Medium·2y12 Fundamental Math Theories Needed to Understand AI
Understanding AI requires knowledge of several key mathematical theories, including the Curse of Dimensionality, Law of Large Numbers, Central Limit Theorem, Bayes’ Theorem, Overfitting and Underfitting, Gradient Descent, Information Theory, Markov Decision Processes, Game Theory, Statistical Learning Theory, Hebbian Theory, and Convolution. These concepts are foundational in AI and enhance understanding of its development.
The Palindrome·2yMachine Learning From Zero is ready to go!
Machine Learning From Zero (mlfz) is an open-source project featuring a tensor library built from scratch and an interactive 100+ pages textbook on neural networks. The project aims to help readers understand neural networks by breaking them down and rebuilding them. Topics include computational graphs, backpropagation, and vectorization.
DigitalOcean Community·2yConstructing Neural Networks From Scratch: Part 1
Building neural networks from scratch provides foundational understanding of deep learning. While popular frameworks like TensorFlow and PyTorch simplify neural net implementation, they may obscure core concepts. This guide explains foundational neural network components using Python and NumPy to solve tasks like the XOR logic gate. It addresses mathematical foundations, forward and backward propagation, and outlines the steps to train a simple neural network without deep learning frameworks.
Community Picks·2yI Trapped this AI Worm in a Dark Room for 1000 Simulated Years
An AI training lab explores training a worm controlled by a neural network. Starting with minimal training, akin to a newborn, the AI progresses by increasing the complexity of its neural network, gradually teaching the worm self-awareness, locomotion, and task completion through reinforcement learning. The process involves tweaking network sizes, joint dampening, and reaction times. The ultimate goal is to see the worm achieve realistic movement with a significantly large neural network. The post also mentions Skillshare as a resource for learning about AI art generation.
Medium·2yUnderstanding Loss and Loss Functions
Loss functions, also known as cost functions, are crucial for the optimization process in machine learning. This post explains the role of loss functions in supervised learning, the differences between training, validation, and testing data, and how loss is calculated and minimized. It also covers various loss functions for regression such as Mean Absolute Error (MAE), Mean Squared Error (MSE), Mean Absolute Percentage Error (MAPE), Root Mean Squared Error (RMSE), Logcosh, and Huber loss, providing their mathematical background and practical applications.
Towards AI·2yPredicting Heart Attacks Using Machine Learning Models: A Comprehensive Approach
This study utilizes Decision Tree and Multilayer Perceptron (MLP) machine learning models to predict heart attack likelihood using a large, diverse dataset. By optimizing hyperparameters and leveraging significant features, the models achieved an accuracy and F1-score of 92.33%. The performance outperforms similar studies, showcasing the potential of machine learning in early heart disease diagnosis. Future work aims to apply optimization algorithms and validate the models with real-time clinical data.
Community Picks·2yHow do Graphics Cards Work? Exploring GPU Architecture
Graphics cards, or GPUs, perform trillions of calculations per second to render realistic video game graphics and other computational tasks. With over 10,000 cores, GPUs execute simple arithmetic instructions in parallel, making them ideal for tasks like video game rendering and Bitcoin mining. The GA102 chip featured in many high-end graphics cards is built from billions of transistors and organized into a hierarchical structure of processing clusters and cores. The card's efficiency is further enhanced by its advanced memory architecture, which enables rapid data transfer. This computational power is also leveraged in AI through tensor cores for complex matrix operations.
Daily Dose of Data Science | Avi Chawla | Substack·2yRank-Consistent Classifiers
Rank-consistent classifiers address the shortcomings of cross-entropy loss functions in multiclass classification tasks where class labels have an inherent order, such as age groups. These models ensure predicted probabilities align with the natural ordering of labels, improving performance and interpretability in ordinal datasets.