The way to connect the nodes, the number of layers present, that is, the levels of nodes between input and output, and the number of neurons per layer, defines the architecture of a neural network. There are various types of architecture in neural networks, but this book will focus mainly on two large architectural families.
Neural network architectures
by Ahmed Menshawy, Md. Rezaul Karim, Giancarlo Zaccone
Deep Learning with TensorFlow
- Preface
- Getting Started with Deep Learning
- First Look at TensorFlow
- General overview
- Installing TensorFlow on Linux
- Requirements for running TensorFlow with GPU from NVIDIA
- How to install TensorFlow
- Installing TensorFlow on Windows
- Computational graphs
- Why a computational graph?
- The programming model
- Data model
- TensorBoard
- Implementing a single input neuron
- Source code for the single input neuron
- Migrating to TensorFlow 1.x
- Summary
- Using TensorFlow on a Feed-Forward Neural Network
- TensorFlow on a Convolutional Neural Network
- Optimizing TensorFlow Autoencoders
- Recurrent Neural Networks
- GPU Computing
- Advanced TensorFlow Programming
- Advanced Multimedia Programming with TensorFlow
- Reinforcement Learning
Neural network architectures
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