#Deep learning

PyTorch Forecasting uses PyTorch's deep learning architectures to simplify time series forecasting for professionals and beginners. Built on PyTorch Lightning, it supports training on both CPUs and GPUs. Key features include a versatile time series dataset class, a robust model training framework with visualization tools, and various neural network architectures optimized for real-world applications. Featuring multi-horizon metrics and hyperparameter tuning using Optuna, this package supports accurate predictions in diverse scenarios. Explore a range of tutorials and resources to enhance forecasting capabilities.

PyTextClassifier is an open-source Python library designed for text classification and clustering applications, incorporating a diverse range of algorithms including Logistic Regression, Random Forest, Decision Tree, and advanced deep learning models like BERT and FastText. It supports sentiment analysis, risk classification, and other complex classification tasks such as binary, multi-class, multi-label, and hierarchical classifications. The library offers straightforward installation and usage for efficient model training, evaluation, and deployment, ensuring high performance and clarity with an emphasis on modular design and ease of use.

PyTorch/XLA leverages the XLA compiler to optimize deep learning workflows across Cloud TPUs and GPUs, facilitating effective large-scale model training. This package supports seamless PyTorch integration on TPUs and has expanded to include a GPU plugin. Experimentation is possible on a free Cloud TPU VM, with various installation options available for stable, nightly, or GPU builds. Extensive guides and user-friendly notebooks assist with setup, and feedback is welcomed through GitHub to aid ongoing enhancements.

Ludwig provides a low-code environment to create tailored AI models such as LLMs and neural networks with ease. The framework uses a declarative YAML-based configuration, supporting features like multi-task and multi-modality learning. Designed for scalable efficiency, it includes tools like automatic batch size selection and distributed training options like DDP and DeepSpeed. With hyperparameter tuning and model explainability, users have detailed control, along with a modular and extensible structure for different model architectures. Ready for production, Ludwig integrates Docker, supports Kubernetes with Ray, and offers model exports to Torchscript and Triton.

Polyaxon streamlines deep learning application development by ensuring reproducibility and efficient resource management. Supporting leading frameworks like Tensorflow and PyTorch, it facilitates operations across cloud and data centers with features such as distributed job management and hyperparameter tuning. Its architecture efficiently utilizes GPU servers as shared resources, complemented by a user-friendly dashboard for project monitoring. Polyaxon is trusted in production environments worldwide, optimizing AI deployment and scaling.

Explore a collection of straightforward implementations of fundamental machine learning algorithms designed for those interested in learning and experimenting with ML models. This project offers insights into key algorithms, including Deep Learning, Random Forests, and SVM, utilizing Python's numpy, scipy, and autograd for clarity and simplicity. The code can be easily executed on local and Docker environments, encouraging contributions to foster collaborative learning and development in the machine learning field.

Discover detailed resources from the Fall 2023 Deep Learning course. Access weekly lecture and practice materials that can be completed locally or through Google Colab. Stay connected via the Telegram chat for community engagement and refer to the online page for deadlines and grading information. The course covers a range of topics including neural networks, convolutional networks, and adaptive optimization. Materials are developed by experts like Victor Lempitsky, Victor Yurchenko, and others, providing content on generative models and PyTorch fundamentals. Engage with the course through collaborative insights and continuous enhancements.

Delve into implementations of diverse transformer models like seq2seq, encoder-only, and decoder-only architectures for AI and deep learning enthusiasts. This open-source inspired repository offers insights into transformer advancements, attention mechanisms, and position embeddings. It serves as a valuable reference for programming skill enhancement and AI model development. Note: Intended for self-study, not formal educational assignments.

Discover how Open3D and PointNet++ improve semantic segmentation on Semantic3D datasets. This project exemplifies advanced 3D data processing, including visualization, voxel down-sampling, and efficient nearest neighbor searching. It provides comprehensive guidelines from preprocessing to model training and prediction, assisting researchers and developers in refining 3D segmentation processes.