دانلود کتاب یادگیری ماشین با کتاب آشپزی پایتون – راه حل های عملی از پیش پردازش تا یادگیری عمیق

When the first edition of this book was published in 2018, it filled a critical gap in the growing wealth of machine learning (ML) content. By providing well-tested, hands-on Python recipes, it enabled practitioners to copy and paste code before easily adapting it to their use cases. In a short five years, the ML space has continued to explode with advances in deep learning (DL) and the associated DL Python frameworks. Now, in 2023, there is a need for the same sort of hands-on content that serves the needs of both ML and DL practitioners with the latest Python libraries. This book intends to build on the existing (and fantastic) work done by the author of the first edition by: • Updating existing examples to use the latest Python versions and frameworks • Incorporating modern practices in data sources, data analysis, ML, and DL • Expanding the DL content to include tensors, neural networks, and DL for text and vision in PyTorch • Taking our models one step further by serving them in an API Like the first edition, this book takes a task-based approach to machine learning, boasting over 200 self-contained solutions (copy, paste, and run) for the most common tasks a data scientist or machine learning engineer building a model will run into.