This book starts by introducing you to supervised learning algorithms such as simple linear regression, the classical multilayer peceptron, and more sophisticated deep convolutional networks. You'll also explore image processing, Recurrent Networks, and unsupervised learning algorithms such as Autoencoders. Finally, you'll take a look at Reinforcement Learning and its application to AI game playing, another popular direction of research and application of neural networks.
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- Optimize step-by-step functions on a large neural network using the Backpropagation Algorithm
- Fine tune a neural network to improve the quality of results
- Use deep learning for image & audio processing
- Utilize Recursive Neural Tensor Networks (RNTNs) to outperform standard word embedding in special cases
- Identify problems for which Recurrent Neural Network (RNN) solutions are suitable
- Explore the process required to implement Autoencoders
- Evolve a deep neural network using reinforcement learning
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