Description

Neural networks are at the heart of AI—so ensure you’re on the cutting edge with this guide! For true beginners, get a crash course in Python and the mathematical concepts you’ll need to understand and create neural networks. Or jump right into programming your first neural network, from implementing the scikit-learn library to using the perceptron learning algorithm. Learn how to train your neural network, measure errors, make use of transfer learning, implementing the CRISP-DM model, and more. Whether you’re interested in machine learning, gen AI, LLMs, deep learning, or all of the above, this is the AI book you need! * Your practical introduction to programming neural networks * Develop and train simple and multi-layer networks with Python * Learn about algorithms, activation functions, transformers, and more **The Basics** Learn about neural networks from the ground up! Understand how neural networks work and what their basic elements are, from algorithms and activation functions to transformers. Includes a primer on mathematics and Python for beginners! **Putting Theory into Practice** Develop different types of neural networks: simple ones, multi-layer ones, and even deep neural networks. Walk through diverse practical examples, from image classification to large language models (LLMs). **Letting the Machine’s Learn** Train your newly created (or modified!) neural network. Get expert tips on skillfully using training data, selecting the right tools, increasing the hit rates of your models, and avoiding pitfalls. * Network creation * Network training * Supervised and unsupervised learning * Reinforcement learning * Algorithms * Multi-layer networks * Deep neural networks * Back propagation * Transformers * Python * Mathematical concepts * TensorFlow