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ver, watching the video before reading the paper will give you a head start. You are more likely to understand the entire paper quickly if you know its overview.</p><p id="29ba"><b>Note: </b>Now, even though the channels mentioned above are great and discuss a selected paper in detail, it is improbable that you would see them discussing papers within your niche on their channels. Papers selected and discussed in their videos are intended to resonate with a general audience and discuss major breakthroughs only, which may or may not have a substantial technical novelty per se.</p><h1 id="03e8">Newsletters</h1><p id="159c">If you are more into reading, newsletters are another great source to keep up with the latest research. From the tons of newsletters out there, two that I follow actively are:</p><h2 id="bacd">#1 The Batch</h2><div id="f092" class="link-block"> <a href="https://read.deeplearning.ai/the-batch/"> <div> <div> <h2>The Batch | DeepLearning.AI | AI News & Insights</h2> <div><h3>Special Issue! Foundational Algorithms, Where They Came From, Where They're Going Government Smacks Down Errant…</h3></div> <div><p>read.deeplearning.ai</p></div> </div> <div> <div style="background-image: url(https://miro.readmedium.com/v2/resize:fit:320/0*6HrncIZnzDtR85N6)"></div> </div> </div> </a> </div><p id="1abd">The Batch is the official weekly newsletter of Deeplearning.ai. The latest news, breakthroughs, and insights in Artificial Intelligence are delivered weekly to your inbox along with a note by Dr. Andrew Ng himself.</p><h2 id="541d">#2 Data Science Weekly</h2><div id="09bb" class="link-block"> <a href="https://www.datascienceweekly.org/"> <div> <div> <h2>Data Science Weekly Newsletter</h2> <div><h3>A free weekly newsletter of Data Science articles, news, tools, libraries, and cool projects One email every Thursday…</h3></div> <div><p>www.datascienceweekly.org</p></div> </div> <div> <div style="background-image: url(https://miro.readmedium.com/v2/resize:fit:320/0*DwkB21eiXleKx0YX)"></div> </div> </div> </a> </div><p id="2615">This is the second newsletter that I have been actively following lately. The main highlights comprise articles, videos, books, job opportunities, and tutorials in the field of Data Science. As per my experience, this newsletter is also a great starting point for aspiring Data Scientists or beginners who are still exploring the vastness of AI and its applications. You can decide if this is a good fit for you by referring to their newsletter archive <a href="https://www.datascienceweekly.org/newsletters">here</a>.</p><p id="e55d"><b>P.S</b>. Explore the footer <a href="https://www.datascienceweekly.org/">here</a> for courses, tutorials, and other Data Science related resources.</p><h1 id="21e4">Podcasts</h1><h2 id="2d3f">#1 Lex Fridman Podcast</h2><div id="7713" class="link-block"> <a href="https://www.youtube.com/c/lexfridman/about"> <div> <div> <h2>Lex Fridman</h2> <div><h3>Lex Fridman Podcast and other videos.</h3></div> <div><p>www.youtube.com</p></div> </div> <div> <div style="background-image: url(https://miro.readmedium.com/v2/resize:fit:320/0*VyE8_oqOusfQ4VKa)"></div> </div> </div> </a> </div><p id="52b6">Formerly known as the AI Podcast, Dr. Lex Fridman hosts interactions with some of the most renowned personalities such as Nobel Prize Winners, industry leaders, well-known researchers in the field of academics, entrepreneurs, athletes, and creatives. It is always great to hear people reflect upon their ground-breaking works and the challenges they faced — something which you never get to know while reading their papers.</p><h2 id="590c">#2 TWIML: This week in Machine Learning</h2><div id="2d04" class="link-block"> <a href="https://twimlai.com/"> <div> <div> <h2>TWIML: The voice of machine learning and artificial intelligence</h2> <div><h3>Intelligent content that gives practitioners, innovators, and leaders an inside look at the present and future of ML &…</h3></div> <div><p>twimlai.com</p></div> </div> <div> <div style="background-image: url(https://miro.readmedium.com/v2/resize:fit:320/0*u_8fsGuQwjBAqrj1)"></div> </div> </div> </a> </div><p id="025e">As the name suggests, the TWIML AI Podcast presents ideas from the world of ML and AI to the broad community of people encompassing ML/AI researchers, data scientists, engineers, and IT leaders. Topics discussed by the host Sam Charrington include machine learning, artificial intelligence, deep learning, natural language processing, neural networks, analytics, computer science, data science, and more.</p><h1 id="e67c">Additional Resources</h1><p id="677b">Mentioned below are some more resources that I frequently refer to get the latest updates as and when needed.</p><h2 id="14c5">#1 Papers With Code</h2><div id="b495" class="link-block"> <a href="https://paperswithcode.com/"> <div> <div> <h2>Papers with Code - The latest in Machine Learning</h2> <div><h3>Papers With Code highlights trending Machine Learning research and the code to implement it.</h3></div> <div><p>paperswithcode.com</p></div> </div> <div> <div style="background-image: url(https://miro.readmedium.com/v2/resize:fit:320/0*CXdqOtFcIT8Ex100)"></div> </div> </div> </a> </div><h2 id="d460">#2 NLP Pr

Options

ogress</h2><div id="753a" class="link-block"> <a href="https://nlpprogress.com/"> <div> <div> <h2>Tracking Progress in Natural Language Processing</h2> <div><h3>For more tasks, datasets and results in Chinese, check out the Chinese NLP website. This document aims to track the…</h3></div> <div><p>nlpprogress.com</p></div> </div> <div> <div style="background-image: url(https://miro.readmedium.com/v2/resize:fit:320/)"></div> </div> </div> </a> </div><h2 id="5c4b">#3 Google AI Blog</h2><div id="7e17" class="link-block"> <a href="https://ai.googleblog.com/"> <div> <div> <h2>Google AI Blog</h2> <div><h3>In efforts to learn about the quantum world, scientists face a big obstacle: their classical experience of the world…</h3></div> <div><p>ai.googleblog.com</p></div> </div> <div> <div style="background-image: url(https://miro.readmedium.com/v2/resize:fit:320/0*nPyEXz5KLkrHcYnz)"></div> </div> </div> </a> </div><h2 id="22c6">#4 Machine Learning Mastery</h2><div id="2a13" class="link-block"> <a href="https://machinelearningmastery.com/"> <div> <div> <h2>Machine Learning Mastery - Machine Learning Mastery</h2> <div><h3>Making developers awesome at machine learning.</h3></div> <div><p>machinelearningmastery.com</p></div> </div> <div> <div style="background-image: url(https://miro.readmedium.com/v2/resize:fit:320/0*ZN5nyevUMJJpBo5H)"></div> </div> </div> </a> </div><h2 id="fb61">#5 Berkeley Artificial Intelligence Research Group</h2><div id="8619" class="link-block"> <a href="https://bair.berkeley.edu/blog/about/"> <div> <div> <h2>The BAIR Blog</h2> <div><h3>The BAIR Blog provides an accessible, general-audience medium for BAIR researchers to communicate research findings…</h3></div> <div><p>bair.berkeley.edu</p></div> </div> <div> <div style="background-image: url(https://miro.readmedium.com/v2/resize:fit:320/0*3mz2PVv3PuqQJA2r)"></div> </div> </div> </a> </div><h2 id="e634">#6 Arxiv-sanity</h2><div id="46f0" class="link-block"> <a href="https://arxiv-sanity-lite.com/"> <div> <div> <h2>arxiv-sanity</h2> <div><h3>(hi! just btw you have to be logged in to be able to add/delete/curate tags for papers and get recommendations)</h3></div> <div><p>arxiv-sanity-lite.com</p></div> </div> <div> <div style="background-image: url(https://miro.readmedium.com/v2/resize:fit:320/)"></div> </div> </div> </a> </div><h2 id="3674">#7 Sebastian Ruder’s Newsletter</h2><div id="a16c" class="link-block"> <a href="https://newsletter.ruder.io/"> <div> <div> <h2>NLP News - Revue</h2> <div><h3>NLP News - Regular analyses of advances in natural language processing and machine learning....</h3></div> <div><p>newsletter.ruder.io</p></div> </div> <div> <div style="background-image: url(https://miro.readmedium.com/v2/resize:fit:320/0*xbZLtcMtW5osCm8O)"></div> </div> </div> </a> </div><h2 id="0f52">#8 DeepMind Blog</h2><div id="da91" class="link-block"> <a href="https://www.deepmind.com/"> <div> <div> <h2>DeepMind</h2> <div><h3>Artificial intelligence could be one of humanity's most useful inventions. We research and build safe artificial…</h3></div> <div><p>www.deepmind.com</p></div> </div> <div> <div style="background-image: url(https://miro.readmedium.com/v2/resize:fit:320/0*LOnNn8WXIysZG_TQ)"></div> </div> </div> </a> </div><h2 id="0e9a">#9 OpenAI Blog</h2><div id="1bea" class="link-block"> <a href="https://openai.com/"> <div> <div> <h2>OpenAI</h2> <div><h3>OpenAI is an AI research and deployment company. Our mission is to ensure that artificial general intelligence benefits…</h3></div> <div><p>openai.com</p></div> </div> <div> <div style="background-image: url(https://miro.readmedium.com/v2/resize:fit:320/0*MjHL0iTgJVtPYLpR)"></div> </div> </div> </a> </div><p id="1859">That’s it. I have intentionally included a few resources from each category in this post. My objective is to provide a starting point for folks struggling or feeling overwhelmed after seeing numerous options. To begin, I would recommend starting slow but being consistent. Remember that learning is always an ongoing process and is never expected to stop. Actively engage with the content they publish to build your interests. Once you build a habit of reading, add more resources as per your requirement or related to your niche.</p><p id="9c94">Also, Twitter and LinkedIn are great places to learn about the latest research. Medium is incredible too. Follow the top writers, trending posts, and best posts within AI and Data Science, and start following people/publications which resonate with your interests.</p><blockquote id="f606"><p>Please do share with me (and other readers) if you have any resources to recommend. I would love to augment my reading list with new learning resources.</p></blockquote></article></body>

Top AI Resources You Must Follow If You Are Into AI

How to keep up with the latest machine learning advancements

Photo by Becca Tapert on Unsplash

With the vast amounts of research and development occurring every day in Machine Learning, it can be challenging for industry professionals solving real-life use-cases and academic researchers to keep up with all the recent innovations in their specific field of interest.

Having struggled for a long time, after a series of exploration, I finally stumbled upon a handful of resources that I wish to share in this post. The list I am proposing here is not exhaustive. However, I am sure they will be sufficient to keep you updated— eliminating all the heavy lifting of scanning the list of published papers yourself.

The resources I will discuss in this post encompass video, newsletter, and podcast formats. Depending upon your personal preference and interests, you may decide which ones you wish to follow. Moreover, I don’t want to overwhelm you with numerous options to pursue. Therefore, I have kept the list as short as I could.

YouTube Channels for Popular Research Papers

Videos are far the most preferred option to acquire knowledge about new topics for many of us. The channels that I actively follow are mentioned below:

#1 Yannic Kilcher

Yannic Kilcher, a Ph.D. from ETH Zurich, makes videos that center around discussing hot research papers in deep learning, mainly in the field of Natural Language Processing (NLP) and Computer Vision (CV). Moreover, I personally love his approach to explaining papers as well. He often provides the necessary details one would need to understand a concept discussed in the paper. His videos are usually marked with a touch of humor, making it an incredible experience all in all to watch and learn.

Once or sometimes twice a month, he also uploads videos tagged as “ML News”, wherein he brings the notable highlights and breakthroughs that took place within the last month.

#2 AI Coffee Break with Letitia

Similar to Yannic Kilcher, Letitia Parcalabescu also posts videos (often bi-weekly) around the latest updates, primarily in the field of Natural Language Processing (NLP) and Computer Vision (CV). Her videos, unlike Yannic’s, are characterized by smooth and visually attractive editing (a personal opinion), which makes it pretty easy to follow and understand her explanations well. Her videos, again, unlike that of Yannic’s, aren’t pretty long, but as per my experience, she covers everything discussed in the selected paper in detail for the viewers to understand.

What’s more, you would see her consistently engaging with her audience by posting poll questions on YouTube, which I like to participate in. She posts one poll question every day, such as the one shown below:

Screenshot taken by the author.

#3 Two Minute Papers

While the two YouTube channels mentioned above cover the technical details in-depth, Two Minutes Papers by Károly Zsolnai-Fehér is meant for everyone. This includes people without any technical background as well. The entire video focuses primarily on the results of the paper being discussed than on the specific technical details and research conducted. This is good because the video gives you an overview of the entire article within a few minutes. Depending upon your interests, you can decide whether to continue reading the paper.

Moreover, watching the video before reading the paper will give you a head start. You are more likely to understand the entire paper quickly if you know its overview.

Note: Now, even though the channels mentioned above are great and discuss a selected paper in detail, it is improbable that you would see them discussing papers within your niche on their channels. Papers selected and discussed in their videos are intended to resonate with a general audience and discuss major breakthroughs only, which may or may not have a substantial technical novelty per se.

Newsletters

If you are more into reading, newsletters are another great source to keep up with the latest research. From the tons of newsletters out there, two that I follow actively are:

#1 The Batch

The Batch is the official weekly newsletter of Deeplearning.ai. The latest news, breakthroughs, and insights in Artificial Intelligence are delivered weekly to your inbox along with a note by Dr. Andrew Ng himself.

#2 Data Science Weekly

This is the second newsletter that I have been actively following lately. The main highlights comprise articles, videos, books, job opportunities, and tutorials in the field of Data Science. As per my experience, this newsletter is also a great starting point for aspiring Data Scientists or beginners who are still exploring the vastness of AI and its applications. You can decide if this is a good fit for you by referring to their newsletter archive here.

P.S. Explore the footer here for courses, tutorials, and other Data Science related resources.

Podcasts

#1 Lex Fridman Podcast

Formerly known as the AI Podcast, Dr. Lex Fridman hosts interactions with some of the most renowned personalities such as Nobel Prize Winners, industry leaders, well-known researchers in the field of academics, entrepreneurs, athletes, and creatives. It is always great to hear people reflect upon their ground-breaking works and the challenges they faced — something which you never get to know while reading their papers.

#2 TWIML: This week in Machine Learning

As the name suggests, the TWIML AI Podcast presents ideas from the world of ML and AI to the broad community of people encompassing ML/AI researchers, data scientists, engineers, and IT leaders. Topics discussed by the host Sam Charrington include machine learning, artificial intelligence, deep learning, natural language processing, neural networks, analytics, computer science, data science, and more.

Additional Resources

Mentioned below are some more resources that I frequently refer to get the latest updates as and when needed.

#1 Papers With Code

#2 NLP Progress

#3 Google AI Blog

#4 Machine Learning Mastery

#5 Berkeley Artificial Intelligence Research Group

#6 Arxiv-sanity

#7 Sebastian Ruder’s Newsletter

#8 DeepMind Blog

#9 OpenAI Blog

That’s it. I have intentionally included a few resources from each category in this post. My objective is to provide a starting point for folks struggling or feeling overwhelmed after seeing numerous options. To begin, I would recommend starting slow but being consistent. Remember that learning is always an ongoing process and is never expected to stop. Actively engage with the content they publish to build your interests. Once you build a habit of reading, add more resources as per your requirement or related to your niche.

Also, Twitter and LinkedIn are great places to learn about the latest research. Medium is incredible too. Follow the top writers, trending posts, and best posts within AI and Data Science, and start following people/publications which resonate with your interests.

Please do share with me (and other readers) if you have any resources to recommend. I would love to augment my reading list with new learning resources.

Artificial Intelligence
Machine Learning
Data Science
NLP
Research
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