Machine Learning and Deep Learning With Python
- Indbinding:
- Hardback
- Sideantal:
- 376
- Udgivet:
- 22. februar 2023
- Størrelse:
- 157x25x235 mm.
- Vægt:
- 711 g.
- 2-3 uger.
- 2. december 2024
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- 1 valgfrit digitalt ugeblad
- 20 timers lytning og læsning
- Adgang til 70.000+ titler
- Ingen binding
Abonnementet koster 75 kr./md.
Ingen binding og kan opsiges når som helst.
- 1 valgfrit digitalt ugeblad
- 20 timers lytning og læsning
- Adgang til 70.000+ titler
- Ingen binding
Abonnementet koster 75 kr./md.
Ingen binding og kan opsiges når som helst.
Beskrivelse af Machine Learning and Deep Learning With Python
This book is a comprehensive guide to understanding and implementing cutting-edge machine learning and deep learning techniques using the Python programming language. Written with both beginners and experienced developers in mind, this book provides a thorough overview of the foundations of machine learning and deep learning, including mathematical fundamentals, optimization algorithms and neural networks.
Starting with the basics of Python programming, this book gradually builds up to more advanced topics, such as artificial and convolutional neural networks, generative adversarial networks. Each chapter is filled with clear explanations, practical examples, and step-by-step tutorials that allow readers to gain a deep understanding of the underlying principles of machine learning and deep learning.
Throughout the book, readers will also learn how to use popular Python libraries and packages, including numpy, pandas, scikit-learn, TensorFlow, and Keras, to build and train powerful machine learning and deep learning models for a variety of real-world applications, such as regressions and classification, K-means, support vector machines, and recommender systems.
Whether you are a seasoned data scientist or a beginner looking to enter the world of machine learning, this book is the ultimate resource for mastering these cutting-edge technologies and taking your skills to the next level. High-school level of mathematical knowledge and all levels (including entry-level) of programming skills are good to start, all Python codes are available at Github.com.
Starting with the basics of Python programming, this book gradually builds up to more advanced topics, such as artificial and convolutional neural networks, generative adversarial networks. Each chapter is filled with clear explanations, practical examples, and step-by-step tutorials that allow readers to gain a deep understanding of the underlying principles of machine learning and deep learning.
Throughout the book, readers will also learn how to use popular Python libraries and packages, including numpy, pandas, scikit-learn, TensorFlow, and Keras, to build and train powerful machine learning and deep learning models for a variety of real-world applications, such as regressions and classification, K-means, support vector machines, and recommender systems.
Whether you are a seasoned data scientist or a beginner looking to enter the world of machine learning, this book is the ultimate resource for mastering these cutting-edge technologies and taking your skills to the next level. High-school level of mathematical knowledge and all levels (including entry-level) of programming skills are good to start, all Python codes are available at Github.com.
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