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Neural networks (Computer science)
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The concept ** Neural networks (Computer science)** represents the subject, aboutness, idea or notion of resources found in **Merrimack Valley Library Consortium**.

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Neural networks (Computer science)
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**Neural networks (Computer science)**represents the subject, aboutness, idea or notion of resources found in**Merrimack Valley Library Consortium**.- Label
- Neural networks (Computer science)

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- What's new in TensorFlow 2.0 : use the new and improved features of TensorFlow to enhance machine learning and deep learning
- 3D neural network visualization with TensorSpace
- Advanced NLP projects with TensorFlow 2.0
- Advanced applied deep learning : convolutional neural networks and object detection
- Advanced deep learning with Keras
- Advanced deep learning with Keras : apply deep learning techniques, autoencoders, GANs, variational autoencoders, deep reinforcement learning, policy gradients, and more
- Advanced deep learning with Python : design and implement advanced next-generation AI solutions using TensorFlow and PyTorch
- Advanced deep learning with R : become an expert at designing, building, and improving advanced neural network models using R
- Advanced deep learning with TensorFlow 2 and Keras : apply DL, GANs, VAEs, deep RL, unsupervised learning, object detection and segmentation, and more
- Analog VLSI circuits for the perception of visual motion
- Applied deep learning : a case-based approach to understanding neural networks
- Applied machine learning and deep learning with R
- Applied machine learning with R
- Applied natural language processing with Python : implementing machine learning and deep learning algorithms for natural language processing
- Artificial intelligence and machine learning fundamentals
- Artificial intelligence and machine learning fundamentals
- Artificial intelligence by example : acquire advanced AI, machine learning, and deep learning design skills
- Artificial neural network for software reliability prediction
- Artificial neural networks with Java : tools for building neural network applications
- Avoiding the pitfalls of deep learning : solving model overfitting with regularization and dropout
- Building Recommender systems with machine learning and AI
- C# machine learning projects : nine real-world projects to build robust and high-performing machine learning models with C#
- Customizing state-of-the-art deep learning models for new computer vision solutions
- Data analytics and machine learning fundamentals : LiveLessons
- 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 belief nets in C++ and CUDA C, Volume 3, Convolutional nets
- Deep learning : a practitioner's approach
- Deep learning : moving toward artificial intelligence with neural networks and machine learning
- 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 architecture for building artificial neural networks
- Deep learning essentials : your hands-on guide to the fundamentals of deep learning and neural network modeling
- Deep learning for coders with fastai and PyTorch : AI applications without a PhD
- Deep learning for computer vision : expert techniques to train advanced neural networks using TensorFlow and Keras
- Deep learning for health tech : neural network applications in healthcare using Python and TensorFlow
- Deep learning for natural language processing : creating neural networks with Python
- Deep learning for numerical applications with SAS
- Deep learning from scratch : building with Python from first principles
- Deep learning quick reference : useful hacks for training and optimizing deep neural networks with TensorFlow and Keras
- Deep learning with Apache Spark
- Deep learning with JavaScript : neural networks in TensorFlow.js
- 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
- Deep learning with PyTorch 1.x : implement deep learning techniques and neural network architecture variants using PyTorch
- 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 Python video edition
- Deep learning with R
- Deep learning with R cookbook : over 45 unique recipes to delve into neural network techniques using R 3.5x
- Deep learning with R for beginners : design neural network models in R 3.5 using TensorFlow, Keras, and MXNet
- Deep learning with R in motion
- Deep learning with TensorFlow
- Deep learning with TensorFlow 2 and Keras : regression, ConvNets, GANs, RNNs, NLP, and more with TensorFlow 2 and the Keras API
- Deep learning with TensorFlow, Keras, and PyTorch
- Deep learning with real world projects
- Deep neuro-fuzzy systems with Python : with case studies and applications from the industry
- Developing an image classifier using TensorFlow : convolutional neural networks
- Discrete-time inverse optimal control for nonlinear systems
- Distributed deep learning with Apache Spark
- Dynamic neural network programming with PyTorch
- Exploring neural networks with C#
- Fighting crime with graph learning
- Foundations of deep reinforcement learning : theory and practice in Python
- Fundamentals of deep learning : designing next-generation machine intelligence algorithms
- GANs in action : deep learning with Generative adversarial networks
- 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 TensorFlow for deep learning
- Getting started with deep learning
- Grokking deep learning
- Grokking deep learning in motion
- Hands-on Generative Adversarial Networks with PyTorch 1.x : implement next-generation neural networks to build powerful GAN models using Python
- Hands-on Java deep learning for computer vision : implement machine learning and neural network methodologies to perform computer vision-related tasks
- Hands-on TensorBoard for PyTorch developers
- 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 algorithms with Python : master deep learning algorithms with extensive math by implementing them using TensorFlow
- Hands-on deep learning for games : leverage the power of neural networks and reinforcement learning to build intelligent games
- Hands-on deep learning with Go : a practical guide to building and implementing neural network models using Go
- Hands-on deep learning with R : a practical guide to designing, building, and improving neural network models using R
- Hands-on deep learning with TensorFlow
- Hands-on generative adversarial networks with Keras : your guide to implementing next-generation generative adversarial networks
- 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 neuroevolution with Python : build high-performing artificial neural network architectures using neuroevolution-based algorithms
- Hands-on one-shot learning with Python : learn to implement fast and accurate deep learning models with fewer training samples using PyTorch
- Hands-on transfer learning with Python : implement advanced deep learning and neural network models using TensorFlow and Keras
- Hands-on unsupervised learning with TensorFlow 2.0
- Image analysis and text classification using CNNs in PyTorch : learn to build powerful image and document classifiers in minutes
- Intelligent projects using Python : 9 real-world AI projects leveraging machine learning and deep learning with TensorFlow and Keras
- Introduction to computer vision with TensorFlow : using convolutional neural networks and TensorFlow to solve computer vision tasks
- Introduction to convolutional neural networks : with image classification using PyTorch
- Introduction to deep learning : concepts and fundamentals
- Introduction to deep learning using PyTorch : create simple neural networks in Python using PyTorch
- Introduction to deep learning using R : a step-by-step guide to learning and implementing deep learning models using R
- Java deep learning cookbook : train neural networks for classification, NLP, and reinforcement learning using Deeplearning4j
- Keras 2.x projects : 3 projects demonstrating faster experimentation of neural network and deep learning applications using Keras
- 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
- Keras tips, tricks, and techniques
- Learn Keras for deep neural networks : a fast-track approach to modern deep learning with Python
- Learning neural networks with TensorFlow
- Live-coding a machine learning model from scratch
- MATLAB deep learning : with machine learning, neural networks and artificial intelligence
- MATLAB for machine learning : functions, algorithms, and use cases
- MATLAB machine learning
- Machine learning 101 with Scikit-Learn and StatsModels
- Machine learning : theory and applications
- Machine learning in R : automated algorithms for business analysis : applying K-Means clustering, decision trees, random forests, and neural networks
- Mastering Keras
- Mastering computer vision problems with state-of-the-art deep learning architectures, MXNet, and GPU virtual machines
- Mastering deep learning using Apache Spark
- Mobile deep learning with TensorFlow Lite, ML Kit and Flutter : build scalable real-world projects to implement end-to-end neural networks on Android and iOS
- 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 and fuzzy logic control of drives and power systems
- 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 pattern recognition
- Neural networks in finance : gaining predictive edge in the market
- 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 networks, Part 5, Introduction to real-world machine learning
- Neuro-fuzzy equalizers for mobile cellular channels
- Power converters and AC electrical drives : with linear neural networks
- Practical MATLAB deep learning : a project-based approach
- Practical computer vision applications using deep learning with CNNs : with detailed examples in Python using TensorFlow and Kivy
- Practical convolutional neural networks : implement advanced deep learning models using Python
- Practical deep learning on the cloud
- Practical neural network recipes in C++
- Practical neural networks and deep learning in R
- Pro deep learning with TensorFlow : a mathematical approach to advanced artificial intelligence in Python
- PyTorch bootcamp for artificial neural networks and deep learning applications
- 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
- Python fundamentals
- 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
- Random search and reproducibility for neural architecture search
- Real-Time IoT imaging with deep neural networks : using Java on the Raspberry Pi 4
- 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
- Sequence to sequence learning for time series forecasting
- Sooner than you think : neural interfaces are finally here
- 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
- The business of deep learning : understanding deep learning and discovering real world applications
- Understanding convolutional neural networks (CNNs) : learn how to implement CNNs to generate visualizations
- Wavelet neural networks : with applications in financial engineering, chaos, and classification

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