Notes:

There are several ways to learn about and use neural networks:

- Online courses and tutorials: There are many online resources available that can help you learn about neural networks and how to implement them. These can include video courses, tutorials, and articles that provide a foundation in the concepts and techniques of neural network design and implementation.
- Books and textbooks: There are also many books and textbooks available that cover neural networks in detail. These can be a useful resource for learning about the theory and practice of neural network design and implementation.
- Hands-on experience: One of the best ways to learn about neural networks is through hands-on experience. This can involve working on projects or exercises that involve designing and training neural networks, and experimenting with different architectures and hyperparameters to see how they affect the performance of the network.
- Research papers and conference proceedings: Reading research papers and conference proceedings can also be a valuable way to learn about neural networks and stay up-to-date on the latest developments in the field. These resources can provide in-depth information about specific topics and techniques related to neural network design and implementation.
- Online communities: Joining online communities or forums dedicated to neural networks can also be a helpful way to learn about the field and connect with others who are interested in the same topics. These communities can provide a wealth of information and support as you learn about and work with neural networks.

There are many ways to get started with making neural networks, and the quickest and easiest way will depend on your specific goals and resources. Here are a few options you might consider:

- Use a pre-trained neural network: One of the quickest and easiest ways to get started with neural networks is to use a pre-trained model. Many organizations and individuals have released pre-trained models that you can use for a variety of tasks, such as image classification, language translation, and more. These models can be fine-tuned for your specific needs, or used as-is.
- Use a neural network library or framework: There are many libraries and frameworks available that make it easy to build and train neural networks. These include popular options like TensorFlow, PyTorch, and Keras. These libraries provide a range of pre-built neural network architectures and functions that you can use to quickly and easily build and train your own models.
- Follow a tutorial or online course: Another option is to follow a tutorial or take an online course to learn about neural networks and how to build them. There are many resources available that provide step-by-step instructions and examples for building neural networks using a variety of tools and techniques.

Wikipedia:

- Category:Neural networks
- Category:Neural network software
- Neural network
- Types of artificial neural networks

See also:

Neural Networks & Dialog Systems

- Neural network tutorial The back propagation algorithm Part 2
- Neural network tutorial The back propagation algorithm Part 1
- Train Neural Networks with Pso Algorithm Tutorial overview
- Neural network tutorial The back propagation algorithm Part 2
- Neural network tutorial The back propagation algorithm Part 1
- Hands-on tutorial for modelling a Neural Network employing NNTOOL in MATLAB
- Deep Neural Network Example Programming with Particle Swarm Optimization Tutorial overview
- Max/MSP Neural Network Tutorial 4: All About Layers
- Max/MSP Neural Network Tutorial 3: Building the Network!
- Max/MSP Neural Network Tutorial 2: Abstracting the Neuron & Troubleshooting
- Max/MSP Neural Network Tutorial 1: Our First Neuron!
- Neural Networks w/ JAVA (Backpropagation 02) – Tutorial 10
- NEURAL NETWORKS TUTORIAL WITH PYBRAIN
- Neural Networks w/ JAVA (Backpropagation 01) – Tutorial 09
- Recurrent Neural Network Tutorial on Deep learning Text Book
- Neural Networks Tutorial – Introduction to Neural Networks and Deep Learning
- Neural Networks w/ JAVA (Hopfield Network) – Tutorial 08
- Neural networks tutorial: Fully Connected 10 [Java] – Saving and loading
- Neural Network – Back-Propagation Tutorial In C#
- Recurrent Neural Networks (RNN) | RNN LSTM | Deep Learning Tutorial | Tensorflow Tutorial | Edureka
- Tensorflow Tutorial 2 – A very simple Convolutional Neural Network
- Tensorflow Tutorial 1 – A very simple Neural Network
- Neural networks tutorial: Fully Connected 9 [Java] – Mnist dataset
- Neural networks tutorial: Fully Connected 8 [Java] – Advanced learning
- Neural networks tutorial: Fully Connected 7 [Java] – Backpropagation implementation
- Neural networks tutorial: Fully Connected 6 [Java] – Backpropagation algorithm
- Artificial Neural Network Tutorial | Deep Learning With Neural Networks | Edureka
- Tutorial RapidMiner Data Mining Neural Network
- CERN ROOT tutorial for beginners — topic: neural network by TMultiLayerPerceptron and TMLPAnalyzer
- Evolving Neural Networks NEAT With 3D Cars + Tutorial
- TUTORIAL NEURAL NETWORK USING MATLAB
- Neural networks tutorial: Fully Connected 5 [Java] – Network Tools
- Neural networks tutorial: Fully Connected 4 [Java] – Feed Forward Implementation
- Neural networks tutorial: Fully Connected 3 [Java] – Feed Forward Intro
- Deep Learning Tutorial | Deep Learning Tutorial for Beginners | Neural Networks | Edureka
- Neural networks tutorial: Fully Connected 2 [Java] – Basic structure
- Tutorial On Programming An Evolving Neural Network In C# w/ Unity3D
- Neural networks tutorial: Fully Connected 1 [Java]
- Neural Networks w/ JAVA (Solve XOR w/ Simulated Annealing) – Tutorial 07
- Neural Networks w/ JAVA (Tutorial 06) – Solve XOR w/ Hill Climbing
- Neural Networks Tutorial – How to Train a Neural Network
- 4. Matlab Tutorial – Neural Network
- Soft Computing Lecture 9 Neural Network Architecture |tutorial|ai|sanjaypathakjec
- Soft Computing Lecture 3 Neural Network in ai artificial intelligence |tutorial|sanjaypathakjec
- Neural Network Tutorial XOR
- Neural Network Tutorial and Visualization (Python and PyQt – part 1)
- Neural Network Tutorial and Visualization (setting up – part 2)
- Neural Network Tutorial Visualized (setting up – part 3)
- Neural Network Tutorial and Visualization (setting up – part 5 – ForwardProp)
- Neural Network Tutorial and Visualization (setting up – part 4)
- Neural Network Tutorial and Visualization (setting up – part 6 – BackProp)
- Neural Network Tutorial and Visualization (setting up – part 7 – Finishing)
- Neural Networks w/ JAVA – Tutorial 05
- Recurrent Neural Networks – A Short TensorFlow Tutorial
- Soft Computing | Tutorial #3 | Biological Neural Networks
- tutorial Neural Network: perceptron
- tutorial Neural Network: adaline
- Neural Networks w/ JAVA – Tutorial 04
- Neural Networks Tutorial – Intro to Neural Networks, Deep Learning, and the Tensorflow API
- Neural Networks w/ JAVA – Tutorial 03
- Neural Networks w/ JAVA – Tutorial 02
- Artificial Neural Network Tutorial
- Tutorial Rapidminer Data Mining Neural Network Dataset Training and Scoring
- Tutorial on Convolutional Neural Networks(CNNs) for image recognition
- Artificial Neural Network Tutorial [FAIL]
- TensorFlow Tutorial #02 Convolutional Neural Network
- Tutorial: Large-Scale Distributed Systems for Training Neural Networks
- Tutorial: Large-Scale Distributed Systems for Training Neural Networks
- ARTIFICIAL NEURAL NETWORKS (ANN) TUTORIAL
- Tutorial NEURAL NETWORK in course Multivariate Data Analysis
- SAS Enterprise Miner Tutorial Video – Neural Network
- Neural Networks w/ JAVA – Tutorial 01
- Neural Networks Tutorial – An Introduction to Neural Networks
- IRIS Flower data set tutorial in artificial neural network in matlab
- Neural Network Deep Style Video Workflow Tutorial (Vue, Torch, Neural-style, After Effects)
- Neural Networks – Tutorial
- Neural network tutorial: The back-propagation algorithm (Part 1)
- Neural Network Image Processing Tutorial
- Neural Network Animation Tutorial
- Tutorial: How to Train a Neural Network with Azure Machine Learning
- FINANCIAL RISK MANAGEMENT TUTORIAL – NEURAL NETWORK APPLICATION TO FINANCIAL HEDGING
- Tutorial: Neural network porosity prediction in OpendTect 3.0
- Neural Network Tutorial
- Neural Network Tutorial – Ch. 14: Code Release!
- Neural Network Tutorial – Ch. 13.2.2: Binary Noise Trainer
- Neural Network Tutorial – Ch. 13.2.1: Binary Noise Trainer
- Neural Network Tutorial – Ch. 13.1: Character Recognition Intro
- Neural Network Tutorial – Ch. 12.5.2: NetworkTrainer class (2/3)
- Neural Network Tutorial – Ch. 12.5.1: NetworkTrainer class (1/3)
- Neural Network Tutorial – Ch. 12.5.3: NetworkTrainer class (3/3)
- Neural Network Tutorial – Ch. 12.4: Nudge (Part 2)
- Neural Network Tutorial – Ch. 12.3: Nudge (Part 1)
- Neural Network Tutorial – Ch. 12.2: Training Improvements
- Neural Network Tutorial – Ch. 6.3: Train method momentum
- Neural Network Tutorial – Ch. 12.1: Simple network trainer
- Neural Network Tutorial – Ch. 11.3: Permutator
- Neural Network Tutorial – Ch. 11.2: Data set
- Neural Network Tutorial – Ch. 11.1: Data point
- Neural Network Tutorial – Ch. 10.4: Ball balance demo
- Neural Network Tutorial – Ch. 10.3 Example: Parabola fitting
- Neural Network Tutorial – Ch. 10.1 Example: XOR-Gate (Part 1)
- Neural Network Tutorial – Ch. 10.2 Example: XOR-Gate (Part 2)
- Neural Network Tutorial – Ch. 9.2 Load method (Part 2)
- Neural Network Tutorial – Ch. 9.1 Load method (Part 1)
- Neural Network Tutorial – Ch. 8.1 Save method (Part 1)
- Neural Network Tutorial – Ch. 8.2 Save method (Part 2)
- Neural Network Tutorial – Ch. 7.2 More transfer functions (Part 2)
- Neural Network Tutorial – Ch. 7.1 More transfer functions (Part 1)
- Neural Network Tutorial – Ch. 6.2 Training the network (Part 2)
- Neural Network Tutorial – Ch. 6.1 Training the network
- Neural Network Tutorial – Ch. 5 Running the network
- Neural Network Tutorial – Ch. 4.1 The constructor (part 1)
- Neural Network Tutorial – Ch. 4.2 The constructor (part 2)
- Neural Network Tutorial – Ch. 2 Back Propagation library
- Neural Network Tutorial – Ch. 3 Private variables
- Neural Network Tutorial – Ch. 1 Back Propagation library
- Neural network tutorial: The back-propagation algorithm (Part 2)
- Neural network tutorial: The back-propagation algorithm (Part 1)