Author Archives: Aishwarya Verma - Page 4
3D face reconstruction has been widely used for gaming applications. Though in the existing methods, game character customization methods require manual efforts from the users’ side to get the desired results. Not long ago, researchers from Netease Fuxi AI Lab and the University of Michigan proposed a new state-of-the-art method in this direction – MeInGame:…
The post How To Create A Game Character Face Using Python & Deep Learning appeared first on Analytics India Magazine.
Facebook ReAgent, previously known as Horizon is an end-to-end platform for using applied Reinforcement Learning in order to solve industrial problems. The main purpose of this framework is to make the development & experimentation of deep reinforcement algorithms fast. ReAgent is built on Python. It uses PyTorch framework for data modelling And training and TorchScript…
The post Hands-on to ReAgent: End-to-End Platform for Applied Reinforcement Learning appeared first on Analytics India Magazine.
Facebook ReAgent, previously known as Horizon is an end-to-end platform for using applied Reinforcement Learning in order to solve industrial problems. The main purpose of this framework is to make the development & experimentation of deep reinforcement algorithms fast. ReAgent is built on Python. It uses PyTorch framework for data modelling And training and TorchScript…
The post Hands-on to ReAgent: End-to-End Platform for Applied Reinforcement Learning appeared first on Analytics India Magazine.
Facebook ReAgent, previously known as Horizon is an end-to-end platform for using applied Reinforcement Learning in order to solve industrial problems. The main purpose of this framework is to make the development & experimentation of deep reinforcement algorithms fast. ReAgent is built on Python. It uses PyTorch framework for data modelling And training and TorchScript…
The post Hands-on to ReAgent: End-to-End Platform for Applied Reinforcement Learning appeared first on Analytics India Magazine.
PP-YOLO is a deep learning framework to detect objects. This framework is based on YOLO4 architecture. This method was published in the form of a Research paper titled as PP-YOLO: An Effective and Efficient Implementation of Object Detector by the researchers of Baidu : Xiang Long, Kaipeng Deng, Guanzhong Wang, Yang Zhang, Qingqing Dang, Yuan…
The post Guide To PP-YOLO: An Effective And Efficient Implementation Of Object Detector appeared first on Analytics India Magazine.
PP-YOLO is a deep learning framework to detect objects. This framework is based on YOLO4 architecture. This method was published in the form of a Research paper titled as PP-YOLO: An Effective and Efficient Implementation of Object Detector by the researchers of Baidu : Xiang Long, Kaipeng Deng, Guanzhong Wang, Yang Zhang, Qingqing Dang, Yuan…
The post Guide To PP-YOLO: An Effective And Efficient Implementation Of Object Detector appeared first on Analytics India Magazine.
VISSL is a computer VIsion library for state-of-the-art Self-Supervised Learning research. This framework is based on PyTorch. The key idea of this library is to speed up the self-supervised learning process from handling a new design to the evaluation part, VISSL does it all. Following are the characteristic of VISSL framework: Reproducibility: It provides a…
The post Guide to VISSL: Vision Library for Self-Supervised Learning appeared first on Analytics India Magazine.
Probabilistic Graphical Models(PGM) are a very solid way of representing joint probability distributions on a set of random variables. It allows users to do inferences in a computationally efficient way. PGM makes use of independent conditions between the random variables to create a graph structure representing the relationships between different random variables. Further, we can…
The post Guide to pgmpy: Probabilistic Graphical Models with Python Code appeared first on Analytics India Magazine.
Probabilistic Graphical Models(PGM) are a very solid way of representing joint probability distributions on a set of random variables. It allows users to do inferences in a computationally efficient way. PGM makes use of independent conditions between the random variables to create a graph structure representing the relationships between different random variables. Further, we can…
The post Guide to pgmpy: Probabilistic Graphical Models with Python Code appeared first on Analytics India Magazine.
Class Imbalance is quite a known problem in datasets, and Imbalanced Learning is used to handle this problem by learning an unbiased model in the data. Existing approaches include Resampling, Reweighting, Ensemble Methods and Meta-Learning Methods. In this article, we will discuss one new method that has outperformed many of the previous methods. It combines…
The post Guide to MESA: Boost Ensemble Imbalanced Learning with MEta-SAmpler appeared first on Analytics India Magazine.