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This is a PyTorch self-supervised learning framework designed to train models that learn visual representations from video. It implements a joint-embedding predictive architecture that extracts spatio-temporal features by predicting missing regions of a signal within a latent representation space rather than reconstructing raw pixels. The project includes a latent space visualization tool that uses a conditional diffusion model to decode feature-space predictions back into pixels. This allows for the verification of learned representations by transforming abstract predictions into interpretab
tsai is a deep learning library for time series classification, regression, and forecasting. Built on PyTorch and fastai, it provides a framework for assigning labels to sequential data, predicting future values in univariate or multivariate sequences, and training representations on unlabeled data through self-supervised learning. The library distinguishes itself with specialized temporal engineering and scaling capabilities. It includes tools for cyclical temporal encoding to capture seasonal patterns and online window slicing to process datasets larger than available memory. It also suppor
This is an educational curriculum for building and training neural networks using PyTorch. It serves as a deep learning training guide and resource, providing a structured series of lessons on tensor computation and architecture development. The course uses an interactive learning model that synchronizes academic theory with practice. It pairs theoretical lecture slides with exercise-driven notebooks, requiring students to implement model logic within predefined templates to validate their conceptual understanding. The curriculum covers a broad range of deep learning capabilities, including
This project is an implementation of the ALBERT language model architecture, providing a framework for training and evaluating transformer-based text classifiers and similarity models. It specifically includes pre-trained assets and tools optimized for generating semantic embeddings and representations of Chinese text. The framework distinguishes itself through tools for converting heavy language model checkpoints into lightweight formats to enable low-latency inference on mobile devices. It utilizes specific weight reduction techniques, including cross-parameter sharing and factorized embedd
This repository contains the code for DABS, a benchmark for domain-agnostic self-supervised learning algorithms. The basic components of the benchmark can be found in datasets, encoders, and algorithms. Training is implemented with the PyTorch Lightning framework, logging with Weights and…
The main features of alextamkin/dabs are: Self-Supervised Learning.
Open-source alternatives to alextamkin/dabs include: facebookresearch/jepa — This is a PyTorch self-supervised learning framework designed to train models that learn visual representations from… timeseriesai/tsai — tsai is a deep learning library for time series classification, regression, and forecasting. Built on PyTorch and… atcold/pytorch-deep-learning-minicourse — This is an educational curriculum for building and training neural networks using PyTorch. It serves as a deep… nyandwi/machine_learning_complete — This is an interactive notebook-based course that teaches machine learning from Python fundamentals through deep… brightmart/albert_zh — This project is an implementation of the ALBERT language model architecture, providing a framework for training and… tensorflow/similarity — TensorFlow Similarity is a Python framework designed for training neural networks to learn high-dimensional vector…