Building Makemore - MLP
Implement a multilayer perceptron character-level language model, covering machine learning basics like model training, hyperparameter tuning, evaluation, and addressing under/overfitting.
Intro to Neural Networks and Backpropagation - Building Micrograd
Step-by-step guide to neural networks and backpropagation, covering core concepts, implementation in Python, and practical exercises. Ideal for beginners with basic Python knowledge.
VQ-GAN - Taming Transformers for High-Resolution Image Synthesis - Paper Explained
Explore VQ-GAN's innovative approach to high-resolution image synthesis, combining modified VQ-VAEs with GPT-2 for impressive results. Learn about perceptual loss, adversarial techniques, and comparisons with other models.
VQ-VAEs - Neural Discrete Representation Learning - Paper + PyTorch Code Explained
Explore VQ-VAEs for neural discrete representation learning, covering key concepts, implementation details, and applications in generative modeling. Gain insights into this important AI technique.
Chip Placement with Deep Reinforcement Learning - Paper Explained
Explore deep reinforcement learning for efficient chip placement, covering RL agents, computer manufacturing, and edge graph neural networks. Gain insights into innovative AI applications in hardware design.
Non-Parametric Transformers - Paper Explained
Explore non-parametric transformers: innovative AI model for tabular data, relational reasoning, and causal mechanisms. Learn architecture, loss function, and connections to other neural networks.
Build a Simple Language Translation App Using Python for Beginners
Learn to build a language translation app using Python, Hugging Face Transformers, and Gradio. Covers installation, text translation with deep learning, and creating a lightweight ML app interface.
Deep Drowsiness Detection Using YOLO, Pytorch and Python
Learn to implement drowsiness detection using YOLO, PyTorch, and Python. Covers object detection, model fine-tuning, and real-time application for driver safety.
Pose Estimation with the Fastest Python Deep Learning Model - MoveNet Lightning
Learn to implement real-time pose estimation using MoveNet Lightning, a fast Python deep learning model. Covers installation, TFLite integration, rendering results, and OpenCV implementation.
Sign Language Detection Using Action Recognition with Python - LSTM Deep Learning Model
Learn to build a sign language detection system using action recognition, LSTM, and Python. Extract keypoints, create datasets, train deep learning models, and make real-time predictions for sign language interpretation.
Reinforcement Learning - Full Course Using Python
Comprehensive introduction to reinforcement learning using Python, covering fundamentals, custom environments, and practical projects like Atari games and autonomous driving.
Automate Stocks and Crypto Research with Python and Deep Learning - Full Python Project
Learn to automate stock and crypto research using Python, web scraping, and deep learning. Build a pipeline to gather, summarize, and analyze financial news for various assets.
Building a Custom Environment for Deep Reinforcement Learning with OpenAI Gym and Python
Learn to create and train custom reinforcement learning environments using OpenAI Gym and Python. Covers environment setup, DQN agent training, and practical implementation with Keras-RL.
Python ANPR with OpenCV and EasyOCR - Automatic Number Plate Recognition Tutorial
Learn to build an Automatic Number Plate Recognition system using Python, OpenCV, and EasyOCR. Master image processing, contour detection, and optical character recognition for practical computer vision applications.
The New AI Model Licenses Have a Legal Loophole - OpenRAIL-M of BLOOM, Stable Diffusion, etc.
Análisis crítico de las licencias de IA responsable, sus limitaciones y posibles consecuencias legales. Explora el dilema entre el uso abierto y las restricciones, proponiendo una alternativa llamada OpenRAIL++.
Neural Networks Are Decision Trees - With Alexander Mattick
Exploring the equivalence between neural networks and decision trees, discussing interpretability, computational advantages, and potential collaborations between the two approaches.
How to Make Your CPU as Fast as a GPU - Advances in Sparsity with Nir Shavit
Explore advances in sparsity and how CPUs can match GPU performance in neural networks. Learn about pruning, efficient algorithms, and the future of sparse architectures in deep learning.
TransGAN - Two Transformers Can Make One Strong GAN - Machine Learning Research Paper Explained
Explore TransGAN, a novel approach using transformers for image generation. Learn about its architecture, key techniques, and competitive performance in various datasets.
NFNets - High-Performance Large-Scale Image Recognition Without Normalization
Explore Normalizer-Free Networks, a novel approach to image recognition that achieves state-of-the-art accuracy without batch normalization, offering faster training and improved transfer learning performance.
Nyströmformer- A Nyström-Based Algorithm for Approximating Self-Attention
Explore Nyströmformer, a novel algorithm for approximating self-attention in Transformers with linear complexity, enabling processing of longer sequences and improving scalability in natural language tasks.
Rethinking Attention with Performers
Explore a novel approach to efficient attention mechanisms in Transformers using random positive orthogonal features, enabling linear-time approximation of attention matrices for scalable deep learning architectures.