GluonNLP is a Natural language processing Deep learning-based toolkit. This toolkit includes cutting-edge pre-trained models, training scripts, and training logs to help with rapid prototyping and reproducible research.
Torchbearer is python based library which is basically a model fitting library for PyTorch models and offers a high-level metric and callback API that can be used in a variety of applications.
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.
Gensim is an open-source python package for natural language processing with a special focus on topic modelling. It is designed as a topic modelling library, allowing users to apply common academic-based models in production or projects.
Object detection is a task that involves bounding boxes and classifying them into categories to locate all positions of objects of interest in an input.
Facial expression analysis is the act of automatically detecting, collecting, and analyzing facial muscle movement and changes that reflect certain human mental states and situations.
AdverTorch is a tool developed by the Borealis AI research lab that employs a number of attack-and-defence tactics.
Kubeflow is an end-to-end machine learning stack orchestration toolset based on Kubernetes for deploying, scaling, and managing complex systems.
The optimization and machine learning toolkit is an open-source software program for optimizing neural network (NN) and gradient-boosted tree high-level representations (GBTs). NNs or GBTs can be integrated into bigger decision-making issues by optimizing over-trained surrogate models.
In this post, we’ll look at TensorLayer, a Python-based machine learning tool.
In this article, we will discuss cloud-based deployment using the Heroku cloud service.
Clustering is the process of putting items together so that members of the same group (cluster) be more common with their peers than members of other groups.
Real-ESRGAN is an extension of the powerful ESRGAN that synthesizes training pairs with a more practical degradation mechanism to recover general real-world low-resolution pictures. Real-ESRGAN is able to repair most real-world photos and produce superior visual performance than prior works
In time series analysis and forecasting, autocorrelation and partial autocorrelation are frequently employed to analyze the data.
Steganography is the practice of concealing a secret message within or even on top of something public.
Various deep learning frameworks such as PyTorch do their computation on numbers in the form of tensors. Tensors are one of the basic fundamental aspects or types of data in deep learning.
PyHealth is a Python-based toolbox. As the name implies, this toolbox contains a variety of ML models and architecture algorithms for working with medical data and modeling.
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 discuss how SMOTE technique can be used to improve the performance of weak learners such as SVM.
A recommender system, sometimes known as a recommendation engine, is a type of information filtering system that attempts to forecast a user’s “rating” or “preference” for an item. In this post, we will look at RGRecSys, a library that performs constraint evaluation of recommender systems.
In this article, we will discuss the uplift modelling, its types of modelling, and Python-based package called CausalML can be used to address the causal inference.
In this article, we will take a look at another possible methodology to make decisions at a more minute level, that is Fuzzy logic systems.
The PyFBAD library is an unsupervised anomaly detection package that works from start to finish. All ml-flow phases have source codes in this package.
In this article, we will discuss the VisionKG in detail and will see how it can query the dataset like COCO and ImageNet.
In this article, we will talk about how to segment images at the image level using the image-level supervision approach.
In this article, we will discuss Python based package HyperTools in detail and how it can help in visualizing high dimensional data.
In this article, we will take a look at knowledge distillation and will discuss its context briefly.
In this article, we are going to discuss how neural networks are being used in the art industry and we will take a look at NN-based architecture called Paint Transformer which results in human-crafted painting images of given natural images.
In this article, we will talk about ChainerCV, a library that has a variety of models that are required for computer vision-related tasks.
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