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.
How to Use OpenAI Whisper to Fix YouTube Search
Learn to enhance YouTube search using OpenAI Whisper, transformers, and vector search. Build a system for precise content retrieval, transcription, and question-answering from YouTube videos.
GUI-Based Few Shot Classification Model Trainer - Demo
Discover how to efficiently train classification models using vector search, enabling rapid fine-tuning with minimal labeled data for improved accuracy and targeted results.
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.
How to Use Color Histograms for Image Retrieval
Learn to use color histograms for image retrieval, including building histograms, using OpenCV, and implementing retrieval techniques. Explore pros, cons, and practical applications of this content-based approach.
Long Form Question Answering in Haystack
Explore long-form question answering using Haystack, covering setup, data indexing, and generating responses. Learn to implement QA systems for efficient information retrieval in organizations.
Spotify's Podcast Search Explained
Explore Spotify's innovative podcast search system using natural language processing and learn to implement a similar solution with transformer models and vector search techniques.
Is GPL the Future of Sentence Transformers - Generative Pseudo-Labeling Deep Dive
Explore Generative Pseudo-Labeling for training high-performance sentence transformers using unlabeled text data. Learn implementation techniques, potential applications, and future implications for NLP and semantic search.
Train Sentence Transformers by Generating Queries - GenQ
Learn to train sentence transformers using GenQ, a technique that generates synthetic queries for effective bi-encoder models in semantic search, with practical code walkthroughs and implementation insights.
How to Build a Q&A AI in Python - Open-Domain Question-Answering
Learn to build an open-domain question-answering AI in Python, covering retriever training, fine-tuning, evaluation, vector database setup, and querying techniques for natural language interfaces.
Building Transformer Tokenizers - Dhivehi NLP #1
Learn to build an effective WordPiece tokenizer for Dhivehi, a low-resource language, overcoming unique challenges in NLP application. Explore tokenizer components, implementation, and usage for this fascinating language.
Question-Answering in NLP - Extractive QA and Abstractive QA
Explore question-answering in NLP, covering extractive and abstractive QA techniques. Learn about semantic search, ODQA, SQuAD format, and various QA approaches for efficient information retrieval and processing.
All You Need to Know on Multilingual Sentence Vectors - 1 Model, 50+ Languages
Learn to create and use multilingual sentence vectors for cross-language text comparison. Covers techniques like multi-task training and knowledge distillation, with practical implementation and evaluation steps.
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.
Training and Testing an Italian BERT - Transformers From Scratch
Guida pratica per addestrare e testare un modello BERT italiano, dalla configurazione al training loop, gestendo errori CUDA e valutando i risultati con test reali.
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.
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.
Transfer Learning in NLP and the Birth of the Transformers Library
Explore the origins of HuggingFace and Transformers library, delving into transfer learning in NLP, open-source models, and the impact of community-driven AI research and development.