Deep learning
Deep learning (also known as deep structured learning) is part of a broader family of machine learning methods based on artificial neural networks with representation learning. Learning can be supervised, semi-supervised or unsupervised. Deep learning architectures such as deep neural networks, deep belief networks, recurrent neural networks and convolutional neural networks have been applied to fields including computer vision, machine vision, speech recognition, natural language processing, audio recognition, social network filtering, machine translation, bioinformatics, drug design, medical image analysis, material inspection and board game programs, where they have produced results comparable to and in some cases surpassing human expert performance. Artificial neural networks (ANNs) were inspired by information processing and distributed communication nodes in biological systems.
Relations
Computer science is the study of computation and information. Computer science deals with theory of c...
Machine learning (ML) is the study of computer algorithms that improve automatically through experien...
Computer vision is an interdisciplinary scientific field that deals with how computers can gain high-...
Computer programming is the process of designing and building an executable computer program to accom...
subtopic of Artificial intelligence (AI)
Artificial intelligence (AI), is intelligence demonstrated by machines, unlike the natural intelligen...
Data science is an inter-disciplinary field that uses scientific methods, processes, algorithms and s...
Big data is a field that treats ways to analyze, systematically extract information from, or otherwis...
In machine learning, backpropagation (backprop, BP) is a widely used algorithm for training feedforwa...
parent of Reinforcement learning
Reinforcement learning (RL) is an area of machine learning concerned with how intelligent agents ough...
cons given in Adversarial machine learning
Adversarial machine learning is a machine learning technique that attempts to fool models by supplyin...
parent of Graph neural network
A graph neural network (GNN) is a class of neural networks for processing data represented by graph d...
parent of Geometric Deep Learning
Geometric Deep Learning is an attempt for geometric unification of a broad class of ML problems from...
has tool Transformer (machine learning model)
A transformer is a deep learning model that adopts the mechanism of attention, differentially weighin...
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Resources
prerequisite for An Attempt at Demystifying Graph Deep Learning - Essays on Data Science
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There are a ton of great explainers of what graph neural networks are. However, I find that a lot of...
treated in Deep Reinforcement Learning | ÚFAL
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In recent years, reinforcement learning has been combined with deep neural networks, giving rise to g...
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In recent years, deep neural networks have been used to solve complex machine-learning problems. They...
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The Deep Learning textbook is a resource intended to help students and practitioners enter the field...
relates to Deep Convolutional and LSTM Recurrent Neural Networks for Multimodal Wearable Activity Recognition
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Human activity recognition (HAR) tasks have traditionally been solved using engineered features obta...
relates to DeepX: A Software Accelerator for Low-Power Deep Learning Inference on Mobile Devices
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In this work, we present the design and implementation of DeepX, a software accelerator for deep lear...
relates to Going deeper with convolutions
We propose a deep convolutional neural network architecture codenamed Inception that achieves the new...
relates to Deep Residual Learning for Image Recognition
Deeper neural networks are more difficult to train. We present a residual learning framework to ease...
relates to Sentiment Analysis with Deep Learning using BERT
In this 2-hour long project, you will learn how to analyze a dataset for sentiment analysis. You will...
relates to Deep Learning in Computer Vision
Deep learning added a huge boost to the already rapidly developing field of computer vision. With dee...
treated in LabML Neural Networks
This is a collection of simple PyTorch implementations of neural networks and related algorithms. The...
treated in Dive into Deep Learning
Interactive deep learning book with code, math, and discussions Implemented with NumPy/MXNet, PyTo...