This guide focuses on practical machine learning using Python, providing insights into the latest developments in the field. It aims to enhance your understanding of various machine learning algorithms and techniques, making it suitable for both beginners and those looking to deepen their knowledge. With hands-on examples and clear explanations, readers will gain the skills needed to apply machine learning concepts effectively in real-world scenarios.
Unlock machine learning best practices with real-world use cases
518 Seiten
19 Lesestunden
Focusing on practical applications, this book guides readers through the essentials of machine learning, covering everything from foundational concepts to advanced topics like NLP transformers and multimodal models. With a hands-on approach, it incorporates best practices and real-world examples, utilizing popular frameworks such as PyTorch, TensorFlow, scikit-learn, and pandas, making complex ideas accessible for learners and practitioners alike.
Develop machine learning and deep learning models with Python
774 Seiten
28 Lesestunden
Focusing on machine and deep learning, this guide offers a thorough exploration of PyTorch, known for its user-friendly coding framework. It is part of a bestselling series, providing readers with practical insights and techniques to effectively implement machine learning concepts. Ideal for both beginners and experienced practitioners, the book emphasizes hands-on learning and real-world applications, making complex topics accessible and engaging.
This book offers five real-world projects designed to help you master deep learning concepts. It covers various paradigms and practical applications, including text generation, sentiment analysis, and fraud detection. You'll explore R's extensive range of deep learning libraries and frameworks, such as deepnet, MXNetR, TensorFlow, H2O, Keras, and text2vec. Each project provides insights into implementing different neural networks, complete with tips and best practices.
Targeted at machine learning professionals and data scientists, this resource requires a basic understanding of R programming and deep learning concepts to maximize its benefits. You will learn to instrument deep learning models using various packages, apply neural networks for handwritten digit recognition, and work with CNN models and Keras for traffic sign classification. Additionally, the book covers credit card fraud detection with Autoencoders, sentiment analysis from movie reviews, and the use of bidirectional Long Short-Term Memory networks.
R is a favored programming language for statistical analysis and deep learning, making this book particularly relevant as deep learning continues to gain traction across multiple domains. Through end-to-end implementations, you will train effective neural networks, including convolutional and recurrent types, and leverage GPU capabilities. By the end, you will have a solid grasp of deep learning techniques and thei
