This project is a deep learning educational resource consisting of PyTorch model implementations and code examples. It provides functional Python scripts and notebooks for building, training, and optimizing neural networks using tensor-based computation. The repository includes implementations for designing custom network layers and loss functions, as well as examples of transfer learning workflows that load pretrained model weights to accelerate development. The codebase covers a broad range of deep learning capabilities, including neural network training, custom model component design, and
This project is a comprehensive collection of practical code examples and implementation libraries for machine learning. It provides a wide array of reference materials for building supervised, unsupervised, and reinforcement learning algorithms. The repository serves as a multi-domain resource, featuring specific implementation suites for financial AI, Bayesian statistical modeling, and deep learning architectures. It includes a framework for training intelligent agents using policy gradients and actor-critic models, as well as practical guides for fine-tuning transformers and utilizing larg
This project is a PyTorch library for building and training Kolmogorov-Arnold Networks. It implements a neural network architecture that replaces fixed activation functions with learnable spline-based functions on edges, serving as a tool for interpretable machine learning. The implementation utilizes reformulated matrix operations to reduce memory overhead and increase computation speed. It employs L1 regularization to sparsify network weights, which improves the transparency of the model's internal logic and decisions. The framework covers a range of capabilities including grid-based funct
Open Model Zoo is a curated collection of pre-trained and optimized deep learning models designed for high-performance inference using OpenVINO. It serves as a model repository and deployment framework that streamlines the integration of neural networks into production environments. The project utilizes a centralized manifest and a versioned registry to automate the downloading and organization of model weights and metadata. It includes tools for benchmarking inference performance and validating model accuracy by comparing outputs against ground-truth tensors to quantify precision loss. The
[Reimplementation Antol et al 2015] Keras-based LSTM/CNN models for Visual Question Answering
Principalele funcționalități ale avisingh599/visual-qa sunt: Deep Learning Models, Neural Network Architectures.
Alternativele open-source pentru avisingh599/visual-qa includ: openvinotoolkit/open_model_zoo — Open Model Zoo is a curated collection of pre-trained and optimized deep learning models designed for high-performance… lazyprogrammer/machine_learning_examples — This project is a comprehensive collection of practical code examples and implementation libraries for machine… blealtan/efficient-kan — This project is a PyTorch library for building and training Kolmogorov-Arnold Networks. It implements a neural network… deep-learning-with-pytorch/dlwpt-code — This project is a deep learning educational resource consisting of PyTorch model implementations and code examples. It… aladdinpersson/machine-learning-collection — This project is a machine learning educational repository providing a collection of implementations and guides for… facebookresearch/slowfast — SlowFast is a PyTorch video understanding framework and spatiotemporal neural network library. It serves as a toolset…