A repository containing tutorials for practical NLP using PyTorch

A collection of deep learning projects implemented in Python, focusing on neural networks, computer vision, and NLP using frameworks like TensorFlow and PyTorch. Includes practical examples, datasets, and tutorials for building and evaluating advanced machine learning models.

In this tutorial I'll show you how to use BERT with the huggingface PyTorch library to quickly and efficiently fine-tune a model to get near state of the art performance in sentence classification. More broadly, I describe the practical application of transfer learning in NLP to create high performance models with minimal effort on a range of NLP tasks.

Implementing a simple, neural network-based Language Model entirely **from scratch** using the PyTorch library. Its main goal is to demonstrate key concepts in Natural Language Processing (NLP) and deep learning mechanisms in a practical and accessible way.

This repository provides practical implementations of natural language processing (NLP) using transformer models in both PyTorch and TensorFlow

Aspiring AI/ML Engineer with hands-on experience in PyTorch, NLP, and deep learning. Passionate about building intelligent systems and learning through practical projects.

A practical guide to deep learning with PyTorch. This repo includes beginner-friendly tutorials, model implementations (CNNs, RNNs, etc.), custom datasets, training loops, and real-world projects in CV and NLP. Perfect for learning and building with PyTorch.

PyTorch Deep Learning Lab - A comprehensive implementation of neural network architectures and exercises. Features CNN, RNN, Transformer models with practical applications in computer vision, NLP, and generative AI. Perfect for learning PyTorch fundamentals and advanced deep learning concepts.

A collection of deep learning models including CNNs, RNNs, Transformers, and FLAN-T5. Covers image classification, sequence modeling, and NLP tasks with implementations in TensorFlow/PyTorch, showcasing practical applications of modern AI architectures.

A collection of deep learning models including CNNs, RNNs, Transformers, and FLAN-T5. Covers image classification, sequence modeling, and NLP tasks with implementations in TensorFlow/PyTorch, showcasing practical applications of modern AI architectures.

A collection of deep learning models including CNNs, RNNs, Transformers, and FLAN-T5. Covers image classification, sequence modeling, and NLP tasks with implementations in TensorFlow/PyTorch, showcasing practical applications of modern AI architectures.

Deep Learning Projects and Implementations — A collection of deep learning models and tutorials using TensorFlow, Keras, and PyTorch. Includes practical implementations of CNNs, RNNs, LSTMs, Autoencoders, and Transfer Learning for real-world applications in computer vision and NLP.

Explore our "LLM Tutorials" GitHub repository for comprehensive guides on using large language models (LLMs) like Llama 2 with PyTorch. Discover practical examples, code snippets, and expert insights to enhance your NLP projects with the latest techniques in language modeling.

A collection of projects in Machine Learning, Deep Learning, and Natural Language Processing. Includes ML algorithms, neural network models using TensorFlow/PyTorch, and NLP tasks like text processing, embeddings, and language models. Explore practical implementations, innovative solutions, and research-driven work.

This repository contains my solutions and notes for the exercises in the book NLP with Transformers. Each chapter explores practical natural language processing tasks using modern transformer models such as BERT, DistilBERT, and more. Code examples are written in Python, leveraging popular libraries like Hugging Face Transformers and PyTorch.

NAVTTC AI/ML Course (3 Months). A practical-first repository focusing on Machine Learning, Deep Learning, and NLP specialization. Includes hands-on tasks for Data Science (Pandas/NumPy), Neural Networks (TensorFlow/PyTorch), and Microsoft Azure AI integration. Aimed at building employable AI skills in the Pakistani tech market.

Inspired by Andrej Karpathy's makemore Part-1, this project builds a character-level language model using PyTorch. The model learns from a dataset of names and generates new names by predicting the next character based on a bigram model. It explores neural networks, probability distributions, and NLP fundamentals in a practical way.