpenAI: hypothetical deep double descent. The OpenAI team tends to post infrequently, but these are important content.
The Taboola blog isn't as well known as some of the other sources in this post, but I think it's unique - the authors write about very mundane, real problems when trying to apply ML in production for a "normal" business: less about self-driving cars and RL agents winning world champions, more about "how do I know if my model is now predicting things with false confidence?". These issues are relevant to almost everyone working in the field and receive less press coverage than more common AI topics, but it still takes world-class talent to properly address these issues. Luckily, Taboola has both this talent and the willingness and ability to write about it so other people can learn too.
Along with Twitter, there's nothing better on Reddit than getting hooked on the research, tools, or wisdom of the crowd.
Posts are published only annually, but filled with information very densely. Compared to other sources on this list, this one is more accessible to non-tech business people. What I love about the talks is that they try to give a more holistic view of where the industry and research are heading, tying together advances in hardware, research, business, and even geopolitics from a bird's eye view. Be sure to start at the end to read about conflicts of interest.
Podcasts
Frankly, I think podcasts are ill-suited for learning about technical topics. After all, they use only sound to explain topics, and data science is a very visual field. Podcasts tend to give you an excuse to explore in more depth later, or for engaging philosophical discussions. However, here are some recommendations:
- when he talks to prominent researchers in the field of artificial intelligence. Episodes with Francois Chollet are especially good!
- . Nice to hear about new data infrastructure tools.
Awesome lists
There's less to keep an eye on here, but more resources that are helpful once you know what you're looking for:
Matty finds beautiful, creative ways to use neural networks, and it's just fun to see his results on your Twitter feed. Take a look at least post.
Ori is just a driving machine . He writes extensively about problems and solutions for data scientists. Be sure to subscribe to be notified when an article is published. His in particular is really impressive.
Co-founder of fast.ai, a comprehensive source of creativity and productivity.
A staff ML engineer at Github, Hamel Hussain is busy at work creating and reporting on many tools for coders in the data domain.
Creator of Keras, now update our understanding of what intelligence is and how to test it.
Research scientist at Google Brain.
Conclusion
The original post may be updated as the author finds great sources of content that it would be a shame not to include in the list. Feel free to contact him if you want to recommend some new source! And also DAGsHub Advocate [approx. transl. Public Practitioner] in Data Science, so if you create your own Data Science content, feel free to write to the author of the post.
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Source: habr.com