Neural Networks Beginnings

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Группа авторов. Neural Networks Beginnings

Introduction

Chapter 1: Basics of Neural Networks

Chapter 2: Training Neural Networks

Chapter 3: Main Types of Neural Networks

Chapter 4: Application of Neural Networks

Chapter 5: Advanced Topics in Neural Networks

Dictionary of Terms

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Neural networks are a powerful tool in the field of artificial intelligence and machine learning. They are used in many applications, such as speech recognition, image processing, and forecasting. However, to understand how a neural network works, one must start with the basics.

The basic unit of a neural network is a neuron. A neuron is a simple information processing unit that mimics the function of a nerve cell in our brain. A neuron receives input signals from other neurons and generates an output signal that is passed on to other neurons.

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Then a sound file in the wav format is loaded, decoded using tf.audio.decode_wav, and transformed into float32 numerical values. The file is then split into fragments of length 640 with a step of 320. If the file cannot be divided into equal fragments, padding is added.

This code implements automatic speech recognition using a neural network based on TensorFlow and Keras. The first step is to define the architecture of the neural network using the Keras Sequential API. In this case, a recurrent LSTM layer is used, which takes in a sequence of 13-length sound snippets. Then there are several fully connected layers with the relu activation function, and one output layer with the softmax activation function, which outputs probabilities for each speech class.

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