NEURAL NETWORKS FROM SCRATCH IN PYTHON

In this book, we embark on a comprehensive journey through the
fascinating world of neural networks, exploring their practical uses,
fundamental elements, implementation in Python, and advanced
applications in deep learning and natural language processing.
Neural networks have revolutionized various fields, from computer
vision and natural language processing to recommender systems and
chatbots. Their ability to learn complex patterns and make
predictions from data has made them indispensable tools in the age
of artificial intelligence.
In the first part of this book, we provide an overview of neural
networks, discussing their practical applications in image
computing, natural language processing, and recommendation
systems. We delve into the essential elements of neural networks,
including neurons, activation functions, layers, and backpropagation,
laying the groundwork for understanding their architecture and
operation.
Understanding the mathematics behind neural networks is crucial for
their implementation and optimization. Therefore, we dedicate a
section to the fundamentals of neural network mathematics, covering
linear algebra, matrix operations, and derivatives, with practical
examples and insights into optimization techniques.
The heart of neural network training lies in gradient descent and
backpropagation algorithms. We provide a comprehensive
introduction to these algorithms, discussing their principles,
implementation, and optimization strategies for improving learning
efficiency.