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See and compare every AI model easily. 100% free & open-source.

Added 5 months ago

countless.dev

Ahmet Dedeler

AI Models · Pricing

Spyware Injection Into Your ChatGPT's Long-Term Memory (SpAIware)

Added 5 months ago

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.

ChatGPT · Prompt Injection · Security

Added 5 months ago

Two Weeks Is All You Need

2-week speedrun to machine learning mastery for either an engineer or researcher path.

Machine Learning · Neural Networks

Mnist Network Visualization

Added 7 months ago

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.

Neural Networks · Visual

The other way to visualize derivatives | Chapter 12, Essence of calculus

Added 7 months ago

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.

Taylor series | Chapter 11, Essence of calculus

Added 7 months ago

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.

Higher order derivatives | Chapter 10, Essence of calculus

Added 7 months ago

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.

What does area have to do with slope? | Chapter 9, Essence of calculus

Added 7 months ago

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.

Integration and the fundamental theorem of calculus | Chapter 8, Essence of calculus

Added 7 months ago

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.

Limits, L'Hôpital's rule, and epsilon delta definitions | Chapter 7, Essence of calculus

Added 7 months ago

Grant discusses the concept of limits in calculus, explaining its relation to derivatives and L'Hopital's rule.

Implicit differentiation, what's going on here? | Chapter 6, Essence of calculus

Added 7 months ago

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.

What's so special about Euler's number e? | Chapter 5, Essence of calculus

Added 7 months ago

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.

Visualizing the chain rule and product rule | Chapter 4, Essence of calculus

Added 7 months ago

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.

Derivative formulas through geometry | Chapter 3, Essence of calculus

Added 7 months ago

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.

The paradox of the derivative | Chapter 2, Essence of calculus

Added 7 months ago

What is an "instantaneous rate of change" when change happens across time? Grant explains the concept of derivatives and the logical contradictions involved.

Added 7 months ago

The essence of calculus

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.

How might LLMs store facts | Chapter 7, Deep Learning

Added 7 months ago

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.

Beginner · Deep Learning · Large Language Models · Transformers · Visual

Attention in transformers, visually explained | Chapter 6, Deep Learning

Added 7 months ago

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.

Attention · Beginner · Deep Learning · Large Language Models · Transformers · Visual

How large language models work, a visual intro to transformers | Chapter 5, Deep Learning

Added 7 months ago

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.

Beginner · Deep Learning · Large Language Models · Transformers · Visual

Backpropagation calculus | Chapter 4, Deep learning

Added 7 months ago

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.

Back Propagation · Beginner · Calculus · Deep Learning · Visual

What is backpropagation really doing? | Chapter 3, Deep learning

Added 7 months ago

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.

Back Propagation · Beginner · Deep Learning · Neural Networks · Visual

Gradient descent, how neural networks learn | Chapter 2, Deep learning

Added 7 months ago

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.

Beginner · Deep Learning · Gradient Descent · Visual

But what is a neural network? | Chapter 1, Deep learning

Added 7 months ago

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.

Beginner · Deep Learning · Neural Networks · Visual

Ordinary Least Squares Regression

Added 7 months ago

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

Machine Learning · Predictive Modeling · Visual

Added 7 months ago

Markov Chains

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.

Markov Chains · Visual

Added 7 months ago

Conditional probability

A visual and interactive explanation of how conditional probability works.

Beginner · Probability · Visual

Model Tuning and the Bias-Variance Tradeoff

Added 7 months ago

R2D3

Part 2 of a visual introduction to machine learning covering model tuning and the bias-variance trade-off.

Machine Learning · Model Tuning · Visual

A visual introduction to machine learning

Added 7 months ago

R2D3

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

Beginner · Decision Trees · Machine Learning · Visual

Added 7 months ago

Image Kernels

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.

Feature Extraction · Machine Learning · Visual

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

Added 7 months ago

LLM University

Cohere

Beginner · Large Language Models · Visual

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.

Added 7 months ago

LLM Visualization

Brendan Bycroft

Large Language Models · Visual

The Narrated Transformer Language Model

Added 7 months ago

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.

Transformers · Visual

This video unravels the mystery behind how machines interpret input data, such as images or sounds, and categorize them into recognizable concepts.

Added 7 months ago

How AI Learns Concepts

Art of the Problem

Visual

How AIs, like ChatGPT, Learn

Added 7 months ago

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.

ChatGPT · Visual

Too much efficiency makes everything worse: overfitting and the strong version of Goodhart’s law

Added 7 months ago

Jascha Sohl-Dickstein

Over-optimizing efficiency worsens the very goals it aims to achieve, known as Goodhart's law.

Goodhart's Law · Machine Learning

Low Level Technicals of LLMs: Daniel Han

Added 8 months ago

Daniel Han

This workshop covers how to analyze and fix LLMs, finetuning with Unsloth and a deep dive into LLM technicals.

Large Language Models

Web Scraping with GPT-4 Vision AI + Puppeteer is Mind-Blowingly EASY!

Added 8 months ago

ByteGrad

Learn how to scrape web pages more easily by utilising GPT-4, GPT-4 Vision and Puppeteer.

GPT-4 · Web Scraping

A practical course for building LLM-based applications with Charles Frye, Sergey Karayev, and Josh Tobin.

Added 8 months ago

Full Stack LLM Bootcamp

The Full Stack

Large Language Models

RLHF: Reinforcement Learning from Human Feedback

Added 8 months ago

Chip Huyen

Incorporating reinforcement learning with human feedback into NLP at a massive scale.

ChatGPT · Natural Language Processing · Reinforcement Learning

How Stable Diffusion Works

Added 8 months ago

Chris McCormick

Learn how Stable Diffusion uses complex equations and machine learning to generate art from noise.

Beginner · Image Processing · Machine Learning · Stable Diffusion

The Illustrated Transformer

Added 8 months ago

Jay Alammar

A visual and simplified exploration of the Transformer model in machine learning.

Deep Learning · Machine Learning · Natural Language Processing · Visual

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

Added 8 months ago

Software 2.0

Andrej Karpathy

Beginner · Neural Networks

Deep Learning in a Nutshell: Core Concepts

Added 8 months ago

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.

Beginner · Convolutional Neural Networks · Deep Learning · Machine Learning

Getting Started with Large Language Models: Key Things to Know

Added 8 months ago

Sebastian Raschka · Niels Bantilan

An introductory guide to LLMs captures the evolving ecosystem and practical application.

Beginner · Large Language Models · Machine Learning

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

Added 8 months ago

Vector Databases

Jide Ogunjobi

Intermediate · Vector Databases

Catching up on the weird world of LLMs

Added 8 months ago

Simon Willison

Summarizing recent developments and ethical concerns in the field of large language models.

Ethics · History · Large Language Models

A feed of noteworthy developments in AI.

Added 8 months ago

bleeding edge

Lachy

History

ChatGPT Explained: A Normie's Guide To How It Works

Added 8 months ago

Jon Stokes

This article demystifies modern chatbots by dissecting how ChatGPT works.

Beginner · ChatGPT · Machine Learning

How to write good prompts: using spaced repetition to create understanding

Added 8 months ago

Andy Matuschak

Guide on writing effective prompts using spaced repetition to enhance memory and understanding.

Prompt Engineering

Raza Habib · Sinan Ozdemir

Added 8 months ago

Prompt Engineering 101

Explore the fundamentals of prompt engineering for optimizing Large Language Model outputs.

Beginner · Large Language Models · Prompt Engineering

The brief history of artificial intelligence: the world has changed fast — what might be next?

Added 8 months ago

Max Roser

Little is as important for the world’s future and our own lives as how this history continues.

Future Predictions · History · Visual

Added 8 months ago

Opportunities in AI

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.

Beginner · Non-Technical

Generative AI exists because of the transformer

Added 8 months ago

Financial Times

A visual explanation of how generative AI works and how hallucinations occur.

Beginner · Visual

Prompt Engineering Best Practices

Added 8 months ago

Sarah Chieng

Current practices and prompting tricks to improve LLM output.

Beginner · Best Practices · Prompt Engineering

Large Language Model Course

Added 8 months ago

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.

GitHub Repos · Large Language Models

Mateusz Malinowski · Aston Zhang · Zachary C. Lipton · Alexander J. Smola

Added 8 months ago

Dive Into Deep Learning

Interactive deep learning book with code, math, and discussions. Implemented with PyTorch, NumPy/MXNet, JAX, and TensorFlow. Adopted at 500 universities from 70 countries.

Deep Learning · NumPy · PyTorch · Python · TensorFlow

Neural Networks: Zero to Hero

Added 8 months ago

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.

Deep Learning · Neural Networks

$65.99

Added 8 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.

Machine Learning · System Design

Full Stack Deep Learning Bootcamp

Added 8 months ago

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.

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

Added 8 months ago

Reinforcement Learning

Google DeepMind

Reinforcement Learning

Probabilistic Graphical Models Specialization

Added 8 months ago

Probabilistic Graphical Models. Master a new way of reasoning and learning in complex domains.

Bayesian Network · Belief Propagation · Inference

CS224N: Natural Language Processing with Deep Learning

Added 8 months ago

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.

Deep Learning · Natural Language Processing

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.

Added 8 months ago

Linear Algebra

MIT

Linear Algebra

Introduction to Statistics

Added 8 months ago

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.

Added 8 months ago

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.

Agents · Case Studies · Ethics · Natural Language Processing · Reinforcement Learning

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.

Added 8 months ago

Generative AI Handbook

William Brown

Generative AI

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.

Added 8 months ago

Computer Vision Models

Simon J.D. Prince

Computer Vision

Understanding Deep Learning

Added 8 months ago

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.

Added 8 months ago

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.

Added 8 months ago

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.

Data Science · Machine Learning · Statistics

$14.73

Added 8 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.

Future Predictions

$20.00

Added 8 months ago

The Alignment Problem

Brian Christian

An exploration of the challenges in aligning artificial intelligence systems with human values and goals.

Artificial Intelligence in Practice

$40.00

Added 8 months ago

Bernard Marr · Matt Ward

A collection of case studies showcasing how AI is being applied in various industries and domains.

Business · Case Studies

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.

Added 8 months ago

simple.ai

Dharmesh Shah

Agents · Business

A podcast for non-technical business leaders who need to find AI opportunities, align AI capabilities with strategy, and deliver ROI.

Added 8 months ago

AI in Business Podcast

Daniel Faggella

Business · Non-Technical

$29.99

Added 8 months ago

The Master Algorithm

Pedro Domingos

An exploration of the quest to find the “master algorithm” that can solve all machine learning problems.

Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow

$54.99

Added 8 months ago

Aurélien Géron

A practical guide to implementing machine learning techniques using popular Python libraries.

Keras · Machine Learning · Python · Scikit-Learn · TensorFlow

$33.99

Added 8 months ago

Superintelligence: Paths, Dangers, Strategies

Nick Bostrom

A thought-provoking examination of the potential risks and implications of advanced artificial intelligence.

Future Predictions

$20.00

Added 8 months ago

The Hundred-Page Machine Learning Book

Andriy Burkov

A concise and accessible introduction to the core principles and algorithms of machine learning.

Added 8 months ago

Yoshua Bengio · Ian Goodfellow · Aaron Courville

An in-depth exploration of the theory and practice of deep learning, a powerful subset of machine learning.

Building LLMs from the Ground Up: A 3-hour Coding Workshop

Added 8 months ago

Sebastian Raschka

Understand and build LLMs from scratch in PyTorch.

Large Language Models

Free - $250.00

Added 9 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.

Beginner

Deep Speech 2: End-to-End Speech Recognition in English and Mandarin

Added 9 months ago

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 · Andrew Ng · 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

Alex Graves · Greg Wayne · Ivo Danihelka

Added 9 months ago

Neural Turing Machines

Kolmogorov Complexity and Algorithmic Randomness

Added 9 months ago

A. Shen · V. A. Uspensky · N. Vereshchagin

Machine Super Intelligence

Added 9 months ago

Shane Legg

A tutorial introduction to the minimum description length principle

Added 9 months ago

Peter Grünwald

Scaling Laws for Neural Language Models

Added 9 months ago

Dario Amodei · Jared Kaplan · Sam McCandlish · Tom Henighan · Tom B. Brown · Benjamin Chess · Rewon Child · Scott Gray · Alec Radford · Jeffrey Wu

Quantifying the Rise and Fall of Complexity in Closed Systems: The Coffee Automation

Added 9 months ago

Scott Aaronson · Sean M. Carroll · Lauren Ouellete

Relational recurrent neural networks

Added 9 months ago

Mike Chrzanowski · Adam Santoro · Ryan Faulkner · David Raposo · Jack Rae · Théophane Weber · Daan Wierstra · Oriol Vinyals · Razvan Pascanu · Timothy Lillicrap

Variational Lossy Autoencoder

Added 9 months ago

Xi Chen · Diederik P. Kingma · Tim Salimans · Yan Duan · Prafulla Dhariwal · John Schulman · Ilya Sutskever · Pieter Abbeel

A simple neural network module for relational reasoning

Added 9 months ago

Adam Santoro · David Raposo · Timothy Lillicrap · David G.T. Barrett · Mateusz Malinowski · Rasvan Pascanu · Peter Battaglia

Identity Mappings in Deep Residual Networks

Added 9 months ago

Kaiming He · Xiangyu Zhang · Shaoqing Ren · Jian Sun

Neural Machine Translation by Jointly Learning to Align and Translate

Added 9 months ago

Dzmitry Bahdanau · KyungHyun Cho · Yoshua Bengio

Attention Is All You Need

Added 9 months ago

Ashish Vaswani · Noam Shazeer · Niki Parmar · Jakob Uszkoreit · Llion Jones · Aidan N. Gomez · Łukasz Kaiser · Illia Polosukhin

Neural Message Passing for Quantum Chemistry

Added 9 months ago

Oriol Vinyals · Justin Gilmer · Samuel S. Schoenholz · Patrick F. Riley · George E. Dahl

Multi-Scale Context Aggregation by Dilated Convolutions

Added 9 months ago

Fisher Yu · Vladlen Koltun

Deep Residual Learning for Image Recognition

Added 9 months ago

Kaiming He · Xiangyu Zhang · Shaoqing Ren · Jian Sun

CS231n Convolutional Neural Networks for Visual Recognition

Added 9 months ago

Advanced · Ilya30u30 · Visual

GPipe: Efficient Training of Giant Neural Networks using Pipeline Parallelism

Added 9 months ago

Yanping Huang · Youlong Cheng · Ankur Bapna · Orhan Firat · Mia Xu Chen · Dehao Chen · HyoukJoong Lee · Jiquan Ngiam · Quoc V. Le · Yonghui Wu · Zhifeng Chen

Order Matters: Sequence to sequence for sets

Added 9 months ago

Oriol Vinyals · Samy Bengio · Manjunath Kudlur

ImageNet Classification with Deep Convolutional Neural Networks

Added 9 months ago

Ilya Sutskever · Alex Krizhevsky · Geoffrey E. Hinton

Oriol Vinyals · Meire Fortunato · Navdeep Jaitly

Added 9 months ago

Pointer Networks

Keeping NN Simple by Minimizing the Description Legnth of the Weights

Added 9 months ago

Geoffrey E. Hinton · Drew van Camp

Recurrent Neural Network Regularization

Added 9 months ago

Oriol Vinyals · Ilya Sutskever · Wojciech Zaremba

Understanding LSTM Networks -- colah's blog

Added 9 months ago

Christopher Olah

LSTMs are crucial for learning long-term dependencies in recurrent neural networks.

Advanced · Deep Learning · Ilya30u30 · LSTM · Machine Learning · Neural Networks · Visual

The Unreasonable Effectiveness of Recurrent Neural Networks

Added 9 months ago

Andrei Karpathy

Explore the magical outputs and robustness of Recurrent Neural Networks in various data formats.

Advanced · Deep Learning · Ilya30u30 · Machine Learning · Recurrent Neural Networks

The First Law of Complexodynamics

Added 9 months ago

Scott Aaronson

Discusses the complexity and entropy relationship in a physical systems' evolution.

Advanced · Complexity · Entropy · Ilya30u30 · Physics

The Annotated Transformer

Added 9 months ago

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.

Deep Learning · Ilya30u30 · Machine Learning · Natural Language Processing · Transformers

$29.00 - $95.00

Added 9 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.

Deep Learning · Neural Networks · Python

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

Added 9 months ago

Spinning Up

OpenAI

Deep Reinforcement Learning

Artificial Intelligence for Beginners

Added 9 months ago

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.

Beginner · Deep Learning · Ethics · GitHub Repos · Neural Networks · Startups

Added 9 months ago

Neural networks

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

Beginner · Neural Networks · Visual

Essence of linear algebra

Added 9 months ago

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

Beginner · Linear Algebra · Visual

Added 9 months ago

Essence of calculus

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.

The training campus for top AI learners, experts, builders.

Added 9 months ago

Build Club

Annie Liao

Startups

Competitions · Data Science · Datasets · Large Language Models · Machine Learning

Added 9 months ago

Kaggle

Free - $20.00 / month

Added 9 months ago

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.

Competitions · Datasets · Large Language Models

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.

Added 9 months ago

The AI Podcast

NVIDIA

Business · Future Predictions

$75.00

Added 9 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%).

Amazon Web Services · Ethics · Generative AI · Machine Learning · Security

$300.00

Added 9 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%).

Amazon Web Services · Data Analysis · Data Engineering · Machine Learning

$99.00

Added 9 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.

Computer Vision · Generative AI · Microsoft Azure · Natural Language Processing

$99.99

Added 9 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.

Data Analysis · Data Science · Deep Learning · Machine Learning · Pandas · Python · TensorFlow

$70.00

Added 9 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.

Deep Learning · Ethics · Machine Learning

What Is ChatGPT Doing … and Why Does It Work? (Video)

Added 9 months ago

Stephen Wolfram

Stephen Wolfram explores what's inside ChatGPT and its ability to produce meaningful text.

ChatGPT · Language Models · Neural Networks

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.

Added 9 months ago

Artificial Intelligence

OpenCourseWare

Decision Trees · Neural Networks · Probability

Neural Networks / Deep Learning - StatQuest

Added 9 months ago

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.

Deep Learning · Neural Networks

Machine Learning - StatQuest

Added 9 months ago

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.

Beginner · Machine Learning

fast.ai · Jeremy Howard · Rachel Thomas

Added 9 months ago

Practical Deep Learning

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.

What Is ChatGPT Doing … and Why Does It Work? (Article)

Added 9 months ago

Stephen Wolfram

Stephen Wolfram explores what's inside ChatGPT and its ability to produce meaningful text.

Language Models · Machine Learning · Neural Networks

Added 9 months ago

AI for Everyone

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.

Beginner · Data Science · Machine Learning · Non-Technical

Deep Learning Specialization

Added 9 months ago

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.

Machine Learning in Production

Added 9 months ago

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.

Machine Learning Specialization

Added 9 months ago

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.

Let's reproduce GPT-2 (124M)

Added 9 months ago

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.

GPT

Let's build the GPT Tokenizer

Added 9 months ago

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.

GPT · Jupyter Notebooks · Python

[1hr Talk] Intro to Large Language Models

Added 9 months ago

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.

Intermediate · Large Language Models · Security

The spelled-out intro to neural networks and backpropagation: building micrograd

Added 9 months ago

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.

Back Propagation · Jupyter Notebooks · Micrograd · Neural Networks · Python