James chen (9 Ergebnisse)

Zustand: New. In English.

Zustand: New. In English.

Zustand: New. In English.

Zustand: New. In English.

Zustand: New. In English.

Zustand: New. In English.

Buch. Zustand: Neu. Neuware - This book is a comprehensive guide aiming to demystify the world of transformers -- the architecture that powers Large Language Models (LLMs) like GPT and BERT. From PyTorch basics and mathematical foundations to implementing a Transformer from scratch, you'll gain a deep understanding of the inner workings of these models.That's just the beginning. Get ready to dive into the realm of pre-training your own Transformer from scratch, unlocking the power of transfer learning to fine-tune LLMs for your specific use cases, exploring advanced techniques like PEFT (Prompting for Efficient Fine-Tuning) and LoRA (Low-Rank Adaptation) for fine-tuning, as well as RLHF (Reinforcement Learning with Human Feedback) for detoxifying LLMs to make them aligned with human values and ethical norms.Step into the deployment of LLMs, delivering these state-of-the-art language models into the real-world, whether integrating them into cloud platforms or optimizing them for edge devices, this section ensures you're equipped with the know-how to bring your AI solutions to life.Whether you're a seasoned AI practitioner, a data scientist, or a curious developer eager to advance your knowledge on the powerful LLMs, this book is your ultimate guide to mastering these cutting-edge models. By translating convoluted concepts into understandable explanations and offering a practical hands-on approach, this treasure trove of knowledge is invaluable to both aspiring beginners and seasoned professionals.…

Buch. Zustand: Neu. Neuware - This book is a comprehensive guide to learning the basics of computer vision and machine learning using the powerful OpenCV library and the Python programming language. The book offers a practical, hands-on approach to learn the concepts and techniques of computer vision through practical example. All codes in this book are available at Github.Through a series of examples, the book covers a wide range of topics including image and video processing, feature detection, object detection and recognition, machine learning and deep neural networks. Each chapter includes detailed explanations of the concepts and techniques involved, as well as practical examples and code snippets that demonstrate how to implement them in Python. Throughout the book, readers will work through hands-on examples and projects, learning how to build image processing applications from scratch. Whether you are a beginner or an experienced programmer, this book provides a valuable resource for learning computer vision with OpenCV and Python. The clear and concise writing style makes it easy for readers to follow along, and the numerous examples ensure that readers can practice and apply what they have learned. By the end of the book, readers will have a solid understanding of the fundamentals of computer vision and be able to build their own computer vision applications with confidence. This book is an excellent resource for anyone looking to learn computer vision and machine learning using the OpenCV library and Python programming language.Table of Contents1. Introduction 52. Installation 13 2.1 Install on Windows 14 2.2 Install Python on Ubuntu 16 2.3 Configure PyCharm and Install OpenCV 183. OpenCV Basics 25 3.1 Load and Display Images 26 3.2 Load and Display Videos 30 3.3 Display Webcam 32 3.4 Image Fundamentals 35 3.5 Draw Shapes 42 3.6 Draw Texts 48 3.7 Draw an OpenCV-like Icon 504. User Interaction 52 4.1 Mouse Operations 53 4.2 Draw Circles with Mouse 56 4.3 Draw Polygon with Mouse 60 4.4 Crop an Image with Mouse 62 4.5 Input Values with Trackbars 645. Image Processing 70 5.1 Conversion of Color Spaces 72 5.2 Resize, Crop and Rotate an Image 77 5.3 Adjust Contrast and Brightness of an Image 83 5.4 Adjust Hue, Saturation and Value 87 5.5 Blend Image 91 5.6 Bitwise Operation 94 5.7 Warp Image 101 5.8 Blur Image 107 5.9 Histogram 1146. Object Detection 120 6.1 Canny Edge Detection 122 6.2 Dilation and Erosion 125 6.3 Shape Detection 129 6.4 Color Detection 139 6.5 Text Recognition with Tesseract 150 6.6 Human Detection 161 6.7 Face and Eye Detection 165 6.8 Remove Background 170 6.9 Blur Background 1897. Machine Learning 196 7.1 K-Means Clustering 200 7.2 K-Nearest Neighbors 216 7.3 Support Vector Machine 237 7.4 Artificial Neural Network (ANN) 254 7.5 Convolutional Neural Network (CNN) 276Index 305References 308About the Author 310.…

Zustand: New.