Artificial Intelligence in Earth Science: Best Practices and Fundamental Challenges provides a comprehensive, step-by-step guide to AI workflows for solving problems in Earth Science. The book focuses on the most challenging problems in applying AI in Earth system sciences, such as training data preparation, model selection, hyperparameter tuning, model structure optimization, spatiotemporal generalization, transforming model results into products, and explaining trained models. In addition, it provides full-stack workflow tutorials to help walk readers through the whole process, regardless of previous AI experience. The book tackles the complexity of Earth system problems in AI engineering, fully guiding geoscientists who are planning to implement AI in their daily work.

Education must adapt to the Fourth Industrial Revolution. The most advanced countries in the world are all facing the challenge of retraining several generations of workers who are becoming obsolete. The education system has failed them because it hasn't evolved to respond to the advent of the Digital Revolution. It has prepared workers to meet yesterday's challenges, not tomorrow's demands. In Learning to Unlearn, Founder and CEO of Global Alumni, Pablo Rivas, an industry leader in digital education, shows us how the Digital Revolution is tearing down the traditional educational system and transforming the economy, building a brave new world for the 21st century. We already enjoy the best standards of living in human history, with better health, longer life expectancy, and access to the best tools and digital technologies. With freer societies and more global interaction, now is the time to rethink the way we learn how to solve the pressing problems we still face, re-engage those who have been left behind and build the world of tomorrow, not yesterday.

Implement supervised, unsupervised, and generative deep learning (DL) models using Keras and Dopamine with TensorFlow Key Features Understand the fundamental machine learning concepts useful in deep learning Learn the underlying mathematical concepts as you implement deep learning models from scratch Explore easy-to-understand examples and use cases that will help you build a solid foundation in DL Book DescriptionWith information on the web exponentially increasing, it has become more difficult than ever to navigate through everything to find reliable content that will help you get started with deep learning. This book is designed to help you if you're a beginner looking to work on deep learning and build deep learning models from scratch, and you already have the basic mathematical and programming knowledge required to get started. The book begins with a basic overview of machine learning, guiding you through setting up popular Python frameworks. You will also understand how to prepare data by cleaning and preprocessing it for deep learning, and gradually go on to explore neural networks. A dedicated section will give you insights into the working of neural networks by helping you get hands-on with training single and multiple layers of neurons. Later, you will cover popular neural network architectures such as CNNs, RNNs, AEs, VAEs, and GANs with the help of simple examples, and learn how to build models from scratch. At the end of each chapter, you will find a question and answer section to help you test what you've learned through the course of the book. By the end of this book, you'll be well-versed with deep learning concepts and have the knowledge you need to use specific algorithms with various tools for different tasks. What you will learn Implement recurrent neural networks (RNNs) and long short-term memory (LSTM) for image classification and natural language processing tasks Explore the role of convolutional neural networks (CNNs) in computer vision and signal processing Discover the ethical implications of deep learning modeling Understand the mathematical terminology associated with deep learning Code a generative adversarial network (GAN) and a variational autoencoder (VAE) to generate images from a learned latent space Implement visualization techniques to compare AEs and VAEs Who this book is forThis book is for aspiring data scientists and deep learning engineers who want to get started with the fundamentals of deep learning and neural networks. Although no prior knowledge of deep learning or machine learning is required, familiarity with linear algebra and Python programming is necessary to get started.