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

Added 6 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 6 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 6 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 6 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 6 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 6 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 6 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 6 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 6 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 6 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 6 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 6 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 6 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 6 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 6 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 6 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 6 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 6 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 6 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 6 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 6 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 6 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 6 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 6 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 6 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 6 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 6 months ago

LLM Visualization

Brendan Bycroft

Large Language Models · Visual

The Narrated Transformer Language Model

Added 6 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 6 months ago

How AI Learns Concepts

Art of the Problem

Visual

How AIs, like ChatGPT, Learn

Added 6 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

The Illustrated Transformer

Added 7 months ago

Jay Alammar

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

Deep Learning · Machine Learning · Natural Language Processing · Visual

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

Added 7 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

Generative AI exists because of the transformer

Added 7 months ago

Financial Times

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

Beginner · Visual

CS231n Convolutional Neural Networks for Visual Recognition

Added 8 months ago

Standford University

Advanced · Ilya30u30 · Visual

Understanding LSTM Networks -- colah's blog

Added 8 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

Added 8 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 8 months ago

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

Beginner · Linear Algebra · Visual

Added 8 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.