1.2 3155 dc xml 2D histograms. Two-dimensional histograms White, Gary, active 2020, academic. moving image 2020 eng Closed-captioned in English. eng Gary White explains how to create and set options for 2D histograms in Matplotlib. Academic, Gary White. Description based on XML content. QA76.9.I52 A15 2020 001.4226 Information visualization Python (Computer program language) (CKB)5410000000102896 (OCoLC)1246683161 (CaToSAGE)SAGE000011706 1-5297-7240-0 9781529772401 alma:41SLSP_UZB/bibs/99118299876905508 99118299876905508 11 dc xml 3D contour plots. Three-dimensional countour plots White, Gary, active 2020, academic. moving image 2020 eng Closed-captioned in English. eng Gary White explains how to create a 3D contour plot in Matplotlib. Academic, Gary White. Description based on XML content. QA76.9.I52 A15 2020 001.4226 Information visualization Three-dimensional imaging. Python (Computer program language) (CKB)5410000000102898 (OCoLC)1246683163 (CaToSAGE)SAGE000011707 1-5297-7268-0 9781529772685 alma:41SLSP_UZB/bibs/99118300429905508 99118300429905508 12 dc xml 3D data science with Python : building accurate digital environments with 3D point cloud workflows / Poux, Florent, author. text Sebastopol, CA : O'REILLY MEDIA, 2025. 2025 eng Our physical world is grounded in three dimensions. To create technology that can reason about and interact with it, our data must be 3D too. This practical guide offers data scientists, engineers, and researchers a hands-on approach to working with 3D data using Python. From 3D reconstruction to 3D deep learning techniques, you'll learn how to extract valuable insights from massive datasets, including point clouds, voxels, 3D CAD models, meshes, images, and more. Dr. Florent Poux helps you leverage the potential of cutting-edge algorithms and spatial AI models to develop production-ready systems with a focus on automation. You'll get the 3D data science knowledge and code to: Understand core concepts and representations of 3D data Load, manipulate, analyze, and visualize 3D data using powerful Python libraries Apply advanced AI algorithms for 3D pattern recognition (supervised and unsupervised) Use 3D reconstruction techniques to generate 3D datasets Implement automated 3D modeling and generative AI workflows Explore practical applications in areas like computer vision/graphics, geospatial intelligence, scientific computing, robotics, and autonomous driving Build accurate digital environments that spatial AI solutions can leverage Florent Poux is an esteemed authority in the field of 3D data science who teaches and conducts research for top European universities. He's also head professor at the 3D Geodata Academy and innovation director for French Tech 120 companies. OCLC-licensed vendor bibliographic record. Q335 006.3 Artificial intelligence. Python (Computer program language) Machine learning. Includes index. (CaSebORM)9781098161323 (CKB)38456810700041 (OCoLC)1514648181 (OCoLC-P)1514648181 9781098161323 1098161327 alma:41SLSP_UZB/bibs/99118232715805508 99118232715805508 13 dc xml 3D Deep learning with python : design and develop your computer vision model with 3D data using PyTorch3D and more / Ma, Xudong, author. Hegde, Vishakh, author. Yolyan, Lilit, author. text 2022 eng This practical guide to 3D deep learning will help you learn everything you need to know about 3D computer vision models and how to incorporate them into your day-to-day work. The book covers top methods and frameworks to demonstrate how 3D data can be processed and help you gain the confidence to implement your own 3D deep learning models. Cover -- Title Page -- Copyright and Credits -- Contributors -- Table of Contents -- Preface -- PART 1: 3D Data Processing Basics -- Chapter 1: Introducing 3D Data Processing -- Technical requirements -- Setting up a development environment -- 3D data representation -- Understanding point cloud representation -- Understanding mesh representation -- Understanding voxel representation -- 3D data file format - Ply files -- 3D data file format - OBJ files -- Understanding 3D coordination systems -- Understanding camera models -- Coding for camera models and coordination systems -- Summary -- Chapter 2: Introducing 3D Computer Vision and Geometry -- Technical requirements -- Exploring the basic concepts of rendering, rasterization, and shading -- Understanding barycentric coordinates -- Light source models -- Understanding the Lambertian shading model -- Understanding the Phong lighting model -- Coding exercises for 3D rendering -- Using PyTorch3D heterogeneous batches and PyTorch optimizers -- A coding exercise for a heterogeneous mini-batch -- Understanding transformations and rotations -- A coding exercise for transformation and rotation -- Summary -- PART 2: 3D Deep Learning Using PyTorch3D -- Chapter 3: Fitting Deformable Mesh Models to Raw Point Clouds -- Technical requirements -- Fitting meshes to point clouds - the problem -- Formulating a deformable mesh fitting problem into an optimization problem -- Loss functions for regularization -- Mesh Laplacian smoothing loss -- Mesh normal consistency loss -- Mesh edge loss -- Implementing the mesh fitting with PyTorch3D -- The experiment of not using any regularization loss functions -- The experiment of using only the mesh edge loss -- Summary -- Chapter 4: Learning Object Pose Detection and Tracking by Differentiable Rendering -- Technical requirements. Why we want to have differentiable rendering -- How to make rendering differentiable -- What problems can be solved by using differentiable rendering -- The object pose estimation problem -- How it is coded -- An example of object pose estimation for both silhouette fitting and texture fitting -- Summary -- Chapter 5: Understanding Differentiable Volumetric Rendering -- Technical requirements -- Overview of volumetric rendering -- Understanding ray sampling -- Using volume sampling -- Exploring the ray marcher -- Differentiable volumetric rendering -- Reconstructing 3D models from multi-view images -- Summary -- Chapter 6: Exploring Neural Radiance Fields (NeRF) -- Technical requirements -- Understanding NeRF -- What is a radiance field? -- Representing radiance fields with neural networks -- Training a NeRF model -- Understanding the NeRF model architecture -- Understanding volume rendering with radiance fields -- Projecting rays into the scene -- Accumulating the color of a ray -- Summary -- PART 3: State-of-the-art 3D Deep Learning Using PyTorch3D -- Chapter 7: Exploring Controllable Neural Feature Fields -- Technical requirements -- Understanding GAN-based image synthesis -- Introducing compositional 3D-aware image synthesis -- Generating feature fields -- Mapping feature fields to images -- Exploring controllable scene generation -- Exploring controllable car generation -- Exploring controllable face generation -- Training the GIRAFFE model -- Frechet Inception Distance -- Training the model -- Summary -- Chapter 8: Modeling the Human Body in 3D -- Technical requirements -- Formulating the 3D modeling problem -- Defining a good representation -- Understanding the Linear Blend Skinning technique -- Understanding the SMPL model -- Defining the SMPL model -- Using the SMPL model -- Estimating 3D human pose and shape using SMPLify. Defining the optimization objective function -- Exploring SMPLify -- Running the code -- Exploring the code -- Summary -- Chapter 9: Performing End-to-End View Synthesis with SynSin -- Technical requirements -- Overview of view synthesis -- SynSin network architecture -- Spatial feature and depth networks -- Neural point cloud renderer -- Refinement module and discriminator -- Hands-on model training and testing -- Summary -- Chapter 10: Mesh R-CNN -- Technical requirements -- Overview of meshes and voxels -- Mesh R-CNN architecture -- Graph convolutions -- Mesh predictor -- Demo of Mesh R-CNN with PyTorch -- Demo -- Summary -- Index -- Other Books You May Enjoy. Description based on publisher supplied metadata and other sources. Description based on print version record. TA1634 .M3 2022 006.37 Computer vision. Deep learning (Machine learning) (MiAaPQ)EBC30208344 (Au-PeEL)EBL30208344 (CKB)25264993900041 (OCoLC)1350185787 (OCoLC-P)1350185787 (PPN)266305407 (FR-PaCSA)88937351 (CaSebORM)9781803247823 (FRCYB88937351)88937351 9781803233680 1803233680 alma:41SLSP_UZB/bibs/99118230082005508 99118230082005508 14 dc xml 3D points and lines. Three-dimensional points and lines White, Gary, active 2020, academic. moving image 2020 eng Closed-captioned in English. eng Gary White explains how to work with 3D figures in Matplotlib. Academic, Gary White. Description based on XML content. QA76.9.I52 A15 2020 001.4226 Information visualization Three-dimensional imaging. Python (Computer program language) (CKB)5410000000102899 (OCoLC)1246682917 (CaToSAGE)SAGE000011708 1-5297-7259-1 9781529772593 alma:41SLSP_UZB/bibs/99118298565105508 99118298565105508 15 dc xml 3D surface plots. Three-dimensional surface plots White, Gary, active 2020, academic. moving image 2020 eng Closed-captioned in English. eng Gary White explains how to create a 3D surface plot in Matplotlib. Academic, Gary White. Description based on XML content. QA76.9.I52 A15 2020 001.4226 Information visualization Three-dimensional imaging. Python (Computer program language) (CKB)5410000000102900 (OCoLC)1246683414 (CaToSAGE)SAGE000011709 1-5297-7267-2 9781529772678 alma:41SLSP_UZB/bibs/99118299193205508 99118299193205508 16 dc xml 3D-Datenwissenschaft mit Python Florent Poux text O'Reilly Media, Inc. 2025 2025 ger (CaSebORM)9798341640429 (CKB)38559562300041 979-83-416-4042-9 alma:41SLSP_UZB/bibs/99118233454005508 99118233454005508 17 dc xml 40 algorithms every programmer should know : hone your problem-solving skills by learning different algorithms and their implementation in Python / Forty algorithms every programmer should know Ahmad, Imran, (author) text 2020 eng eng "Learn algorithms for solving classic computer science problems with this concise guide covering everything from fundamental algorithms, such as sorting and searching, to modern algorithms used in machine learning and cryptography Key Features Learn the techniques you need to know to design algorithms for solving complex problems Become familiar with neural networks and deep learning techniques Explore different types of algorithms and choose the right data structures for their optimal implementation Book Description Algorithms have always played an important role in both the science and practice of computing. Beyond traditional computing, the ability to use algorithms to solve real-world problems is an important skill that any developer or programmer must have. This book will help you not only to develop the skills to select and use an algorithm to solve real-world problems but also to understand how it works. You'll start with an introduction to algorithms and discover various algorithm design techniques, before exploring how to implement different types of algorithms, such as searching and sorting, with the help of practical examples. As you advance to a more complex set of algorithms, you'll learn about linear programming, page ranking, and graphs, and even work with machine learning algorithms, understanding the math and logic behind them. Further on, case studies such as weather prediction, tweet clustering, and movie recommendation engines will show you how to apply these algorithms optimally. Finally, you'll become well versed in techniques that enable parallel processing, giving you the ability to use these algorithms for compute-intensive tasks. By the end of this book, you'll have become adept at solving real-world computational problems by using a wide range of algorithms. What you will learn Explore existing data structures and algorithms found in Python libraries Implement graph algorithms for fraud detection using network analysis Work with machine learning algorithms to cluster similar tweets and process Twitter data in real time Predict the weather using supervised learning algorithms Use neural networks for object detection Create a recommendation engine that suggests relevant movies to subscribers Implement foolproof security using symmetric and asymmetric encryption on Google Cloud Platform (GCP) Who this book is for This book is for programmers or developers who want to understand the use of algorithms for problem-solving and writing efficient code. Whether you are a beginner looking to learn the most commonly used algorithms in a clear and concise way or an experienced programmer looking to explore cutting-edge algorithms in data science, machine learning, and cryptography, you'll find this book useful. Although Python programming experience is a must, knowledge of data science will be helpful but not necessary." -- Publisher's description. Includes bibliographical references and index. Section 1: Fundamentals and Core Algorithms. Chapter 1: Overview of Algorithms ; Chapter 2: Data Structures Used in Algorithms ; Chapter 3: Sorting and Searching Algorithms ; Chapter 4: Designing Algorithms ;Chapter 5: Graph Algorithms -- Section 2: Machine Learning Algorithms. Chapter 6: Unsupervised Machine Learning Algorithms ; Chapter 7: Traditional Supervised Learning Algorithms ; Chapter 8: Neural Network Algorithms ; Chapter 9: Algorithms for Natural Language Processing ; Chapter 10: Recommendation Engines -- Section 3: Advanced Topics. Chapter 11: Data Algorithms ; Chapter 12: Cryptography ; Chapter 13: Large-Scale Algorithms ; Chapter 14: Practical Considerations. Mode of access: World Wide Web. Ahmad Imran: Imran Ahmad has been a part of cutting-edge research about algorithms and machine learning for many years. He completed his PhD in 2010, in which he proposed a new linear programming-based algorithm that can be used to optimally assign resources in a large-scale cloud computing environment. In 2017, Imran developed a real-time analytics framework named StreamSensing. He has since authored multiple research papers that use StreamSensing to process multimedia data for various machine learning algorithms. Imran is currently working at Advanced Analytics Solution Center (A2SC) at the Canadian Federal Government as a data scientist. He is using machine learning algorithms for critical use cases. Imran is a visiting professor at Carleton University, Ottawa. He has also been teaching for Google and Learning Tree for the last few years. QA76.9.A43 .A363 2020 005.1 Computer algorithms. Python (Computer program language) (CKB)4100000011297724 (MiAaPQ)EBC6229061 (CaSebORM)9781789801217 (PPN)249686007 (OCoLC)1200095147 (OCoLC)on1200095147 (DE-B1597)695323 (DE-B1597)9781789809862 (iGPub)PACKT0005655 (DE-B1597)BR1423961 (OCoLC)1457220996 9781789809862 178980986X 9781789801217 1789801214 alma:41SLSP_UZB/bibs/99118233444005508 99118233444005508 18 dc xml <<A>> Python data analyst's toolkit learn Python and Python-based libraries with applications in data analysis and statistics Rajagopalan, Gayathri (DE-588)1227622503 aut text 2021 eng PDF 020 004 DATENANALYSE (MATHEMATISCHE STATISTIK) PYTHON (PROGRAMMING LANGUAGES) SOFTWAREHANDBÜCHER (SOFTWAREPRODUKTE) STATISTIKSOFTWARE Python (Computer program language). Statistics . Computer software. Datenanalyse Python https://urn.ub.unibe.ch/urn:ch:slsp:9781484263983:ihv:pdf (DE-627)1755743556 9781484263983 alma:41SLSP_UZB/bibs/99117027169205508 99117027169205508 19 dc xml The Absolute Beginner's Guide to Python Programming : A Step-by-Step Guide with Examples and Lab Exercises / Wilson, Kevin (Kevin Peter), 1978- author. text 2022 eng Written as an illustrated, step-by-step guide, this book will introduce you to Python with examples using the latest version of the language. You'll begin by learning to set up your Python environment. The next few chapters cover the basics of Python such as language classifications, Python language syntax, and how to write a program. Next, you will learn how to work with variables, basic data types, arithmetic, companion, and Boolean operators, followed by lab exercises. Further, the book covers flow control, using functions, and exception handling, as well as the principles of object-oriented programming and building an interface design. The last section explains how to develop a game by installing PyGame and how to use basic animation, and concludes with coverage of Python web development with web servers and Python web frameworks. The Absolute Beginners Guide to Python Programming will give you the tools, confidence, andinspiration to start writing Python programs. If you are a programmer, developer, or a student, or someone who wants to learn on their own, this book is for you. You will: Gain an understanding of computer programming Understand different data and data types Work with Classes and OOP Build interfaces, simple games, and web development with Python. Includes bibliographical references and index. Chapter 1: What is Python -- Chapter 2: The Basics -- Chapter 3: Working with Data -- Chapter 4: Flow Control -- Chapter 5: Handling Files -- Chapter 6: Using Functions -- Chapter 7: Using Modules -- Chapter 8: Exception Handling -- Chapter 9: Object Oriented Programming -- Chapter 10: Building an Interface -- Chapter 11: Developing a Game -- Chapter 12: Python Web Development. Accessibility summary: This PDF does not fully comply with PDF/UA standards, but does feature limited screen reader support, described non-text content (images, graphs), bookmarks for easy navigation and searchable, selectable text. Users of assistive technologies may experience difficulty navigating or interpreting content in this document. We recognize the importance of accessibility, and we welcome queries about accessibility for any of our products. If you have a question or an access need, please get in touch with us at accessibilitysupport@springernature.com. No reading system accessibility options actively disabled Publisher contact for further accessibility information: accessibilitysupport@springernature.com Mode of access: World Wide Web. QA76.73.P98 .W557 2022 005.133 Python (Computer program language) Programming languages (Electronic computers) Python. Programming Language. Includes index. (MiAaPQ)EBC7107722 (Au-PeEL)EBL7107722 (CKB)25048787900041 (NjHacI)9925048787900041 (OCoLC)1347225618 (OCoLC-P)1347225618 (DE-He213)978-1-4842-8716-3 (PPN)265863848 (CaSebORM)9781484287163 (Perlego)4513913 (iGPub)SPNA0093245 9781484287163 1484287169 alma:41SLSP_UZB/bibs/99118230287105508 99118230287105508 20 21 true 2026-08-07T14:01:16+0200