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.

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.

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.

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.

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.

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

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.

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.

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.

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.

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

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.

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.

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.

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.

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.

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.

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.

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.

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.

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

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.

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

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

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.

Understand and build LLMs from scratch in PyTorch.

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

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.

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.

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.

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.