Bøger af Mei Wong

"Neural Networks with Python" serves as an introductory guide for those taking their first steps into neural network development with Python. It's tailored to assist beginners in understanding the foundational elements of neural networks and to provide them with the confidence to delve deeper into this intriguing area of machine learning.In this book, readers will embark on a learning journey, starting from the very basics of Python programming, progressing through essential concepts, and gradually building up to more complex neural network architectures. The book simplifies the learning process by using relatable examples and datasets, making the concepts accessible to everyone. You will be introduced to various neural network architectures such as Feedforward, Convolutional, and Recurrent Neural Networks, among others. Each type is explained in a clear and concise manner, with practical examples to illustrate their applications. The book emphasizes the real-world applications and practical aspects of neural network development, rather than just theoretical knowledge.Readers will also find guidance on how to troubleshoot and refine their neural network models. The goal is to equip you with a solid understanding of how to create efficient and effective neural networks, while also being mindful of the common challenges that may arise.By the end of your journey with this book, you will have a foundational understanding of neural networks within the Python ecosystem and be prepared to apply this knowledge to real-world scenarios. "Neural Networks with Python" aims to be your stepping stone into the vast world of machine learning, empowering you to build upon this knowledge and explore more advanced topics in the future.Key LearningsMaster Python for machine learning, from setup to complex models.Gain flexibility with diverse neural network architectures for various problems.Hands-on experience in building, training, and fine-tuning neural networks.Learn strategic approaches for troubleshooting and optimizing neural models.Grasp advanced topics like autoencoders, capsule networks, and attention mechanisms.Acquire skills in crucial data preprocessing and augmentation techniques.Understand and apply optimization techniques and hyperparameter tuning.Implement an end-to-end machine learning project, from data to deployment.Table of ContentPython, TensorFlow, and your First Neural NetworkDeep Dive into Feedforward NetworksConvolutional Networks for Visual TasksRecurrent Networks for Sequence DataData Generation with GANsTransformers for Complex TasksAutoencoders for Data Compression and GenerationCapsule Networks

"Google JAX Essentials" is a comprehensive guide designed for machine learning and deep learning professionals aiming to leverage the power and capabilities of Google's JAX library in their projects. Over the course of eight chapters, this book takes the reader from understanding the challenges of deep learning and numerical computations in the existing frameworks to the essentials of Google JAX, its functionalities, and how to leverage it in real-world machine learning and deep learning projects.The book starts by emphasizing the importance of numerical computing in ML and DL, demonstrating the limitations of standard libraries like NumPy, and introducing the solution offered by JAX. It then guides the reader through the installation of JAX on different computing environments like CPUs, GPUs, and TPUs, and its integration into existing ML and DL projects. The book details the advanced numerical operations and unique features of JAX, including JIT compilation, automatic differentiation, batched operations, and custom gradients. It illustrates how these features can be employed to write code that is both simpler and faster.The book also delves into parallel computation, the effective use of the vmap function, and the use of pmap for distributed computing. Lastly, the reader is walked through the practical application of JAX in training different deep learning models, including RNNs, CNNs, and Bayesian models, with an additional focus on performance-tuning strategies for JAX applications.Key LearningsMastering the installation and configuration of JAX on various computing environments.Understanding the intricacies of JAX's advanced numerical operations.Harnessing the power of JIT compilation in JAX for accelerated computations.Implementing batched operations using the vmap function for efficient processing.Leveraging automatic differentiation and custom gradients in JAX.Proficiency in using the pmap function for distributed computing in JAX.Training different types of deep learning models using JAX.Applying performance tuning strategies to maximize JAX application efficiency.Integrating JAX into existing machine learning and deep learning projects.Complementing the official JAX documentation with practical, real-world applications.Table of ContentNecessity for Google JAXUnravelling JAXSetting up JAX for Machine Learning and Deep LearningJAX for Numerical ComputingDiving Deeper into Auto Differentiation and GradientsEfficient Batch Processing with JAXPower of Parallel Computing with JAXTraining Neural Networks with JAXAudienceThis is must read for machine learning and deep learning professionals to be skilled with the most innovative deep learning library. Knowing Python and experience with machine learning is sufficient is desired to begin with this book