4 ML Roadmaps to Help You Find Useful Resources To Learn From
There are lots of ML resources around. But so much material how do you pick which ones are good and right for your situation?
These are what roadmaps are for. Many people in the ML community have made some you can view.
One of the most comprehensive ML roadmaps I have seen. Most beginner to intermediate questions will likely be answered in this mind map.
Deep Learning Papers Reading Roadmap
A great list of papers that you can try to implement. That starts from the fundamentals of deep learning to the state of the art.
Deep Learning's Most Important Ideas - A Brief Historical Review
It is not formally a roadmap. But can be used as such. As it talks about the most fundamental papers of DL. That you can implement.
A machine learning roadmap by Santiago
Short article giving a high level run down on learning ML. And the various resources.
This is a very short article but hopefully, you found some of the resources useful.
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Some YouTube channels that review papers
When I was reading a Reddit thread. People were wondering if there were YouTubers reviewing papers. As the OP noticed that one of the YouTuber's that he regularly watched stopped uploading videos. There are a few YouTubers that talk about ML and review papers.
I decided to compile some of the YouTube channels into this short list.
Two Minute Papers does great overviews of fascinating papers. Showing the increasing progress of ML.
Some of the videos I liked:
- 4 Experiments Where the AI Outsmarted Its Creators
This video showed various AI solving a problem not in the way the researchers intended to. That may include abusing the physics in the simulation or lateral thinking used by the model.
- A Video Game That Looks Like Reality!
A review of a paper that takes GTA V gameplay and converts them to photo-realistic footage.
Yannic Kilcher does in-depth reviews of various papers. As you go through the paper he shows you his thought process. And showing what important inside the paper. Very useful if don’t read that many papers. (Like me)
Some good videos:
A review of a paper that introduced transformers.
- DeepMind's AlphaFold 2 Explained! AI Breakthrough in Protein Folding What we know (& what we don't)
A great rundown on protein folding and speculating how Alphafold 2 works.
- GPT-3: Language Models are Few-Shot Learners (Paper Explained)
A comprehensive paper reading of the GPT-3 paper.
Bycloud you may have seen him around on Reddit. Creates short and insightful summaries of papers.
- AI Sky Replacement with SkyAR
Summary of paper that creates AR effects in video footage. Adding various effects to the video footage’s sky.
- AI Generates Cartoon Characters In Real Life [Pixel2Style2Pixel]
Reviewing a paper that converts cartoon characters to real-life equivalents and vice versa. Also explains how the paper made it easier to adjust the parameters of the GAN. Helping us adjust what images we want to produce.
This is a podcast series that interviews top ML researchers. While they don’t have videos about papers alone. As they interview various experts in the field. So they talk about many papers as a consequence.
While this is a short list maybe you can find these channels interesting and learn something new.
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Some Maths Resources to Help You in Your ML Journey
I have been looking for content to improve my maths skills for ML. I have also noticed when scrolling a few threads many people did not find content that explains maths in an intuitive manner. Leading to a lack of belief in learning ML. But this does not have to be.
I’m with you, odd-looking characters and Greek letters don’t look welcoming. But they are some good teachers online that can demystify that experience.
Some of those materials are below:
3blue1brown Calculus and Linear Algebra series
I remember watching both of these series a while. And I will be watching them again. The narrator explores the topic without getting bogged down in the details. Feels like your discovering the maths with the original people who made calculus. In the linear algebra series, he does such a great job visualising vector space. You can see the various operations done to vectors and matrices in picture form.
3blue1brown Deep Learning series
Taking the concepts from the previous series and applying them to deep learning.
I’m sure you know about Sal Kahn by now. As you watched a couple of his videos. His video intuitively explains various topics. Also, show you the various hand by hand actions you need to take to do various calculations. Like matrix multiplication and calculating derivatives.
Mathematics for Machine Learning book
I tend to use this book as a reference guide if it’s a concept I want to check out. This book goes through the most important subjects relevant to machine learning and goes in-depth.
Mathematics for Machine Learning - Multivariate Calculus – Imperial College London
A multi-hour series explaining how calculus is used in deep learning. The material comes at the subject with a high-level view. But goes into sufficient enough detail to help you learn a lot.
Understand Calculus in 35 Minutes - The Organic Chemistry Tutor
A general overview of the subject. So you can be familiar with the concepts for deep learning later on.
NOTE: you won’t learn all of calculus in 30 minutes. But the video will help you get accustomed to the main ideas of the subject.
Now, these are resources that I have not used or have used very lightly but gotten good recommendations from various people.
So check them out:
This course talks about the linear algebra used in real computation. Not just Linear algebra done by hand.
Deep Learning book by Ian Goodfellow and Yoshua Bengio and Aaron Courville
From their website:
The Deep Learning textbook is a resource intended to help students and practitioners enter the field of machine learning in general and deep learning in particular.
I have not thoroughly read all of the book. But I have used the notation page to understand maths symbols in various deep learning work.
An Introduction to Statistical Learning
A few people in this subreddit and the main subreddit have recommended this book. But I have never read it.
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Short list of ML-Ops resources
There is increasing the need for software engineering practices to be applied to ML. This is the field of MLops.
This is a field I want to learn about. A redditor noticed that that the area seems a bit fragmented. And does not know what combinations would be suitable for use case.
I know very little about the topic. But I will provide some links I’ve seen, and I want to use. To learn about the subject.
Full-stack deep learning: A course that focuses on the production side of ML.
DVCorg ML-Ops tutorials: A YouTube playlist showing how to use GitHub actions for ml ops
Github actions for ML-ops: A blog post from GitHub showing how GitHub actions can be used for ML-ops and data science
MLOps Tooling Landscape v2 (+84 new tools) - Dec '20: A decent rundown of the ML-Ops field. (Also follow the author she writes regularly about ML-ops. Chip Huyen)
How to Deploy a Machine Learning Model to Google Cloud for 20% Software Engineers (CS329s tutorial): Example of deploying a model
GitHub for MLOps: Collection of blog posts using GitHub for ML-ops
How to improve software engineering skills as a researcher: A guide showing how to use software engineering tools for your deep learning model.
More Suggestions from Redditors:
Machine Learning Engineering for Production (MLOps) Specialization: Course by Andrew Ng about MLops
Gokul Mohandas Tutorial: MLOps course teaching the beginners about ML-ops
AI Engineering Youtube playlist: Youtube playlist about MLOps
DataTalks.Club DataOps: Podcast episode about DataOps
DataTalks.Club MLOps: Podcast episode about MLOPs
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I haven’t used them personally, but people have given these resources good reviews.
Honestly, I want to release couple of my projects outside a Google Colab notebook. Also learning about ML ops seems helpful for industry experience. (I don’t know, please correct me if I’m wrong)
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