Complete Natural Language Processing Tutorial in Python
Comprehensive Python tutorial covering NLP fundamentals, techniques, and state-of-the-art models. Includes practical examples using popular libraries and hands-on implementation of advanced concepts.
Real-World Python Neural Nets Tutorial - Image Classification with CNN - Tensorflow & Keras
Learn to build and train a CNN for image classification using TensorFlow and Keras. Covers data preparation, model improvement, hyperparameter tuning, and practical applications in Python.
Enhance Low Light Images Using Keras, Python and Weights & Biases
Learn to enhance low-light images using Zero-DCE, an unsupervised technique implemented with Python, Keras, and Weights & Biases. Explore model training, experiment tracking, and result visualization in this hands-on tutorial.
Introducing the RAVEN MVP - A General Purpose AI Companion - With a Live Demo
Explore RAVEN, an innovative AI companion with real-time assistance capabilities, cognitive architecture, and self-improvement features. Learn about its development, principles, and future potential in artificial intelligence.
Supercharge Your Training With PyTorch Lightning and Weights & Biases
Learn to enhance ML pipelines using PyTorch Lightning and Weights & Biases. Discover efficient experiment tracking, advanced logging techniques, and how to leverage callbacks for improved model development and visualization.
Convolutional Neural Nets Explained and Implemented in Python - PyTorch
Comprehensive guide to Convolutional Neural Networks: theory, implementation, and practical applications using Python and PyTorch for computer vision tasks like image classification.
Vision Transformers Explained + Fine-Tuning in Python
Explore Vision Transformers (ViT) for image classification, from theory to implementation. Learn about attention mechanisms, patch embeddings, and fine-tuning using Hugging Face in Python.
Fast Zero Shot Object Detection with OpenAI CLIP
Learn zero-shot object detection and localization using OpenAI CLIP, a multi-modal deep learning model. Implement practical techniques for efficient, domain-flexible computer vision tasks without fine-tuning.
AlexNet and ImageNet Explained
Explore the revolutionary AlexNet CNN and ImageNet dataset, their impact on deep learning, and practical implementation using PyTorch for image classification tasks.
Fine-Tune High Performance Sentence Transformers With Multiple Negatives Ranking
Learn to fine-tune sentence transformers using multiple negatives ranking loss, improving embedding quality for similarity prediction and outperforming SBERT. Includes PyTorch and sentence-transformers library implementations.
Fine-Tune Sentence Transformers the OG Way - With NLI Softmax Loss
Explore fine-tuning sentence transformers using NLI softmax loss. Learn preprocessing, PyTorch implementation, and Sentence-Transformers library usage for improved sentence embeddings in NLP applications.
PyTorch Crash Course - Getting Started with Deep Learning
Comprehensive introduction to PyTorch, covering tensor basics, autograd, linear regression, neural networks, and CNNs. Ideal for beginners in deep learning.
JAX Crash Course - Accelerating Machine Learning Code
Accelerate machine learning with JAX: Learn NumPy-like operations on CPU, GPU, and TPU, automatic differentiation, and high-performance techniques for ML research in this comprehensive introduction.
Building a GAN From Scratch With PyTorch - Theory + Implementation
Learn to build a Generative Adversarial Network from scratch using PyTorch and PyTorch Lightning, covering theory, implementation, and testing in this comprehensive tutorial.
NLP Workflows with Keras
Explore NLP workflows using Keras, covering text vectorization, various modeling techniques, and practical implementations for efficient natural language processing tasks.
A Gentle Visual Intro to Transformer Models
Explore key concepts of Transformer models, including encoder, decoder, and attention, through a visual and accessible explanation by ML expert Jay Alammar.
Food for Diffusion
Explore advanced diffusion techniques with Stable Diffusion co-creator Patrick Esser, covering compositional structures, texture generation, and innovative training methods for next-gen models.
Stable Diffusion and Friends - High-Resolution Image Synthesis via Two-Stage Generative Models
Explore the evolution of generative image models with Stable Diffusion co-creator Robin Rombach, covering GANs, Transformers, and latent Diffusion models for high-resolution image synthesis and creative applications.
Beyond Text - Giving Stable Diffusion New Abilities
Explore advanced Stable Diffusion techniques like face variation and super resolution with Lambda Labs' Justin Pinkey in this technical deep dive on expanding AI image generation capabilities.
Develop Successful Deep Learning Projects with This 10 Steps Formula
Learn a 10-step formula for successful deep learning projects, covering problem definition, data preparation, model selection, training strategies, regularization, augmentation, interpretability, and deployment.
Prompt Engineering - Understanding Large Language Models with ChatGPT
Explore large language models and ChatGPT through prompt engineering. Learn about transformer architecture, text embedding, self-attention, and key deep learning methods with practical Python examples.