Newsletter exploring AI & ML
- Weekly trends
- LLM/FM insights
- Unicorn spotlights
- Global dynamics
- History
Led by
@kseniase_
Elevate your AI game 👇🏼
CS109A Data Science course materials
@Harvard
are free and open for everyone!
1. Lecture notes
2. R code, Python notebooks
3. Lab material
4. Advanced sections
Learn here:
All algorithms implemented in Python 🤯
This library has 163k stars on GitHub!
It includes a ton of algorithms from arithmetic analysis to blockchain to data structures.
MetaGPT: Simulates a whole software company 🤔
It assigns roles like product managers, architects, project managers, and engineers to GPTs.
With just one line of code, MetaGPT generates user stories, competitive analyses, requirements, data structures, APIs, documents, and
CS109A Data Science course materials
@Harvard
are free and open for everyone!
1. Lecture notes
2. R code, Python notebooks
3. Lab material
4. Advanced sections
Learn here:
A free book for you!
Fundamentals of Data Visualization by Claus O. Wilke
It's a guide to making visualizations that accurately reflect the data, tell a story, and look professional.
Read the open book here:
🔥3 free data science books, the most popular among our subscribers!
1. Fundamentals of Data Visualization by
@ClausWilke
2. Python Data Science Handbook by
@jakevdp
3. Hands-On Data Visualization by
@HandsOnDataViz
Links⬇️
3 free books, the most popular ones!
1. Fundamentals of Data Visualization
2. Hands-On Data Visualization
3. Reinforcement Learning: An Introduction
Share this post with your friends to spread the word!
Links⬇️
CS109A Data Science course materials
@Harvard
are free and open for everyone!
1. Lecture notes
2. R code, Python notebooks
3. Lab material
4. Advanced sections
Learn here:
3 free courses, the most popular ones!
1. CS109A Data Science
@Harvard
2. Linear Algebra
@MIT
3. Mathematics of Big Data and Machine Learning
@MIT
Share this post with your friends to spread the word!
Links⬇️
CS109A Data Science course materials
@Harvard
are free and open for everyone!
1. Lecture notes
2. R code, Python notebooks
3. Lab material
4. Advanced sections
Learn here:
OpenAI CEO, Sam Altman, testified before Congress yesterday for 4 hours.
Don't have the time to watch it all?
We've collected the highlights that are already resonating with people 🧵
TimeGPT is the first foundation model specifically designed for time series analysis.
It excels at generating precise forecasts across a diverse range of datasets and domains.
Here's what you need to know about it:
1/8
3 free books, the most popular ones!
1. Fundamentals of Data Visualization
2. Hands-On Data Visualization
3. Reinforcement Learning: An Introduction
Share this post with your friends to spread the word!
1/2
A free book for you!
Hands-On Data Visualization by Hands-On Data Visualization has an open version.
It takes you step-by-step through
1. tutorials
2. real-world examples
3. online resources
Read here:
Linear Algebra course by
@MIT
is free and open for everyone!
Find:
1. Videos and lecture notes
2. Assignments: problem sets with solutions
3. Exams and solutions
Learn the basics:
A free book for you!
Learn:
1. NumPy & Pandas
2. Matplotlib: data visualizations
3. Scikit-Learn: efficient & clean implementations of ML algorithms
Read the open "Python Data Science Handbook: Essential Tools for Working with Data":
A free book for you!
Learn:
1. NumPy & Pandas
2. Matplotlib: data visualizations
3. Scikit-Learn: efficient & clean implementations of ML algorithms
Read the open "Python Data Science Handbook: Essential Tools for Working with Data":
Linear algebra course materials
@MIT
are free and open for everyone.
1) Lecture videos and slides
2) Homeworks
3) Exams and solutions
Learn linear algebra as a basis for ML. Find materials here:
CS109A Data Science course materials
@Harvard
are free and open to everyone!
1. Lecture notes
2. R code, Python notebooks
3. Lab material
4. Advanced sections
Learn here:
3 free courses, the most popular ones!
1. CS109A Data Science
@Harvard
2. Linear Algebra
@MIT
3. Mathematics of Big Data and Machine Learning
@MIT
Share this post with your friends to spread the word!
Links⬇️
Machine learning is famous for its open resources.
We collected 3 free courses about ML:
▪️ Introduction to Machine Learning,
@MIT
▪️ Mathematics for Computer Science,
@MIT
▪️ Practical Deep Learning,
@fastdotai
🧵
A free book for you!
Learn:
1. NumPy & Pandas
2. Matplotlib: data visualizations
3. Scikit-Learn: efficient & clean implementations of ML algorithms
Read the open "Python Data Science Handbook: Essential Tools for Working with Data":
Mathematics of Big Data and Machine Learning course materials by
@MIT
are free and open for everyone!
Find:
1. Lecture notes
2. Lecture videos
3. Slides
Learn here:
Linear Algebra course by
@MIT
is free and open for everyone!
Find:
1. Videos and lecture notes
2. Assignments: problem sets with solutions
3. Exams and solutions
Learn the basics:
Microsoft presented Azure ChatGPT.
The code is open-sourced under MIT Licence.
Benefits:
1. Privacy: Fully isolated from data operated by OpenAI.
2. Control: Network traffic can be fully isolated to your network
3. Plug-n-play: Add your internal data sources or use plug-ins
A free book for you!
Learn:
1. NumPy & Pandas
2. Matplotlib: data visualizations
3. Scikit-Learn: efficient & clean ML algorithms
Read the open "Python Data Science Handbook: Essential Tools for Working with Data":
"NLP with Deep Learning" by
@stanfordnlp
is free and open!
Find links on:
- lecture slides
- lecture videos
- notes
- codes
- suggested readings
All is here⬇️
A free book for you!
Fundamentals of Data Visualization by Claus O. Wilke
It's a guide to making visualizations that accurately reflect the data, tell a story, and look professional.
Read the open book here:
"NLP with Deep Learning" by
@stanfordnlp
is free and open!
Find links on:
- lecture slides
- lecture videos
- notes
- codes
- suggested readings
All is here⬇️
Happy Holidays, friends!🥳
We've made a recap of the most popular courses for you.
1. Deep Learning by
@ylecun
,
@alfcnz
2. Linear Algebra
@MIT
3. Deep Learning for NLP
@DeepMind
,
@CompSciOxford
Find links in the thread!
1/5
Download your copy of Understanding Machine Learning: From Theory to Algorithms book by Shai Shalev-Shwartz and Shai Ben-David.
It introduces the reader to ML: from fundamental theoretical ideas to maths behind practical algorithms.
🔗
Check out a free edition of Think Bayes by Allen Downey.
1. This book uses Python code instead of math!
2. There’s a Jupyter notebook for every chapter
3. Code is written in NumPy, SciPy, and Pandas
Find it here for free:
Mathematics of Big Data and Machine Learning course materials by
@MIT
are free and open for everyone!
Find:
1. Lecture notes
2. Lecture videos
3. Slides
Learn here:
Linear Algebra course by
@MIT
is free and open to everyone!
Find:
1. Videos and lecture notes
2. Assignments: problem sets with solutions
3. Exams and solutions
Learn the basics here:
A free book for you!
Learn:
1. NumPy & Pandas
2. Matplotlib: data visualizations
3. Scikit-Learn: efficient implementations of ML algorithms
Read the open "Python Data Science Handbook: Essential Tools for Working with Data":
"Deep Learning for Natural Language Processing" course lectures are open for everyone!
It's an advanced course from
@CompSciOxford
and
@DeepMind
.
All lecture materials, recordings, and practicals:
A free book for you!
Learn:
1. NumPy & Pandas
2. Matplotlib: data visualizations
3. Scikit-Learn: efficient & clean implementations of ML algorithms
Read the open "Python Data Science Handbook: Essential Tools for Working with Data":
A free book for you!
Hands-On Data Visualization by Hands-On Data Visualization has an open version.
It takes you step-by-step through
1. tutorials
2. real-world examples
3. online resources
Read here:
Machine learning is famous for its open resources.
We collected 3 free courses about ML:
▪️ Introduction to Machine Learning,
@MIT
▪️ Mathematics for Computer Science,
@MIT
▪️ Practical Deep Learning,
@fastdotai
🧵
"NLP with Deep Learning" by
@stanfordnlp
is free and open!
Find links on:
1. lecture slides & videos
2. notes
3. codes
4. suggested readings
All is here⬇️
"Deep Learning for Natural Language Processing" course lectures are open for everyone!
It's an advanced course from
@CompSciOxford
and
@DeepMind
.
All lecture materials, recordings, and practicals:
Recently, PyTorch introduced mm, a 3D matrix multiplication (matmul) visualizer.
This tool is interactive, runs in the browser or notebook iframes, and features shareable links.
You can visualize:
• Matrix multiplications
• Attention heads
• LORA
Some examples:
A free book for you!
"Foundations of Data Science" by Avrim Blum, John Hopcroft, and Ravindran Kannan.
It provides an introduction to the mathematical and algorithmic foundations of data science.
Find it here:
A free book for you!
"Foundations of Data Science" by Avrim Blum, John Hopcroft, and Ravindran Kannan.
It provides an introduction to the mathematical and algorithmic foundations of data science.
Find it here:
"NLP with Deep Learning" by
@stanfordnlp
is free and open!
Find links on:
1. lecture slides & videos
2. notes
3. codes
4. suggested readings
All is here⬇️
Mathematics for Computer Science course
@MIT
is open for everyone!
1. Definitions, proofs, sets, functions, relations
2. Discrete structures: graphs, state machines, modular arithmetic, counting
3. Discrete probability theory
A free book for you!
Learn:
1. NumPy & Pandas
2. Matplotlib: data visualizations
3. Scikit-Learn: efficient & clean ML algorithms
Read the open "Python Data Science Handbook: Essential Tools for Working with Data":
Python for Data Science, AI & Development is a free course by
@IBM
.
It will take you from 0 to programming in Python.
It consists of 4 weeks:
1. Basics
2. Data Structures
3. Programming Fundamentals
4. Working with Data
Learn here:
A free book for you!
Hands-On Data Visualization,
@HandsOnDataViz
has an open version.
It takes you step-by-step through
1. tutorials
2. real-world examples
3. online resources
Read here:
8 free courses to master large language models:
- Cohere LLM University
- Hugging Face NLP course
- DeepLearning AI courses
- Weights & Biases course
- Introduction to LLMs course by Google Cloud
...
🧵
Microsoft, MIT, and Google collaborated to transform the entire Project Gutenberg Collection into audiobooks.
The library now boasts over 800 audiobooks!
Utilizing advanced AI models for text-to-audio conversion, the team achieved exceptional quality of voice acting.
Large-Scale Automatic Audiobook Creation
paper page:
An audiobook can dramatically improve a work of literature's accessibility and improve reader engagement. However, audiobooks can take hundreds of hours of human effort to create, edit, and publish. In
Linear Algebra course by
@MIT
is free and open for everyone!
Find:
1. Videos and lecture notes
2. Assignments: problem sets with solutions
3. Exams and solutions
Learn the basics:
ML Research of this week:
1.
@MetaAI
Data2vec
2.
@GoogleAI
MoE Task Routing
3.
@MSFTResearch
DeepSpeed and MoE
4.
@GoogleAI
StylEx – Visual Interpretability of Classifiers
Find details here:
If you're in ML, chances are you completed the
@AndrewYNg
ML specialization.
But what if we told you there's an incredible resource to help you refresh your knowledge? Or even pass the course from scratch?
Notes and implementations of algorithms
Check out the open edition of Think Bayes by
@AllenDowney
.
1. This book uses Python code instead of math!
2. There’s a Jupyter notebook for every chapter
3. Code is written in NumPy, SciPy, and Pandas
Find it here for free:
"NLP with Deep Learning" by
@stanfordnlp
is free and open!
Find links on:
1. lecture slides & videos
2. notes
3. codes
4. suggested readings
All is here⬇️
If you're in ML, chances are you completed the
@AndrewYNg
ML specialization.
But what if we told you there's an incredible resource to help you refresh your knowledge? Or even pass the course from scratch?
Notes and implementations of algorithms
We recommend you a free Learning From Data course with 7M views
@Caltech
.
It covers: basic ML theory, algorithms, and applications
Find the materials:
1. Lectures
2. Homework materials & solution keys.
Learn here:
Free book on Statistics!
Introduction to Modern Statistics covers:
1. Exploratory data analysis
2. Regression modeling
3. Statistical inference
Read here:
"NLP with Deep Learning" by
@stanfordnlp
is free and open!
Find links on:
- lecture slides
- lecture videos
- notes
- codes
- suggested readings
All is here⬇️
We recommend you "Learning From Data" course (
@telecourse
)
@Caltech
.
It covers: basic ML theory, algorithms, and applications
Find the materials of the course with 7M views:
1. Lectures
2. Homework materials
There is a solution key for each homework!
A FREE tutorial on diffusion models and flow matching for ML!
Created by
@Apple
, Mila and
@UMontreal
, this tutorial simplifies the math but ensures precision for deriving correct algorithms.
Audience: beginners in diffusion with a technical background, such as advanced
Introduction to Machine Learning
@MIT
is available to anyone!
1. Understand the formulation of ML problems
2. Learn how to perform supervised and reinforcement learning, with images and temporal sequences
Learn here:
Math background for ML lecture videos
@CarnegieMellon
are available for everyone!
The course provides the necessary mathematical background for further study in ML.
Find videos here:
Happy holidays, friends!☺
"Deep Learning for Natural Language Processing" course lectures are open for everyone!
It's an advanced course from
@CompSciOxford
,
@DeepMind
taught by Phil Blunsom.
All lecture materials, recordings, and practicals:
Introduction to Deep Learning course by
@TU_Muenchen
is free and open for everyone!
Find course materials by
@MattNiessner
:
1) Lecture slides
2) Recordings
3) Tutorials and exercises
Discover it here:
3 free books, the most popular ones!
1. Fundamentals of Data Visualization
2. Hands-On Data Visualization
3. Reinforcement Learning: An Introduction
Share this post with your friends to spread the word!
1/2
Autonomous AI is a new trend.
2 repos were highly talked about everywhere
▪️ Auto-GPT
@SigGravitas
▪️ BabyAGI
@yoheinakajima
Original tweets, experiments, and opinions 🧵
Prepare to be amazed! 🤯 This library contains a vast collection of algorithms implemented in Python.
With an astonishing 171k stars on GitHub, it covers:
- Arithmetic Analysis
- Blockchain
- Data Structures
And much, much more.
Introduction to Machine Learning course materials from
@CarnegieMellon
are available to everyone!
▪️ Lecture slides and useful links
▪️ Handouts
▪️ Homework assignments
🔗
If you're in ML, chances are you completed the
@AndrewYNg
ML specialization.
But what if we told you there's an incredible resource to help you refresh your knowledge? Or even pass the course from scratch?
Notes and implementations of algorithms
Meet 🦍Gorilla: LLM connected with massive APIs
With Gorilla, you can effortlessly navigate over 1,600 API calls with minimal error!
It surpasses the performance of GPT-4 when it comes to making API invocations.
What's even better? You are free to use Gorilla for commercial
A free book for you!
The Data Science Handbook is a compilation of in-depth interviews with 25 data scientists.
The book was created by
@wzchen
, Carl Shan, Henry Wang, Max Song.
You can pay for ebook or download it for free: