Why do you do what you do? Why are some people and organizations more innovative, more influential, and moer profitable than others? Why do some command greater loyalty from customers and employees alike? Even among the successful, why are so few able to repeat their success over and over? People like Martin Luther King Jr., Steve Jobs, and the Wright Brothers might have ...(展开全部)

Why do you do what you do? Why are some people and organizations more innovative, more influential, and moer profitable than others? Why do some command greater loyalty from customers and employees alike? Even among the successful, why are so few able to repeat their success over and over? People like Martin Luther King Jr., Steve Jobs, and the Wright Brothers might have ...(展开全部)

PyTorch is extremely powerful and yet easy to learn. It provides advanced features, such as supporting multiprocessor, distributed, and parallel computation. This book is an excellent entry point for those wanting to explore deep learning with PyTorch to harness its power. This book will introduce you to the PyTorch deep learning library and teach you how to train deep learning models without any hassle. We will set up the deep learning environment using PyTorch, and then train and deploy different types of deep learning models, such as CNN, RNN, and autoencoders. You will learn how to optimize models by tuning hyperparameters and how to use PyTorch in multiprocessor and distributed environments. We will discuss long short-term memory network (LSTMs) and build a language model to predict text.By the end of this book, you will be familiar with PyTorch's capabilities and be able to utilize the library to train your neural networks with relative ease.

Machine learning is an essential part of today's data-driven world and is extensively used across industries, including financial forecasting, robotics, and web technology. This book will teach you how to efficiently develop machine learning applications in Go.The book starts with an introduction to machine learning and its development process, explaining the types of problems that it aims to solve and the solutions it offers. It then covers setting up a frictionless Go development environment, including running Go interactively with Jupyter notebooks. Finally, common data processing techniques are introduced.The book then teaches the reader about supervised and unsupervised learning techniques through worked examples that include the implementation of evaluation metrics. These worked examples make use of the prominent open-source libraries GoML and Gonum.The book also teaches readers how to load a pre-trained model and use it to make predictions. It then moves on to the operational side of running machine learning applications: deployment, Continuous Integration, and helpful advice for effective logging and monitoring.At the end of the book, readers will learn how to set up a machine learning project for success, formulating realistic success criteria and accurately translating business requirements into technical ones.

PyTorch is extremely powerful and yet easy to learn. It provides advanced features, such as supporting multiprocessor, distributed, and parallel computation. This book is an excellent entry point for those wanting to explore deep learning with PyTorch to harness its power. This book will introduce you to the PyTorch deep learning library and teach you how to train deep learning models without any hassle. We will set up the deep learning environment using PyTorch, and then train and deploy different types of deep learning models, such as CNN, RNN, and autoencoders. You will learn how to optimize models by tuning hyperparameters and how to use PyTorch in multiprocessor and distributed environments. We will discuss long short-term memory network (LSTMs) and build a language model to predict text.By the end of this book, you will be familiar with PyTorch's capabilities and be able to utilize the library to train your neural networks with relative ease.

Machine learning is an essential part of today's data-driven world and is extensively used across industries, including financial forecasting, robotics, and web technology. This book will teach you how to efficiently develop machine learning applications in Go.The book starts with an introduction to machine learning and its development process, explaining the types of problems that it aims to solve and the solutions it offers. It then covers setting up a frictionless Go development environment, including running Go interactively with Jupyter notebooks. Finally, common data processing techniques are introduced.The book then teaches the reader about supervised and unsupervised learning techniques through worked examples that include the implementation of evaluation metrics. These worked examples make use of the prominent open-source libraries GoML and Gonum.The book also teaches readers how to load a pre-trained model and use it to make predictions. It then moves on to the operational side of running machine learning applications: deployment, Continuous Integration, and helpful advice for effective logging and monitoring.At the end of the book, readers will learn how to set up a machine learning project for success, formulating realistic success criteria and accurately translating business requirements into technical ones.

傾注對電影的熱血夢想，化不可能為可能！ 「既然沒有人要拍，就由我自己來！」 一句話，中山七里自行將《連續殺人鬼青蛙男》拍成紙上電影！ ‧《連續殺人鬼青蛙男》竟然拍成電影！？ 「上顎被鉤子勾住，一具全裸女屍懸掛於大廈十三樓……」 將人命當兒戲的青蛙男，接二連三犯下隨機殺人事件。慘不忍睹的死者遺體附近，總會留有一張宛如幼童字跡般的「犯罪聲明」。 視「人命」為「青蛙」的無差別殺人慘案，將「人性」逼近「瘋狂」邊緣…… 這樣殘虐駭人的兇殺情節，竟然有人要將它搬上大螢幕！？ ‧《連續殺人鬼青蛙男》拍攝困難重重、意外不斷！？ 出資的大股東以資金威脅導演，硬是想要干涉其中。 以人道關懷為宗旨的團體，屢次要求導演撤除某些內容。 此外，還有輕率的男偶像與醜聞纏身的招牌女優， 一堆頭痛的問題之外，竟還發生弔詭的命案！ 負責拍片的知名導演大森，是個性格暴躁卻又堅持完美主義的...(展开全部)

傾注對電影的熱血夢想，化不可能為可能！ 「既然沒有人要拍，就由我自己來！」 一句話，中山七里自行將《連續殺人鬼青蛙男》拍成紙上電影！ ‧《連續殺人鬼青蛙男》竟然拍成電影！？ 「上顎被鉤子勾住，一具全裸女屍懸掛於大廈十三樓……」 將人命當兒戲的青蛙男，接二連三犯下隨機殺人事件。慘不忍睹的死者遺體附近，總會留有一張宛如幼童字跡般的「犯罪聲明」。 視「人命」為「青蛙」的無差別殺人慘案，將「人性」逼近「瘋狂」邊緣…… 這樣殘虐駭人的兇殺情節，竟然有人要將它搬上大螢幕！？ ‧《連續殺人鬼青蛙男》拍攝困難重重、意外不斷！？ 出資的大股東以資金威脅導演，硬是想要干涉其中。 以人道關懷為宗旨的團體，屢次要求導演撤除某些內容。 此外，還有輕率的男偶像與醜聞纏身的招牌女優， 一堆頭痛的問題之外，竟還發生弔詭的命案！ 負責拍片的知名導演大森，是個性格暴躁卻又堅持完美主義的...(展开全部)

Scikit-learn is a robust machine learning library for the Python programming language. It provides a set of supervised and unsupervised learning algorithms. This book is the easiest way to learn how to deploy, optimize, and evaluate all of the important machine learning algorithms that scikit-learn provides.This book teaches you how to use scikit-learn for machine learning. You will start by setting up and configuring your machine learning environment with scikit-learn. To put scikit-learn to use, you will learn how to implement various supervised and unsupervised machine learning models. You will learn classification, regression, and clustering techniques to work with different types of datasets and train your models.Finally, you will learn about an effective pipeline to help you build a machine learning project from scratch. By the end of this book, you will be confident in building your own machine learning models for accurate predictions.

Based on extensive research and real-world examples, this book upends accepted wisdom about how to achieve success when launching a startup or creating a new product. The breakthrough concepts of Pattern Breakers come from the observations of Mike Maples Jr., a seasoned venture capitalist, who noticed something strange. Start-ups like Twitter, Twitch, and Lyft had achieved extr...(展开全部)