#
Neural networks (Computer science)
Resource Information
The concept ** Neural networks (Computer science)** represents the subject, aboutness, idea or notion of resources found in **San Francisco Public Library**.

The Resource
Neural networks (Computer science)
Resource Information

The concept

**Neural networks (Computer science)**represents the subject, aboutness, idea or notion of resources found in**San Francisco Public Library**.- Label
- Neural networks (Computer science)

## Context

Context of Neural networks (Computer science)#### Subject of

- Advanced deep learning with Keras : apply deep learning techniques, autoencoders, GANs, variational autoencoders, deep reinforcement learning, policy gradients, and more
- Application of autoassociative neural networks to health monitoring of the CAT 7 diesel engine
- Applied Deep Learning : A Case-Based Approach to Understanding Deep Neural Networks
- Applied deep learning : a case-based approach to understanding neural networks
- Applied natural language processing with Python : implementing machine learning and deep learning algorithms for natural language processing
- Artificial intelligence and machine learning fundamentals
- Artificial life : the quest for a new creation
- Artificial neural networks with Java : tools for building neural network applications
- Beginning AI Bot Frameworks : Getting Started with Bot Development
- Bio-inspired routing protocols for vehicular ad hoc networks
- Bioinformatics : the machine learning approach
- Building Telegram Bots : Develop Bots in 12 Programming Languages Using the Telegram Bot API
- Building better models with JMP Pro
- C# machine learning projects : nine real-world projects to build robust and high-performing machine learning models with C#
- Computational Web intelligence : intelligent technology for Web applications
- Darwin among the machines : the evolution of global intelligence
- Deep Belief Nets in C++ and CUDA C, Volume 1, Restricted Boltzmann machines and supervised feedforward networks
- Deep Belief Nets in C++ and CUDA C, Volume 2, Autoencoding in the complex domain
- Deep Learning & Neural Network Implementation : Applying PCA
- Deep Learning & Neural Network Implementation : Classification and Bayesian Ridge
- Deep Learning & Neural Network Implementation : Data Sampling
- Deep Learning & Neural Network Implementation : Exercise: Working with Linear Regression
- Deep Learning & Neural Network Implementation : Gaussian Regression Process
- Deep Learning & Neural Network Implementation : Linear Model
- Deep Learning & Neural Network Implementation : Linear Regression Modelling
- Deep Learning & Neural Network Implementation : Logistic Regression Using Linear Method
- Deep Learning & Neural Network Implementation : Pre-Model and Workflow
- Deep Learning & Neural Network Implementation : Recurrent Neural Network
- Deep Learning for Natural Language Processing : Creating Neural Networks with Python
- Deep belief nets in C++ and CUDA C
- Deep belief nets in C++ and CUDA C, Volume 3, Convolutional nets
- Deep learning : a practitioner's approach
- Deep learning : a practitioner's approach
- Deep learning : practical neural networks with Java : build and run intelligent applications by leveraging key Java machine learning libraries : a course in three modules
- Deep learning and the game of Go
- Deep learning and the game of Go
- Deep learning essentials : your hands-on guide to the fundamentals of deep learning and neural network modeling
- Deep learning for computer vision : expert techniques to train advanced neural networks using TensorFlow and Keras
- Deep learning for natural language processing : creating neural networks with Python
- Deep learning for numerical applications with SAS
- Deep learning for search
- Deep learning quick reference : useful hacks for training and optimizing deep neural networks with TensorFlow and Keras
- Deep learning with Keras : implement neural networks with Keras on Theano and TensorFlow
- Deep learning with Microsoft Cognitive Toolkit quick start guide : a practical guide to building neural networks using Microsoft's open source deep learning framework
- Deep learning with PyTorch : a practical approach to building neural network models using PyTorch
- Deep learning with PyTorch quick start guide : learn to train and deploy neural network models in Python
- Deep learning with Python
- Deep learning with R
- Deep learning with Theano : build the artificial brain of the future, today
- Developing Bots with QnA Maker Service : Integration with Azure Bot Service and Microsoft Bot Framework
- Discrete-time inverse optimal control for nonlinear systems
- Efficient learning machines : theories, concepts, and applications for engineers and system designers
- Expert Systems & Reinforcement Learning : Clustering Concept
- Expert Systems & Reinforcement Learning : Datasets and Training Models
- Expert Systems & Reinforcement Learning : Defining Rules
- Expert Systems & Reinforcement Learning : Exercise: Working with Datasets and Clustering
- Expert Systems & Reinforcement Learning : Expert Systems Tools
- Expert Systems & Reinforcement Learning : Feature Search and Feature Evaluation Techniques
- Expert Systems & Reinforcement Learning : Graph Modeling
- Expert Systems & Reinforcement Learning : Hierarchical Clustering
- Expert Systems & Reinforcement Learning : Outlier Types
- Expert Systems & Reinforcement Learning : Principal Component Analysis Data Transformation
- Expert Systems & Reinforcement Learning : Supervised Learning and Notations
- Expert Systems & Reinforcement Learning : Working with Jess
- Fundamental of artificial neural network and fuzzy logic
- Fundamentals of deep learning : designing next-generation machine intelligence algorithms
- Generative adversarial networks cookbook : over 100 recipes to build generative models using Python, TensorFlow, and Keras
- Generative adversarial networks projects : build next-generation generative models using TensorFlow and Keras
- Generative deep learning : teaching machines to paint, write, compose, and play
- Getting started with deep learning
- Grokking deep learning
- Guidance for the verification and validation of neural networks
- Guide to Convolutional Neural Networks : A Practical Application to Traffic-Sign Detection and Classification
- Hands-on Java deep learning for computer vision : implement machine learning and neural network methodologies to perform computer vision-related tasks
- Hands-on computer vision with Julia : build complex applications with advanced Julia packages for image processing, neural networks, and artificial intelligence
- Hands-on convolutional neural networks with TensorFlow : solve computer vision problems with modeling in TensorFlow and Python
- Hands-on deep learning for games : leverage the power of neural networks and reinforcement learning to build intelligent games
- Hands-on neural network programming with C# : add powerful neural network capabilities to your C# enterprise applications
- Hands-on neural networks with Keras : design and create neural networks using deep learning and artificial intelligence principles
- Hands-on transfer learning with Python : implement advanced deep learning and neural network models using TensorFlow and Keras
- How smart machines think
- Industrial image processing : visual quality control in manufacturing
- Intelligent projects using Python : 9 real-world AI projects leveraging machine learning and deep learning with TensorFlow and Keras
- Introduction to deep learning using R : a step-by-step guide to learning and implementing deep learning models using R
- Keras 2.x projects : 9 projects demonstrating faster experimentation of neural network and deep learning applications using Keras
- Keras deep learning cookbook : over 30 recipes for implementing deep neural networks in Python
- Learn Keras for Deep Neural Networks : A Fast-Track Approach to Modern Deep Learning with Python
- Learn Keras for deep neural networks : a fast-track approach to modern deep learning with Python
- MATLAB Deep Learning : With Machine Learning, Neural Networks and Artificial Intelligence
- MATLAB deep learning : with machine learning, neural networks and artificial intelligence
- MATLAB for machine learning : functions, algorithms, and use cases
- Make your own neural network : a gentle journey through the mathematics of neural networks, and making your own using the Python computer language
- Memristor networks
- Natural language processing with Java : techniques for building machine learning and neural network models for NLP
- Natural language processing with Java cookbook : over 70 recipes to create linguistic and language translation applications using Java libraries
- Neural Network & NLP Implementation : Classifying Text and Documents
- Neural Network & NLP Implementation : Components of NLP
- Neural Network & NLP Implementation : Detecting Parts of Speech
- Neural Network & NLP Implementation : Exercise: Working with NLP Components
- Neural Network & NLP Implementation : Implementing Multilayer Networks
- Neural Network & NLP Implementation : Language and Sentence
- Neural Network & NLP Implementation : Multilayer Networks and Computation Graphs
- Neural Network & NLP Implementation : NLP Introduction
- Neural Network & NLP Implementation : Speech Implementation
- Neural Network & NLP Implementation : Tokenizer and Name Finder
- Neural Network & NLP Implementation : Using Parser to Extract Relationships
- Neural network programming with Java : create and unleash the power of neural networks by implementing professional Java code
- Neural network projects with Python : the ultimate guide to using Python to explore the true power of neural networks through six projects
- Neural networks and deep learning : a textbook
- Neural networks for RF and microwave design
- Neural networks with Keras cookbook : over 70 recipes leveraging deep learning techniques across image, text, audio, and game bots
- Neural networks with R : smart models using CNN, RNN, deep learning, and artificial intelligence principles
- Neural smithing : supervised learning in feedforward artificial neural networks
- Neuro-fuzzy equalizers for mobile cellular channels
- On intelligence
- Power converters and AC electrical drives : with linear neural networks
- Practical convolutional neural networks : implement advanced deep learning models using Python
- Predicting diameter distributions of longleaf pine plantations : a comparison between artificial neural networks and other accepted methodologies
- Principles of artificial neural networks, 3rd edition
- Pro deep learning with TensorFlow : a mathematical approach to advanced artificial intelligence in Python
- PyTorch recipes : a problem-solution approach
- Python deep learning : exploring deep learning techniques and neural network architectures with PyTorch, Keras, and TensorFlow
- Python deep learning : next generation techniques to revolutionize computer vision, AI, speech and data analysis
- Python deep learning projects : 9 projects demystifying neural network and deep learning models for building intelligent systems
- Python for programmers : with introductory AI case studies
- R deep learning cooking : solve complex neural net problems with TensorFlow, H2O and MXNet
- R deep learning essentials : a step-by-step guide to building deep learning models using TensorFlow, Keras, and MXNet
- Recurrent neural networks with Python Quick Start Guide : sequential learning and language modeling with TensorFlow
- Reinforcement learning with TensorFlow : a beginner's guide to designing self-learning systems with TensorFlow and OpenAI Gym
- Shi zhan Google shen du xue xi ji shu : shi yong TensorFlow
- Soft computing : concepts and techniques
- Soft computing for image and multimedia data processing
- TensorFlow 2.0 quick start guide : get up to speed with the newly introduced features of TensorFlow 2.0
- TensorFlow reinforcement learning quick start guide : get up and running with training and deploying intelligent, self-learning agents using Python
- Total recall : how the E-memory revolution will change everything
- Using neural networks to correlate satellite imagery and ground-truth data
- Wind power plant prediction by using neural networks : preprint
- Your life, uploaded : the digital way to better memory, health, and productivity

## Embed (Experimental)

### Settings

Select options that apply then copy and paste the RDF/HTML data fragment to include in your application

Embed this data in a secure (HTTPS) page:

Layout options:

Include data citation:

<div class="citation" vocab="http://schema.org/"><i class="fa fa-external-link-square fa-fw"></i> Data from <span resource="http://link.sfpl.org/resource/3LyspnMhOzs/" typeof="CategoryCode http://bibfra.me/vocab/lite/Concept"><span property="name http://bibfra.me/vocab/lite/label"><a href="http://link.sfpl.org/resource/3LyspnMhOzs/">Neural networks (Computer science)</a></span> - <span property="potentialAction" typeOf="OrganizeAction"><span property="agent" typeof="LibrarySystem http://library.link/vocab/LibrarySystem" resource="http://link.sfpl.org/"><span property="name http://bibfra.me/vocab/lite/label"><a property="url" href="http://link.sfpl.org/">San Francisco Public Library</a></span></span></span></span></div>

Note: Adjust the width and height settings defined in the RDF/HTML code fragment to best match your requirements

### Preview

## Cite Data - Experimental

### Data Citation of the Concept Neural networks (Computer science)

Copy and paste the following RDF/HTML data fragment to cite this resource

`<div class="citation" vocab="http://schema.org/"><i class="fa fa-external-link-square fa-fw"></i> Data from <span resource="http://link.sfpl.org/resource/3LyspnMhOzs/" typeof="CategoryCode http://bibfra.me/vocab/lite/Concept"><span property="name http://bibfra.me/vocab/lite/label"><a href="http://link.sfpl.org/resource/3LyspnMhOzs/">Neural networks (Computer science)</a></span> - <span property="potentialAction" typeOf="OrganizeAction"><span property="agent" typeof="LibrarySystem http://library.link/vocab/LibrarySystem" resource="http://link.sfpl.org/"><span property="name http://bibfra.me/vocab/lite/label"><a property="url" href="http://link.sfpl.org/">San Francisco Public Library</a></span></span></span></span></div>`