100 Best Neural Network Tutorial Videos

Notes:

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

1. 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.
2. 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.
3. 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.
4. 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.
5. 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:

1. 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.
2. 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.
3. 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

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[118x Nov 2017]

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• Tutorial on Convolutional Neural Networks(CNNs) for image recognition
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• Neural Network Tutorial – Ch. 11.3: Permutator
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• Neural Network Tutorial – Ch. 10.4: Ball balance demo
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• 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)
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• Neural network tutorial: The back-propagation algorithm (Part 1)