Community Picks·1y🤗 Transformers
🤗 Transformers provides APIs and tools for easily downloading and training state-of-the-art pretrained models for tasks in natural language processing, computer vision, audio, and multimodal categories. It supports interoperability between PyTorch, TensorFlow, and JAX, allowing for flexible model training and deployment. The library also offers comprehensive documentation, tutorials, and guides to help users get started and achieve specific goals.
gitconnected·1yLet’s Build our own GPT Model from Scratch with PyTorch
Learn how to build a basic Generative Pre-trained Transformer (GPT) model from scratch using PyTorch. This tutorial covers auto-regressive models, character-level tokenization, data batching, and training using text in the style of William Shakespeare. It provides a detailed implementation of a bi-gram language model including the use of multi-head attention, forward and training operations, and generating new text tokens.
Towards AI·1yAn Introduction to PyTorch versus TensorFlow for Deep Learning
PyTorch and TensorFlow are the most popular frameworks in the deep learning community, providing customizable boilerplates for coding neural network architectures and optimizing computations with GPU resources. Without these frameworks, deep learning models had to be coded from scratch using Numpy, which is more cumbersome and slower without GPU optimization. Familiarity with these frameworks enhances the development of neural networks significantly.
Daily Dose of Data Science | Avi Chawla | Substack·1yA Hands-on Demo of Autoencoders
Autoencoders are powerful tools in machine learning, useful for tasks such as dimensionality reduction, anomaly detection, data denoising, and detecting multivariate covariate shifts. The post provides a hands-on demo using PyTorch Lightning to train an autoencoder, explaining the key components (encoder and decoder) and their roles. It highlights how to implement and train the model, alongside useful training optimizations like epoch and batch iteration, checkpoint saving, and multi-GPU support. Autoencoders are essential for addressing covariate shift problems in real-world ML models.
Daily Dose of Data Science | Avi Chawla | Substack·1yFrom PyTorch to PyTorch Fabric
Lightning Fabric integrates the flexibility of PyTorch with the convenience of distributed training features, like those in PyTorch Lightning. It simplifies model scaling with minimal code changes, such as importing the lightning module, configuring the Fabric object for system hardware, and replacing specific calls like `loss.backward()` with `fabric.backward(loss)`. Learn how to scale models efficiently with step-by-step instructions and leverage hardware like CPU, TPU, and GPU.