توضیحات
Written by Tariq Rashid, this book introduces the fundamental concepts of artificial neural networks (ANNs) and machine learning without requiring prior advanced knowledge. The author focuses on a hands-on approach, showing how neural networks operate, learn, and make predictions using practical Python examples.
Key topics include:
Introduction to Neural Networks: Understanding neurons, layers, and activation functions.
Mathematical Foundations: Explaining concepts like weighted sums, sigmoid functions, and gradient descent in an approachable manner.
Feedforward Networks: How data moves through layers and generates predictions.
Backpropagation Algorithm: Step-by-step explanation of how neural networks learn from errors.
Python Implementation: Writing your own neural network code without relying on heavy frameworks.
Training and Testing: Using datasets to train the network and evaluate its performance.
Practical Examples: Building networks for pattern recognition, classification, and simple AI tasks.
By the end of this book, readers will understand how neural networks function, be able to code a basic neural network from scratch, and have the foundation to explore more advanced machine learning and deep learning techniques.
نقد و بررسیها
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