Electronics, Free Full-Text

Por um escritor misterioso

Descrição

In recent years, deep learning has garnered tremendous success in a variety of application domains. This new field of machine learning has been growing rapidly and has been applied to most traditional application domains, as well as some new areas that present more opportunities. Different methods have been proposed based on different categories of learning, including supervised, semi-supervised, and un-supervised learning. Experimental results show state-of-the-art performance using deep learning when compared to traditional machine learning approaches in the fields of image processing, computer vision, speech recognition, machine translation, art, medical imaging, medical information processing, robotics and control, bioinformatics, natural language processing, cybersecurity, and many others. This survey presents a brief survey on the advances that have occurred in the area of Deep Learning (DL), starting with the Deep Neural Network (DNN). The survey goes on to cover Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), including Long Short-Term Memory (LSTM) and Gated Recurrent Units (GRU), Auto-Encoder (AE), Deep Belief Network (DBN), Generative Adversarial Network (GAN), and Deep Reinforcement Learning (DRL). Additionally, we have discussed recent developments, such as advanced variant DL techniques based on these DL approaches. This work considers most of the papers published after 2012 from when the history of deep learning began. Furthermore, DL approaches that have been explored and evaluated in different application domains are also included in this survey. We also included recently developed frameworks, SDKs, and benchmark datasets that are used for implementing and evaluating deep learning approaches. There are some surveys that have been published on DL using neural networks and a survey on Reinforcement Learning (RL). However, those papers have not discussed individual advanced techniques for training large-scale deep learning models and the recently developed method of generative models.
Electronics, Free Full-Text
The Ultimate Electronics Cooling Guide White Paper
Electronics, Free Full-Text
Electronics & Communication Level 7 - Free and - Nepal Telecom
Electronics, Free Full-Text
Sefram - Metrix Electronics Ltd
Electronics, Free Full-Text
Electronics - Free Books at EBD
Electronics, Free Full-Text
Free Electronics Recycling Event, October 14, 9 am to Noon - Welcome to the City of Eagle River
Electronics, Free Full-Text
Electronics icon set Royalty Free Vector Image
Electronics, Free Full-Text
Antelope Audio Orion Studio Synergy Core - Perfect Circuit
Electronics, Free Full-Text
Smartbox Full Hd Mpeg-4 Free To Air Media Player Set Top Box Life Time Channel Free
Electronics, Free Full-Text
Digital Electronics: A Primer
Electronics, Free Full-Text
RPHF Solid Waste District – Tire & Electronics Disposal Event – Ross County Health District
Electronics, Free Full-Text
New Elektor magazine website now online
Electronics, Free Full-Text
Araabmuzik - Electronic Dream (Standard) (Full Playlist)
de por adulto (o preço varia de acordo com o tamanho do grupo)