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Handson Machine Learning With Scikitlearn And Tensorflow: Concepts, Tools, And Techniques To Build Intelligent Systems
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Graphics In This Book Are Printed In Black And White.Through A Series Of Recent Breakthroughs, Deep Learning Has Boosted The Entire Field Of Machine Learning. Now, Even Programmers Who Know Close To Nothing About This Technology Can Use Simple, Efficient Tools To Implement Programs Capable Of Learning From Data. This Practical Book Shows You How.By Using Concrete Examples, Minimal Theory, And Two Productionready Python Frameworksscikitlearn And Tensorflowauthor Aur?Lien G?Ron Helps You Gain An Intuitive Understanding Of The Concepts And Tools For Building Intelligent Systems. You?Ll Learn A Range Of Techniques, Starting With Simple Linear Regression And Progressing To Deep Neural Networks. With Exercises In Each Chapter To Help You Apply What You?Ve Learned, All You Need Is Programming Experience To Get Started. Explore The Machine Learning Landscape, Particularly Neural Nets Use Scikitlearn To Track An Example Machinelearning Project Endtoend Explore Several Training Models, Including Support Vector Machines, Decision Trees, Random Forests, And Ensemble Methods Use The Tensorflow Library To Build And Train Neural Nets Dive Into Neural Net Architectures, Including Convolutional Nets, Recurrent Nets, And Deep Reinforcement Learning Learn Techniques For Training And Scaling Deep Neural Nets Apply Practical Code Examples Without Acquiring Excessive Machine Learning Theory Or Algorithm Details
⚠️ WARNING (California Proposition 65):
This product may contain chemicals known to the State of California to cause cancer, birth defects, or other reproductive harm.
For more information, please visit www.P65Warnings.ca.gov.
- Q: How many pages does this book have? A: This book has five hundred seventy-two pages. It provides extensive coverage of machine learning concepts and techniques.
- Q: What is the binding type of this book? A: This book is a paperback edition. The paperback binding makes it lightweight and easy to handle.
- Q: What are the dimensions of the book? A: The dimensions of the book are seven point zero one inches in length, one point five inches in width, and nine point one seven inches in height. These dimensions make it a convenient size for reading.
- Q: Is this book suitable for beginners in machine learning? A: Yes, this book is suitable for beginners. It uses concrete examples and minimal theory to help new learners understand machine learning.
- Q: What programming experience do I need to read this book? A: You only need basic programming experience to get started. The book is designed to be accessible for those with little to no knowledge of machine learning.
- Q: How can I apply what I learn from this book? A: You can apply your learning through exercises provided in each chapter. These practical exercises help reinforce the concepts discussed.
- Q: How should I store this book? A: Store this book in a cool, dry place. Avoid exposing it to direct sunlight to preserve the quality of the pages and binding.
- Q: Can I clean the book's cover? A: Yes, you can clean the book's cover with a dry cloth. Avoid using water or cleaning solutions to prevent damage.
- Q: Is this book safe for children? A: Yes, the book is appropriate for older children and teens interested in machine learning. However, adult supervision is recommended due to the complex topics.
- Q: Does this book cover advanced machine learning topics? A: Yes, the book covers advanced topics such as deep neural networks and reinforcement learning. It progresses from basic to more complex concepts.
- Q: What unique techniques are discussed in this book? A: The book discusses techniques like support vector machines, decision trees, and ensemble methods. These techniques are essential for building intelligent systems.
- Q: What frameworks does this book focus on? A: This book focuses on scikit-learn and TensorFlow. These frameworks are widely used for building machine learning models.
- Q: How do I troubleshoot issues while reading this book? A: If you encounter issues, refer to the exercises and examples for guidance. You can also seek online forums for additional help.
- Q: What if the book arrives damaged? A: If the book arrives damaged, you can return it for a replacement or refund. Check the retailer's return policy for specific instructions.
- Q: Is this book updated with the latest machine learning trends? A: Yes, the book includes recent breakthroughs in deep learning. It reflects the current trends and tools in the machine learning landscape.