Pytorch learning rate scheduler is used to find the optimal learning rate for various models by conisdering the model architecture and parameters.
Torcharrow is a Pytorch preprocessing library for data processing and visualization with various aspects of data processing.
The new release contains 3124 commits and is developed with the help of 433 contributors.
Embedding Q-Learning with Policy network would generate recommendation
Detecto is an open-source library for computer vision programming that helps us in fitting state-of-the-art computer vision and object detection models into our image data. One of the great things about this package is we can fit these models using very few lines of code.
functorch aims to provide composable vmap (vectorization) and autodiff transforms that work well with PyTorch modules and PyTorch autograd.
Explainable AI refers to strategies and procedures used in the application of artificial intelligence (AI) that allow human specialists to understand the solution’s findings.
Steganography is the practice of concealing a secret message within or even on top of something public.
Through this tutorial, we will demonstrate how to define and use a convolutional neural network (CNN) in a very easy way by explaining each of the steps in detail.
This article we will walk you through and compare the code usability and ease to use of TensorFlow and PyTorch on the most widely used MNIST dataset to classify handwritten digits.
In this article, we will learn how we can build a simple neural network using the PyTorch library in just a few steps. For this purpose, we will demonstrate a hands-on implementation where we will build a simple neural network for a classification problem.
A perceiver is a transformer that can handle non-textual data like images, sounds, and video, as well as spatial data.
While PyTorch Live remained the biggest announcement of the PyTorch Developer Day Conference 2021, there are many important highlights.
A versatile and simple library for sequential agent learning, including reinforcement learning
DORO is a robust outlier refinement of DRO that takes inspiration from its robust statistics. The refined risk function, which prevents DRO from overfitting to potential outliers, intuitively, the new risk function adaptively filters out a small fraction of data with high risk during training, which is potentially caused by outliers.
NVIDIA has submitted its training results for all eight benchmarks.
The library has a pipeline-based API that unifies the workflow in several steps that helps to increase the flexibility of the models. These APIs are designed to accomplish the following steps of any machine learning workflow
The pykale supports graph, images, text and videos data that can be loaded by PyTorch Dataloaders and supports CNN, GCN, transformers modules for machine learning.
In machine learning, the aim is to create algorithms that can learn and predict a required target output from the learnings. To achieve this, the
Sentiment Analysis (SA)is an amazing application of Text Classification, Natural Language Processing, through which we can analyze a piece of text and know its sentiment.
Rendering, also known as image synthesis, generates an output from a set of specific descriptions. It can also be described as transforming an impression or
Question Answering is a classical Natural Language Processing. This is a task involving a question being asked to a system from a set of documents or text and should be able to answer that question.
We will discuss Google AI’s state-of-the-art, T5 transformer which is a text to text transformer model. The gist of the paper is a survey of the existing modern transfer learning techniques used in Natural Language Understanding, proposing a unified framework that will combine all language problems into a text-to-text format.
Neural Networks are a series of algorithms that imitate the operations of a human brain to understand the relationships present in vast amounts of data.
NVIDIA’s Kaolin is a PyTorch library for all 3D deep learning needs from data preprocessing to model deployment, intending faster research
Facebook’s D2Go with in-built Detectron2 is the state-of-the-art toolkit for training & deployment of computer vision models on mobile devices
Barlow twins is a novel architecture inspired by the redundancy reduction principle in the work of neuroscientist H. Barlow.
Perceiver is a transformer-based model that uses both cross attention and self-attention layers to generate representations of multimodal data. A latent array is used to extract information from the input byte array using top-down or feedback processing
Catalyst is a PyTorch framework developed with the intent of advancing research and development in the domain of deep learning. It enables code reusability, reproducibility
STRIPE excels probabilistic time-series forecasting with space and time diversity
Torch-Points3D is a flexible and powerful framework that aims to make deep learning on 3D data both more accessible and reproducible.
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