Learn Anything Online

139 Best Artificial Intelligence Resources

Learn Anything Online offers the best resources for learning about artificial intelligence. From fundamentals to advanced concepts, explore a curated collection of courses, tutorials, papers and more to enhance your artificial intelligence education.

Free

30 days ago

countless.dev
Ahmet Dedeler
See and compare every AI model easily. 100% free & open-source.
Free

Last month

Spyware Injection Into Your ChatGPT's Long-Term Memory (SpAIware)
Johann Rehberger
This post explains an attack chain for the ChatGPT macOS application. Through prompt injection from untrusted data, attackers could insert long-term persistent spyware into ChatGPT’s memory. This led to continuous data exfiltration of any information the user typed or responses received by ChatGPT, including any future chat sessions.
Free

3 months ago

Mnist Network Visualization
Bones
A visualization of an MNIST neural network written from scratch in Odin and Raylib. This project provides a 3D visualisation of how a neural network works for the famous handwriting detection problem.
Free

3 months ago

The other way to visualize derivatives | Chapter 12, Essence of calculus
3Blue1Brown
Grant discusses the transformative way of thinking about derivatives in calculus and its applications in understanding complex mathematical concepts. Understanding derivatives as transformations rather than mere slopes can revolutionize calculus learning and application.
Free

3 months ago

Taylor series | Chapter 11, Essence of calculus
3Blue1Brown
Grant discusses the application and importance of Taylor series in mathematics and physics, illustrating its utility in simplifying complex functions. Understanding Taylor series allows for effective approximation and deeper insights into complex functions.
Free

3 months ago

Higher order derivatives | Chapter 10, Essence of calculus
3Blue1Brown
Grant discusses higher-order derivatives, focusing on the second derivative, its implications in graph slopes, motion, and acceleration. Understanding second derivatives through graph slopes and motion provides deeper insights into acceleration and function behavior.
Free

3 months ago

What does area have to do with slope? | Chapter 9, Essence of calculus
3Blue1Brown
Grant discusses the use of integration to find the average value of continuous variables, illustrating with the sin(x) function. Understanding integration and its relationship with derivatives unlocks insights into continuous variables and natural phenomena.
Free

3 months ago

Integration and the fundamental theorem of calculus | Chapter 8, Essence of calculus
3Blue1Brown
Grant discusses integrals, their relationship with derivatives, and how to use them to solve problems involving variable speeds. Integrals, by measuring areas under curves, simplify calculations and enhance understanding of variable rates.
Free

3 months ago

Limits, L'Hôpital's rule, and epsilon delta definitions | Chapter 7, Essence of calculus
3Blue1Brown
Grant discusses the concept of limits in calculus, explaining its relation to derivatives and L'Hopital's rule.
Free

3 months ago

Implicit differentiation, what's going on here? | Chapter 6, Essence of calculus
3Blue1Brown
Implicit differentiation can feel strange, but thought of the right way it makes a lot of sense. Understanding limits through the epsilon-delta definition enhances foundational calculus concepts and practical applications.
Free

3 months ago

What's so special about Euler's number e? | Chapter 5, Essence of calculus
3Blue1Brown
Grant discusses the derivatives of exponential functions, specifically focusing on how the exponential function 2^t can represent population mass and its growth over time. Understanding exponential functions and their derivatives offers profound insights into natural growth processes.
Free

3 months ago

Visualizing the chain rule and product rule | Chapter 4, Essence of calculus
3Blue1Brown
A visual explanation of what the chain rule and product rule are, and why they are true. Grant discusses the concept of derivatives, emphasizing understanding over memorization, and exploring function composition.
Free

3 months ago

Derivative formulas through geometry | Chapter 3, Essence of calculus
3Blue1Brown
A visual explanation of derivative formulas through geometry. Grant explains how to find derivatives for different functions like polynomials, trigonometric, exponential, and illustrates with geometric interpretations. Understanding derivatives through geometric visualizations bridges abstract concepts with practical applications.
Free

3 months ago

The paradox of the derivative | Chapter 2, Essence of calculus
3Blue1Brown
What is an "instantaneous rate of change" when change happens across time? Grant explains the concept of derivatives and the logical contradictions involved.
Free

3 months ago

The essence of calculus
3Blue1Brown
In the first video of a series on the essence of calculus, Grant explores foundational concepts, using visual methods to explain how ideas in calculus, like differentiation and integration, are interconnected and can be conceptually reinvented. Understanding calculus through visualization and fundamental concepts can make anyone feel capable of inventing it.
Free

3 months ago

How might LLMs store facts | Chapter 7, Deep Learning
3Blue1Brown
Unpacking the multilayer perceptrons in a transformer, and how they may store facts. Grant explores the inner workings of large language models, specifically focusing on how they predict and encode information. Understanding the storage and retrieval of facts in AI relies on complex neural architectures and high-dimensional spaces.
Free

3 months ago

Attention in transformers, visually explained | Chapter 6, Deep Learning
3Blue1Brown
Demystifying attention, the key mechanism inside transformers and LLMs. Grant delves into the inner workings of transformers in large language models, focusing on the attention mechanism and its data processing capabilities.
Free

3 months ago

How large language models work, a visual intro to transformers | Chapter 5, Deep Learning
3Blue1Brown
Grant provides an in-depth visual explanation of transformers in AI, discussing their structure, function, and the process of generating text using GPT models. Transformers use attention mechanisms and vast data to revolutionize text generation in AI.
Free

3 months ago

Backpropagation calculus | Chapter 4, Deep learning
3Blue1Brown
Grant discusses the application of calculus in machine learning, specifically the back propagation algorithm and its calculations. Back propagation is essential for optimizing neural networks through detailed calculus applications.
Free

3 months ago

What is backpropagation really doing? | Chapter 3, Deep learning
3Blue1Brown
What's actually happening to a neural network as it learns? Grant discusses back propagation, a key algorithm behind how neural networks learn, using the example of recognizing handwritten numbers.
Free

3 months ago

Gradient descent, how neural networks learn | Chapter 2, Deep learning
3Blue1Brown
A visual explanation of how neural networks learn through a process called gradient descent. Understanding and optimizing neural networks involves mastering gradient descent, cost functions, and network architectures.
Free

3 months ago

But what is a neural network? | Chapter 1, Deep learning
3Blue1Brown
What are the neurons, why are there layers, and what is the math underlying it? Understanding and adjusting neural network structures and functions is crucial for effective pattern recognition and learning.
Free

3 months ago

Ordinary Least Squares Regression
Victor Powell
An interactive explanation of how Ordinary Least Squares Regression works. This is widely used for predictive modeling in machine learning.
Free

3 months ago

Markov Chains
Victor Powell
A visual explanation of how Markov Chains work. They are fundamental to generative AI and employed to generate sequences of data, such as text, music, or images, by modeling the probability of transitioning from one state to another.
Free

3 months ago

Conditional probability
Victor Powell
A visual and interactive explanation of how conditional probability works.
Free

3 months ago

Model Tuning and the Bias-Variance Tradeoff
R2D3
Part 2 of a visual introduction to machine learning covering model tuning and the bias-variance trade-off.
Free

3 months ago

A visual introduction to machine learning
R2D3
An interactive introduction to machine learning and decision trees which comes to life as you scroll.
Free

3 months ago

Image Kernels
Victor Powell
Visual explanation of image kernels which are used in machine learning for 'feature extraction', a technique for determining the most important portions of an image.
Free

3 months ago

LLM University
Cohere
A visual course on the fundamentals of LLMs including text representation, text generation, semantic search and prompt engineering.
Free

3 months ago

LLM Visualization
Brendan Bycroft
A visualization and walkthrough of LLM algorithm that backs OpenAI’s ChatGPT. Explore the algorithm down to every add & multiply, seeing the whole process in action.
Free

3 months ago

The Narrated Transformer Language Model
Jay Alammar
A gentle introduction to the Transformer architecture and its various applications. Understanding and utilizing transformer models are crucial as they increasingly underpin advanced AI applications.
Free

3 months ago

How AI Learns Concepts
Art of the Problem
This video unravels the mystery behind how machines interpret input data, such as images or sounds, and categorize them into recognizable concepts.
Free

3 months ago

How AIs, like ChatGPT, Learn
CGP Grey
How do all the algorithms, like ChatGPT, around us learn to do their jobs? Algorithms dominate online experiences, yet their intricate operations remain largely undisclosed and misunderstood.
Free

3 months ago

Too much efficiency makes everything worse: overfitting and the strong version of Goodhart’s law
Jascha Sohl-Dickstein
Over-optimizing efficiency worsens the very goals it aims to achieve, known as Goodhart's law.
Free

4 months ago

Low Level Technicals of LLMs: Daniel Han
Daniel Han
This workshop covers how to analyze and fix LLMs, finetuning with Unsloth and a deep dive into LLM technicals.
Free

4 months ago

Web Scraping with GPT-4 Vision AI + Puppeteer is Mind-Blowingly EASY!
ByteGrad
Learn how to scrape web pages more easily by utilising GPT-4, GPT-4 Vision and Puppeteer.
Free

4 months ago

Full Stack LLM Bootcamp
The Full Stack
A practical course for building LLM-based applications with Charles Frye, Sergey Karayev, and Josh Tobin.
Free

4 months ago

RLHF: Reinforcement Learning from Human Feedback
Chip Huyen
Incorporating reinforcement learning with human feedback into NLP at a massive scale.
Free

4 months ago

How Stable Diffusion Works
Chris McCormick
Learn how Stable Diffusion uses complex equations and machine learning to generate art from noise.
Free

4 months ago

The Illustrated Transformer
Jay Alammar
A visual and simplified exploration of the Transformer model in machine learning.
Free

4 months ago

Software 2.0
Andrej Karpathy
Software 2.0 signifies a foundational shift in how software is developed using neural networks.
Free

4 months ago

Deep Learning in a Nutshell: Core Concepts
NVIDIA Technical Blog
This post provides an intuitive introduction to the core concepts of deep learning. It covers the most important deep learning concepts and aims to provide an understanding of each concept rather than its mathematical and theoretical details. While the mathematical terminology is sometimes necessary and can further understanding, these posts use analogies and images whenever possible to provide easily digestible bits comprising an intuitive overview of the field of deep learning.
Free

4 months ago

Getting Started with Large Language Models: Key Things to Know
Sebastian Raschka · Niels Bantilan
An introductory guide to LLMs captures the evolving ecosystem and practical application.
Free

4 months ago

Vector Databases
Jide Ogunjobi
A technical primer on vector databases. This course covers vector database core concepts, understanding search similarity, indexing and querying, and more.
Free

4 months ago

Catching up on the weird world of LLMs
Simon Willison
Summarizing recent developments and ethical concerns in the field of large language models.
Free

4 months ago

bleeding edge
Lachy
A feed of noteworthy developments in AI.
Free

4 months ago

Best 100 Stable Diffusion Prompts: The Most Beautiful AI Text-to-Image Prompts | Metaverse Post
Kenth Bennett
Discover the top 100+ Stable Diffusion prompts for creating stunning AI-generated images.
Free

4 months ago

ChatGPT Explained: A Normie's Guide To How It Works
Jon Stokes
This article demystifies modern chatbots by dissecting how ChatGPT works.
Free

4 months ago

How to write good prompts: using spaced repetition to create understanding
Andy Matuschak
Guide on writing effective prompts using spaced repetition to enhance memory and understanding.
Free

4 months ago

Prompt Engineering 101
Raza Habib · Sinan Ozdemir
Explore the fundamentals of prompt engineering for optimizing Large Language Model outputs.
Free

4 months ago

The brief history of artificial intelligence: the world has changed fast — what might be next?
Max Roser
Little is as important for the world’s future and our own lives as how this history continues.
Free

4 months ago

Opportunities in AI
Andrew Ng
Andrew Ng discusses AI as a general-purpose technology, highlighting supervised learning and generative AI's impact and opportunities. AI, akin to electricity, is revolutionizing diverse sectors with supervised learning and generative AI, promising vast opportunities yet requiring ethical oversight.
Free

4 months ago

Generative AI exists because of the transformer
Financial Times
A visual explanation of how generative AI works and how hallucinations occur.
Free

4 months ago

Prompt Engineering Best Practices
Sarah Chieng
Current practices and prompting tricks to improve LLM output.
Free

4 months ago

Large Language Model Course
Maxime Labonne
Course to get into Large Language Models (LLMs) with roadmaps and Colab notebooks. This course is divided into three parts: 1. LLM Fundamentals covers essential knowledge about mathematics, Python, and neural networks. 2. The LLM Scientist focuses on building the best possible LLMs using the latest techniques. 3. The LLM Engineer focuses on creating LLM-based applications and deploying them. For an interactive version of this course, Maxime has created two LLM assistants that will answer questions and test your knowledge in a personalized way.
Free

4 months ago

Dive Into Deep Learning
Aston Zhang · Zachary C. Lipton · Mateusz Malinowski · Alexander J. Smola
Interactive deep learning book with code, math, and discussions. Implemented with PyTorch, NumPy/MXNet, JAX, and TensorFlow. Adopted at 500 universities from 70 countries.
Free

4 months ago

Neural Networks: Zero to Hero
Andrej Kaparthy
A course by Andrej Karpathy on building neural networks, from scratch, in code. We start with the basics of backpropagation and build up to modern deep neural networks, like GPT. In my opinion language models are an excellent place to learn deep learning, even if your intention is to eventually go to other areas like computer vision because most of what you learn will be immediately transferable. This is why we dive into and focus on languade models.
$65.99

4 months ago

Designing Machine Learning Systems
Chip Huyen
An Iterative Process for Production-Ready Applications. Author Chip Huyen, co-founder of Claypot AI, considers each design decision - such as how to process and create training data, which features to use, how often to retrain models, and what to monitor - in the context of how it can help your system as a whole achieve its objectives.
Free

4 months ago

Full Stack Deep Learning Bootcamp
The Full Stack
Design, train, and deploy models from A to Z. This is also a great resource for those struggling with the machine learning system design questions in interviews.
Free

4 months ago

Reinforcement Learning
Google DeepMind
Introduction to RL with intuitive explanations and fun examples, taught by one of the world’s leading RL experts.
Free

4 months ago

Probabilistic Graphical Models Specialization
Standford University
Probabilistic Graphical Models. Master a new way of reasoning and learning in complex domains.
Free

4 months ago

CS224N: Natural Language Processing with Deep Learning
Standford University
Taught by one of the most influential (and most down-to-earth) researcher, Christopher Manning, this is must-take course for anyone interested in natural language processing. The course is well organized, well taught, and up-to-date with the latest NLP research.
Free

4 months ago

Linear Algebra
MIT
One of the best linear algebra courses taught by the legendary professor Gilbert Strang. This is a basic subject on matrix theory and linear algebra. Emphasis is given to topics that will be useful in other disciplines, including systems of equations, vector spaces, determinants, eigenvalues, similarity, and positive definite matrices.
Free

4 months ago

Introduction to Statistics
Standford University
Self-paced course covers basic concepts in probability and statistics spanning over four fundamental aspects of machine learning: exploratory data analysis, producing data, probability, and inference.
Free

4 months ago

Hugging Face Blog
Hugging Face
The blog of Hugging Face, a leading open-source platform for machine learning (ML) and natural language processing (NLP) that provides a suite of tools, libraries, and models. It is best known for its Transformers library, which simplifies the implementation of state-of-the-art NLP models like GPT, BERT, and T5. Hugging Face offers easy access to pre-trained models for tasks such as text classification, text generation, translation, and more.
Free

4 months ago

Generative AI Handbook
William Brown
This document aims to serve as a handbook for learning the key concepts underlying modern artificial intelligence systems. Given the speed of recent development in AI, there really isn’t a good textbook-style source for getting up-to-speed on the latest-and-greatest innovations in LLMs or other generative models, yet there is an abundance of great explainer resources (blog posts, videos, etc.) for these topics scattered across the internet. The goal is to organize the “best” of these resources into a textbook-style presentation, which can serve as a roadmap for filling in the prerequisites towards individual AI-related learning goals.
Free

4 months ago

Computer Vision Models
Simon J.D. Prince
A principled model-based approach to computer vision that unifies disparate algorithms, approaches, and topics under the guiding principles of probabilistic models, learning, and efficient inference algorithms.
Free

4 months ago

Understanding Deep Learning
Simon J.D. Prince
An authoritative, accessible, and up-to-date treatment of deep learning that strikes a pragmatic middle ground between theory and practice.
Free

4 months ago

AI Today
Cognilytica
The top podcast for those wanting no-hype, practical, real-world insight into what enterprises, public sector agencies, thought leaders, leading technology companies, pundits, and experts are doing with AI today.
Free

4 months ago

Data Skeptic
Kyle Polich
Data Skeptic is your source for a perspective of scientific skepticism on topics in statistics, machine learning, big data, artificial intelligence, and data science. The weekly podcast and blog bring you stories and tutorials to help understand our data-driven world.
$14.73

4 months ago

Life 3.0
Max Tegmark
A thought-provoking examination of the potential impact of advanced AI on the future of humanity. How will Artificial Intelligence affect crime, war, justice, jobs, society and our very sense of being human? The rise of AI has the potential to transform our future more than any other technology - and there’s nobody better qualified or situated to explore that future than Max Tegmark, an MIT professor who’s helped mainstream research on how to keep AI beneficial.
$20.00

4 months ago

The Alignment Problem
Brian Christian
An exploration of the challenges in aligning artificial intelligence systems with human values and goals.
$40.00

4 months ago

Artificial Intelligence in Practice
Bernard Marr · Matt Ward
A collection of case studies showcasing how AI is being applied in various industries and domains.
Free

4 months ago

simple.ai
Dharmesh Shah
Learn how to use AI agents to grow your career or business. Newsletter by Dharmesh Shah, the cofounder and chief technological officer of cloud-based marketing software startup HubSpot.
Free

4 months ago

AI in Business Podcast
Daniel Faggella
A podcast for non-technical business leaders who need to find AI opportunities, align AI capabilities with strategy, and deliver ROI.
$29.99

4 months ago

The Master Algorithm
Pedro Domingos
An exploration of the quest to find the “master algorithm” that can solve all machine learning problems.
$54.99

4 months ago

Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow
Aurélien Géron
A practical guide to implementing machine learning techniques using popular Python libraries.
$33.99

4 months ago

Superintelligence: Paths, Dangers, Strategies
Nick Bostrom
A thought-provoking examination of the potential risks and implications of advanced artificial intelligence.
$20.00

4 months ago

The Hundred-Page Machine Learning Book
Andriy Burkov
A concise and accessible introduction to the core principles and algorithms of machine learning.
Free

4 months ago

Deep Learning
Ian Goodfellow · Yoshua Bengio · Aaron Courville
An in-depth exploration of the theory and practice of deep learning, a powerful subset of machine learning.
Free

4 months ago

Building LLMs from the Ground Up: A 3-hour Coding Workshop
Sebastian Raschka
Understand and build LLMs from scratch in PyTorch.
Free - $250.00

5 months ago

Ben’s Bites
Ben Tossell · Adam Tossell · Keshav Jindal · Shanice Stewart-Jones
Bite-sized education to boost your AI knowledge - from beginner to pro. Learn through courses, workshops and their blog posts.
Free

5 months ago

Kolmogorov Complexity and Algorithmic Randomness
A. Shen · V. A. Uspensky · N. Vereshchagin
Suggested reading from Ilya Sutskever, OpenAI’s chief scientist, for John Carmack.
Free

5 months ago

Machine Super Intelligence
Shane Legg
Suggested reading from Ilya Sutskever, OpenAI’s chief scientist, for John Carmack.
Free

5 months ago

A tutorial introduction to the minimum description length principle
Peter Grünwald
Suggested reading from Ilya Sutskever, OpenAI’s chief scientist, for John Carmack.
Free

5 months ago

Scaling Laws for Neural Language Models
Jared Kaplan · Sam McCandlish · Tom Henighan · Tom B. Brown · Benjamin Chess · Rewon Child · Scott Gray · Alec Radford · Jeffrey Wu · Dario Amodei
Suggested reading from Ilya Sutskever, OpenAI’s chief scientist, for John Carmack.
Free

5 months ago

Deep Speech 2: End-to-End Speech Recognition in English and Mandarin
Andrew Ng · Dario Amodei · Rishita Anubhai · Eric Battenberg · Carl Case · Jared Casper · Bryan Catanzaro · Jingdong Chen · Mike Chrzanowski · Adam Coates · Greg Diamos · Erich Elsen · Jesse Engel · Linxi Fan · Christopher Fougner · Tony Han · Awni Hannun · Billy Jun · Patrick LeGresley · Libby Lin · Sharan Narang · Sherjil Ozair · Ryan Prenger · Jonathan Raiman · Sanjeev Satheesh · David Seetapun · Shubho Sengupta · Yi Wang · Zhiqian Wang · Chong Wang · Bo Xiao · Dani Yogatama · Jun Zhan · Zhenyao Zhu
Suggested reading from Ilya Sutskever, OpenAI’s chief scientist, for John Carmack.
Free

5 months ago

Neural Turing Machines
Alex Graves · Greg Wayne · Ivo Danihelka
Suggested reading from Ilya Sutskever, OpenAI’s chief scientist, for John Carmack.
Free

5 months ago

Quantifying the Rise and Fall of Complexity in Closed Systems: The Coffee Automation
Scott Aaronson · Sean M. Carroll · Lauren Ouellete
Suggested reading from Ilya Sutskever, OpenAI’s chief scientist, for John Carmack.
Free

5 months ago

Relational recurrent neural networks
Mike Chrzanowski · Adam Santoro · Ryan Faulkner · David Raposo · Jack Rae · Théophane Weber · Daan Wierstra · Oriol Vinyals · Razvan Pascanu · Timothy Lillicrap
Suggested reading from Ilya Sutskever, OpenAI’s chief scientist, for John Carmack.
Free

5 months ago

Variational Lossy Autoencoder
Xi Chen · Diederik P. Kingma · Tim Salimans · Yan Duan · Prafulla Dhariwal · John Schulman · Ilya Sutskever · Pieter Abbeel
Suggested reading from Ilya Sutskever, OpenAI’s chief scientist, for John Carmack.
Free

5 months ago

A simple neural network module for relational reasoning
Mateusz Malinowski · Adam Santoro · David Raposo · Timothy Lillicrap · David G.T. Barrett · Rasvan Pascanu · Peter Battaglia
Suggested reading from Ilya Sutskever, OpenAI’s chief scientist, for John Carmack.
Free

5 months ago

Identity Mappings in Deep Residual Networks
Kaiming He · Xiangyu Zhang · Shaoqing Ren · Jian Sun
Suggested reading from Ilya Sutskever, OpenAI’s chief scientist, for John Carmack.
Free

5 months ago

Neural Machine Translation by Jointly Learning to Align and Translate
Yoshua Bengio · Dzmitry Bahdanau · KyungHyun Cho
Suggested reading from Ilya Sutskever, OpenAI’s chief scientist, for John Carmack.
Free

5 months ago

Attention Is All You Need
Ashish Vaswani · Noam Shazeer · Niki Parmar · Jakob Uszkoreit · Llion Jones · Aidan N. Gomez · Łukasz Kaiser · Illia Polosukhin
Suggested reading from Ilya Sutskever, OpenAI’s chief scientist, for John Carmack.
Free

5 months ago

Neural Message Passing for Quantum Chemistry
Oriol Vinyals · Justin Gilmer · Samuel S. Schoenholz · Patrick F. Riley · George E. Dahl
Suggested reading from Ilya Sutskever, OpenAI’s chief scientist, for John Carmack.
Free

5 months ago

Multi-Scale Context Aggregation by Dilated Convolutions
Fisher Yu · Vladlen Koltun
Suggested reading from Ilya Sutskever, OpenAI’s chief scientist, for John Carmack.
Free

5 months ago

Deep Residual Learning for Image Recognition
Kaiming He · Xiangyu Zhang · Shaoqing Ren · Jian Sun
Suggested reading from Ilya Sutskever, OpenAI’s chief scientist, for John Carmack.
Free

5 months ago

CS231n Convolutional Neural Networks for Visual Recognition
Standford University
Suggested reading from Ilya Sutskever, OpenAI’s chief scientist, for John Carmack.
Free

5 months ago

GPipe: Efficient Training of Giant Neural Networks using Pipeline Parallelism
Yanping Huang · Youlong Cheng · Ankur Bapna · Orhan Firat · Mia Xu Chen · Dehao Chen · HyoukJoong Lee · Jiquan Ngiam · Quoc V. Le · Yonghui Wu · Zhifeng Chen
Suggested reading from Ilya Sutskever, OpenAI’s chief scientist, for John Carmack.
Free

5 months ago

Order Matters: Sequence to sequence for sets
Oriol Vinyals · Samy Bengio · Manjunath Kudlur
Suggested reading from Ilya Sutskever, OpenAI’s chief scientist, for John Carmack.
Free

5 months ago

ImageNet Classification with Deep Convolutional Neural Networks
Ilya Sutskever · Alex Krizhevsky · Geoffrey E. Hinton
Suggested reading from Ilya Sutskever, OpenAI’s chief scientist, for John Carmack.
Free

5 months ago

Pointer Networks
Oriol Vinyals · Meire Fortunato · Navdeep Jaitly
Suggested reading from Ilya Sutskever, OpenAI’s chief scientist, for John Carmack.
Free

5 months ago

Keeping NN Simple by Minimizing the Description Legnth of the Weights
Geoffrey E. Hinton · Drew van Camp
Suggested reading from Ilya Sutskever, OpenAI’s chief scientist, for John Carmack.
Free

5 months ago

Recurrent Neural Network Regularization
Oriol Vinyals · Ilya Sutskever · Wojciech Zaremba
Suggested reading from Ilya Sutskever, OpenAI’s chief scientist, for John Carmack.
Free

5 months ago

Understanding LSTM Networks -- colah's blog
Christopher Olah
LSTMs are crucial for learning long-term dependencies in recurrent neural networks.
Free

5 months ago

The Unreasonable Effectiveness of Recurrent Neural Networks
Andrei Karpathy
Explore the magical outputs and robustness of Recurrent Neural Networks in various data formats.
Free

5 months ago

The First Law of Complexodynamics
Scott Aaronson
Discusses the complexity and entropy relationship in a physical systems' evolution.
Free

5 months ago

The Annotated Transformer
Sasha Rush · Austin Huang · Suraj Subramanian · Jonathan Sum · Khalid Almubarak · Stella Biderman
Annotated version of a transformer with comments adds explanation to the model architecture.
$29.00 - $95.00

5 months ago

Neural Networks from Scratch
Harrison Kinsley · Daniel Kukiela
A book intended to teach you how to build neural networks on your own, without any libraries, so you can better understand deep learning and how all of the elements work. This is so you can go out and do new/novel things with deep learning as well as to become more successful with even more basic models.
Free

5 months ago

Spinning Up
OpenAI
An educational resource produced by OpenAI that makes it easier to learn about deep reinforcement learning (deep RL).
Free

5 months ago

Artificial Intelligence for Beginners
Microsoft
A 12-week, 24-lesson curriculum for Artificial Intelligence from Microsoft. It includes practical lessons, quizzes, and labs. The curriculum is beginner-friendly and covers tools like TensorFlow and PyTorch, as well as ethics in AI.
Free

5 months ago

Neural networks
3Blue1Brown
Learn the basics of neural networks and backpropagation, one of the most important algorithms for the modern world.
Free

5 months ago

Essence of linear algebra
3Blue1Brown
A free course offering the core concept of linear algebra with a visuals-first approach.
Free

5 months ago

Essence of calculus
3Blue1Brown
A free course offering the core concept of Calculus, with a visuals-first approach aimed at making you feel like you could have discovered the subject yourself.
Free

5 months ago

Build Club
Annie Liao
The training campus for top AI learners, experts, builders.
Free

5 months ago

Kaggle
Kaggle
Kaggle is a popular online platform for data science and machine learning competitions, as well as a community for data enthusiasts, professionals, and researchers. It provides a wide range of datasets, notebooks, and public code to help users learn and develop their skills in data science and machine learning.
Free - $20.00 / month

5 months ago

Hugging Face
Hugging Face
Hugging Face is a leading open-source platform for machine learning (ML) and natural language processing (NLP) that provides a suite of tools, libraries, and models. It is best known for its Transformers library, which simplifies the implementation of state-of-the-art NLP models like GPT, BERT, and T5. Hugging Face offers easy access to pre-trained models for tasks such as text classification, text generation, translation, and more.
Free

5 months ago

The AI Podcast
NVIDIA
NVIDIA podcast exploring AI one person at a time - from the wildlife biologist tracking endangered rhinos across the savannah here on Earth to astrophysicists analyzing 10 billion-year-old starlight in distant galaxies to the Walmart data scientist grappling with the hundreds of millions of parameters lurking in the retailer’s supply chain.
$75.00

5 months ago

AWS Certified AI Practitioner
Amazon Web Services
This AWS certification cover Fundamentals of AI and ML (20%), Fundamentals of Generative AI (24%), Applications of Foundation Models (28%), Guidelines for Responsible AI (14%), and Security, Compliance, and Governance for AI Solutions (14%).
$300.00

5 months ago

AWS Certified Machine Learning - Specialty
Amazon Web Services
This AWS certification covers Data Engineering (20%), Exploratory Data Analysis (24%), Modeling (36%), and Machine Learning Implementation and Operations (20%).
$99.00

5 months ago

Microsoft Certified: Azure AI Fundamentals
Microsoft
Microsoft certification covering fundamental AI concepts. This certification covers AI workloads and considerations, fundamental principles of machine learning on Azure, features of computer vision workloads on Azure, NLP workloads on Azure and generative AI.
$99.99

5 months ago

Complete A.I. & Machine Learning, Data Science Bootcamp
Andrei Neagoie · Daniel Bourke
This modern and comprehensive Data Science and Machine Learning course on Udemy teaches you the latest skills using Python, TensorFlow 2.0, and other libraries. It covers everything from basic Python programming to advanced topics like Neural Networks, Deep Learning, and Transfer Learning. You'll build real-world projects, gain hands-on experience, and learn to work with popular tools such as Pandas, Scikit-learn, and Matplotlib. By the end, you'll have a portfolio of projects and the skills needed to become a professional Data Scientist, ready for the job market.
$70.00

5 months ago

Artificial Intelligence: A Modern Approach
Stuart Russell · Peter Norvig
This book explores the full breadth and depth of the field of artificial intelligence (AI). The 4th Edition brings readers up-to-date on the latest technologies, presents concepts in a more unified manner, and offers new or expanded coverage of machine learning, deep learning, transfer learning, multi agent systems, robotics, natural language processing, causality, probabilistic programming, privacy, fairness, and safe AI.
Free

5 months ago

What Is ChatGPT Doing … and Why Does It Work? (Video)
Stephen Wolfram
Stephen Wolfram explores what's inside ChatGPT and its ability to produce meaningful text.
Free

5 months ago

Artificial Intelligence
OpenCourseWare
This MIT course introduces students to the basic knowledge representation, problem solving, and learning methods of artificial intelligence. Upon completion, students should be able to develop intelligent systems by assembling solutions to concrete computational problems; understand the role of knowledge representation, problem solving, and learning in intelligent-system engineering; and appreciate the role of problem solving, vision, and language in understanding human intelligence from a computational perspective.
Free

5 months ago

Neural Networks / Deep Learning - StatQuest
Josh Starmer
Everything you need to know, from the basics, to image classification with Convolutional Neural Networks, to the state of the art transformers used for large language models like ChatGPT, presented one step at a time so that it is easily understood.
Free

5 months ago

Machine Learning - StatQuest
Josh Starmer
A gentle introduction to Machine Learning. Linear Models and Logistic Regression are just the tips of the machine learning iceberg. There’s tons more to learn, and this playlist will help you trough it all, one step at a time.
Free

5 months ago

Practical Deep Learning
fast.ai · Jeremy Howard · Rachel Thomas
A free course from fast.ai designed for people with some coding experience, who want to learn how to apply deep learning and machine learning to practical problems.
Free

5 months ago

What Is ChatGPT Doing … and Why Does It Work? (Article)
Stephen Wolfram
Stephen Wolfram explores what's inside ChatGPT and its ability to produce meaningful text.
Free

5 months ago

AI for Everyone
Andrew Ng
A non-technical course on AI technologies taught by Andrew Ng, one of the industry leaders in Artificial Intelligence. This course covers what AI is, building AI projects, AI in your company and AI and society.
Free

5 months ago

Deep Learning Specialization
Andrew Ng
A course focused on specializing in Deep Learning taught by Andrew Ng, one of the industry leaders in Artificial Intelligence. This course covers neural networks and deep learning, improving neural networks, structuring machine learning projects, convolutional neural networks and sequence models.
Free

5 months ago

Machine Learning in Production
Andrew Ng
A course on implementing Machine Learning in production taught by Andrew Ng, one of the industry leaders in Artificial Intelligence. This course covers the ML lifecycle and deployment, modelling challenges and strategies and data definition and baseline.
Free

5 months ago

Machine Learning Specialization
Andrew Ng
A course on specializing in Machine Learning taught by Andrew Ng, one of the industry leaders in Artificial Intelligence. This course covers supervised machine learning, advanced learning algorithms, unsupervised learning and recommender systems.
Free

5 months ago

Let's reproduce GPT-2 (124M)
Andrej Kaparthy
A comprehensive guide on reproducing and training the GPT-2 124M model using PyTorch, comparing its performance to OpenAI's pre-trained models. Taught by Andrej Kaparthy, a founding member of OpenAI and a ex-senior director of AI at Tesla.
Free

5 months ago

Let's build the GPT Tokenizer
Andrej Kaparthy
A walkthrough on building the GPT tokenizer. This focuses on tokenization in large language models, discussing its complexities, necessary understanding, and its impact on model performance. Taught by Andrej Kaparthy, a founding member of OpenAI and a ex-senior director of AI at Tesla.
Free

5 months ago

[1hr Talk] Intro to Large Language Models
Andrej Kaparthy
A 1 hour general-audience introduction to LLMs. Understanding and advancing large language models involves technical, ethical, and security considerations. Taught by Andrej Kaparthy, a founding member of OpenAI and a ex-senior director of AI at Tesla.
Free

5 months ago

The spelled-out intro to neural networks and backpropagation: building micrograd
Andrej Kaparthy
Andrej presents a comprehensive lecture on training deep neural networks, explaining the creation and training of a neural network using his library, micrograd, and comparing it to PyTorch. Understanding and training neural networks requires mastering concepts like backpropagation, loss functions, and gradient descent. Taught by Andrej Kaparthy, a founding member of OpenAI and a ex-senior director of AI at Tesla.
Free

5 months ago

Latent Space
Swyx
A technical newsletter, podcast and community exploring UX, Agents, Devtools, Infra, Open Source.

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