In machine learning, if you’re not developing, you’ll fall out of the loop sooner or later. It’s a difficult and demanding discipline that requires constant growth and broadening of skills.
One of the best ways to always stay on top of the latest machine learning trends is to follow blogs, forums, and websites dedicated to this specific profession. It’s a great way to learn from other people, very often renowned experts in their field, discover the latest findings and tips that can help you become a better data scientist.
If you’re looking for the best machine learning resources that are always up-to-date, make sure to check out this list. You can find different resources based on category. They all include fields such as AI, machine learning, IT, tech, and general Data Science.
FORUMS & COMMUNITIES
Reddit – Reddit is the most powerful source of all knowledge on machine learning, deep learning and Data Science in general. You can find many different threads with interesting information including resources – websites, blogs, problems people face, and smart solutions to common difficulties. Try out these threads to squeeze everything you can out of Reddit: r/MachineLearning, r/DeepLearning, r/DataScience, r/learnmachinelearning.
Stack Overflow – it’s an open community for people who spend their lives coding and are looking for answers to all types of questions or simply enjoy searching through interesting threads. It’s a great platform for sharing your knowledge and discovering new things.
Quora – Quora is another forum where people seek help or share their knowledge. It is not as detailed as Reddit, but you can still use it to look for some interesting resources. Make sure to check out different spaces related to machine learning to get updated information.
Kaggle – having a problem? Kaggle will help you. Share your issue with the Kaggle community and you will have it solved. Kaggle offers a large repository of code and data to make your work easier. Use the community to get inspired, fix an issue or develop your skills.
Jupyter community – community for people using Jupyter who need to find a solution to a problem, help others in fixing bugs and issues or share their work.
DEV – a community of software developers. Use it to find a solution to your dilemmas, experiments or share your knowledge.
ods.ai – Open DataScience is a superb Russian forum that unites researchers, engineers, and developers who work in Data Science. An extremely engaging place where you can build and improve relationships with other people and learn from each other.
fast.ai – similar to ods.ai, fast.ai is a place for people who want to learn, share ideas, and collaborate with others. It offers free courses for coders, software library, cutting-edge research, and community.
GreyCampus – here, you can find numerous courses from the field of data science. Other resources on GreyCampus include Codelabs where you can learn to code, OpenCampus with access to a large resource library, and a blog with interesting articles published regularly.
DataFlair – here, you will find helpful courses on Big Data. DataFlair is a platform that combines, training courses with discussion forums, assignments, and quizzes. You can also find interesting and extensive blog posts on different topics.
Coursera – under this link you will find one of the most popular, highly-rated course on machine learning offered by Stanford University. Coursera is a well-liked platform with online courses. You can search it for other interesting courses to expand your knowledge.
MIT OpenCourseWare – OCW is a free and open online publication of material from thousands of MIT courses, covering the entire MIT curriculum, ranging from the introductory to the most advanced graduate courses. You can check their YouTube channel for helpful videos.
edX – edX is another platform with helpful courses where you can get certified.
Harvard Online Courses – this is a Harvard website with high-quality courses covering various subjects. Everyone can find something helpful in this source of knowledge.
Stanford Courses – if you’re looking for courses on machine learning from Stanford University, make sure to check their website with online pieces of training.
Data Science Dojo – this platform offers online and in-person, hands-on data science training. Their goal is to teach students how to approach different business problems and think critically while using concepts and techniques learned in the course.
BLOGS & USEFUL WEBSITES
Anders Pink – it’s more of a product than a blog. Anders Pink offers a content curation tool.
It’s an extremely helpful tool, especially if you don’t have much time to go through the internet in search of relevant articles, Anders Pink helps to stay up-to-date with the latest trending content on the Big Data. Their AI-powered algorithm learns your preferences to provide you with fully relevant content for you and your team.
Neptune.ai – look around our website to find something of interest. We publish regularly to let you know what’s happening in the world of machine learning. Learn and get inspired!
Machine Learning Blogs – insightful articles to kill some free time (if you have any).
Science Daily – all the latest research news in the tech industry, and not only.
Hacker Noon – an independent tech media site with light content for tech enthusiasts.
Distill – latest articles about machine learning. Fully professional for most demanding data scientists.
Medium’s Machine Learning feed – helpful and interesting articles by experts from all over the world in section Machine Learning.
Springboard blog – on their blog, Springboard writes about Data Science, machine learning and other related topics.
OpenAI blog – OpenAI is a research laboratory based in San Francisco, California. They offer comprehensive resources on AI – a blog, research papers, and interesting articles. Everything is up-to-date provided by the experts in their field.
Google AI Blog – all the latest updates from the researchers and engineers at Google. On the blog, you can read how Google is incorporating AI and ML technology into its products.
KDnuggets – a leading site on AI, Analytics, Big Data, Data Mining, Data Science, and Machine Learning. Edited by Gregory Piatetsky-Shapiro and Matthew Mayo.
Bair – Berkley Artificial Intelligence Research. The BAIR Blog provides an accessible, general-audience medium for BAIR researchers to communicate research findings, perspectives on the field, and various updates. Posts are written by students, post-docs, and faculty in BAIR, and are intended to provide relevant and timely discussion of research findings and results, both to experts and the general audience. Posts on a variety of topics studied at BAIR will appear approximately once every two weeks.
Salmon Run – a collection of articles, tips, and random musings on application development and system design.
DeepMind – some interesting articles about the latest news from the company and their achievements.
O’Reilly – O’Reilly blog is worth following as they post high-quality articles on the ideas, information, and tools that make data work.
Hacker News – everything you want to know but are afraid to ask. A comprehensive source of topics provided by the Y Combinator.
Flipboard – numerous articles on machine learning gathered by Flipboard.
Artificial Lawyer – a useful blog for those working in the field of Law. From this blog, you can learn about the latest tech trends related to law and how to automate work of a law agency.
Lionbridge – Lionbridge regularly publishes articles covering such topics as machine learning or AI.
Google News – for all the latest hot news from the world of machine learning.
Towards Data Science – interesting topics around data science, machine learning, artificial intelligence, programming, and more to help you learn and develop skills.
SPD Group Blog – insightful articles about the Machine Learning industry.
RESEARCH PAPERS & ACADEMIC RESOURCES
MIT News – straight from MIT (Massachusetts Institute of Technology) all the latest news from the world of machine learning.
ScienceDirect – lets you explore scientific, technical, and medical research.
Nature.com – interesting research on machine learning.
Academia.edu – Academia lets people share their research papers with others working in the field of machine learning.
Paper With Code – a free and open resource with Machine Learning papers, code, and evaluation tables.
arXiv – a free distribution service and an open archive for scholarly articles in the fields of physics, mathematics, computer science.
University of Oxford – research papers from the University of Oxford.
CIT – research papers from California Institute of Technology.
Machine Learning @ Berkley – A student-run organization at UC Berkeley working on ML applications in industry, academic research, and making ML education more accessible to all.
The Batch – a weekly newsletter from deeplearning.ai. The Batch presents the most important AI events and perspective in a curated, easy-to-read report for engineers and business leaders. Every Wednesday, The Batch highlights a mix of the most practical research papers, industry-shaping applications, and high-impact business news.
Books – if you are a bookworm, you can search through the Amazon to find a book that interests you.
Deep Learning – an MIT Press book by Ian Goodfellow, Yoshua Bengio, and Aaron Courville. The Deep Learning textbook is an online book available for free. It is intended to help students and practitioners enter the field of machine learning in general and deep learning in particular. You can also order it on Amazon.
Data Science Weekly – A free weekly newsletter featuring curated news, articles and jobs related to Data Science. Make sure to subscribe!
Data Elixir – A free weekly newsletter with top data science picks from around the web. Covering machine learning, data visualization, analytics, and strategy. Definitely worth subscribing!
To wrap it up
Everything you can find on this list is chosen based on popularity and users recommendations.
I’ll be constantly updating the list with helpful links so you can stay on top of machine learning news. Make sure to leave a comment if you think something should be included or excluded. Share your opinion, I’d love to hear from you!
ML Experiment Tracking: What It Is, Why It Matters, and How to Implement It
Jakub Czakon | Posted November 26, 2020
Let me share a story that I’ve heard too many times.
”… We were developing an ML model with my team, we ran a lot of experiments and got promising results…
…unfortunately, we couldn’t tell exactly what performed best because we forgot to save some model parameters and dataset versions…
…after a few weeks, we weren’t even sure what we have actually tried and we needed to re-run pretty much everything”
– unfortunate ML researcher.
And the truth is, when you develop ML models you will run a lot of experiments.
Those experiments may:
- use different models and model hyperparameters
- use different training or evaluation data,
- run different code (including this small change that you wanted to test quickly)
- run the same code in a different environment (not knowing which PyTorch or Tensorflow version was installed)
And as a result, they can produce completely different evaluation metrics.
Keeping track of all that information can very quickly become really hard. Especially if you want to organize and compare those experiments and feel confident that you know which setup produced the best result.
This is where ML experiment tracking comes in.Continue reading ->