Tools and Machines (Experimenting With Everyday Science) | 8.2 MB
Title: Experimenting with Everyday Science: Tools and Machines
Author: Stephen M. Tomecek
Category: Nonfiction, Computers, Advanced Computing, Engineering, Computer Vision, Theory, General Computing
Language: English | 183 Pages | ISBN: 9798266973213
Description:
The potential of machine learning today is extraordinary, yet many aspiring developers and tech professionals find themselves daunted by its complexity. Whether you’re looking to enhance your skill set and apply machine learning to real-world projects or are simply curious about how AI systems function, this book is your jumping-off place.
With an approachable yet deeply informative style, author Aurélien Géron delivers the ultimate introductory guide to machine learning and deep learning. Drawing on the Hugging Face ecosystem, with a focus on clear explanations and real-world examples, the book takes you through cutting-edge tools like Scikit-Learn and PyTorch-from basic regression techniques to advanced neural networks. Whether you’re a student, professional, or hobbyist, you’ll gain the skills to build intelligent systems.
The potential of machine learning today is extraordinary, yet many aspiring developers and tech professionals find themselves daunted by its complexity. Whether you’re looking to enhance your skill set and apply machine learning to real-world projects or are simply curious about how AI systems function, this book is your jumping-off place.
With an approachable yet deeply informative style, author Aurélien Géron delivers the ultimate introductory guide to machine learning and deep learning. Drawing on the Hugging Face ecosystem, with a focus on clear explanations and real-world examples, the book takes you through cutting-edge tools like Scikit-Learn and PyTorch-from basic regression techniques to advanced neural networks. Whether you’re a student, professional, or hobbyist, you’ll gain the skills to build intelligent systems.
- Understand ML basics, including concepts like overfitting and hyperparameter tuning
- Complete an end-to-end ML project using scikit-Learn, covering everything from data exploration to model evaluation
- Learn techniques for unsupervised learning, such as clustering and anomaly detection
- Build advanced architectures like transformers and diffusion models with PyTorch
- Harness the power of pretrained models-including LLMs-and learn to fine-tune them
- Train autonomous agents using reinforcement learning
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