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R 611.00
Robo-Advisor with Python
Build your own robo-advisor in Python to manage your investments and get up and running in no timePurchase of the print or Kindle book includes a free PDF eBookKey FeaturesExplore the use cases, workflow, and features that make up robo-advisorsLearn how to build core robo-advisor capabilities for goals, risk questions, portfolios, and projectionsDiscover how to operate the automated processes of a built and deployed robo-advisorBook DescriptionRobo-advisors are becoming table stakes for the wealth management industry across all segments, from retail to high-net-worth investors. Robo-advisors enable you to manage your own portfolios and financial institutions to create automated platforms for effective digital wealth management. This book is your hands-on guide to understanding how Robo-advisors work, and how to build one efficiently. The chapters are designed in a way to help you get a comprehensive grasp of what Robo-advisors do and how they are structured with an end-to-end workflow. You'll begin by learning about the key decisions that influence the building of a Robo-advisor, along with considerations on building and licensing a platform. As you advance, you'll find out how to build all the core capabilities of a Robo-advisor using Python, including goals, risk questionnaires, portfolios, and projections. The book also shows you how to create orders, as well as open accounts and perform KYC verification for transacting. Finally, you'll be able to implement capabilities such as performance reporting and rebalancing for operating a Robo-advisor with ease. By the end of this book, you'll have gained a solid understanding of how Robo-advisors work and be well on your way to building one for yourself or your business.What you will learnExplore what Robo-advisors do and why they existCreate a workflow to design and build a Robo-advisor from the bottom upBuild and license Robo-advisors using different approachesOpen and fund accounts, complete KYC verification, and manage ordersBuild Robo-advisor features for goals, projections, portfolios, and moreOperate a Robo-advisor with P&L, rebalancing, and fee managementWho this book is forIf you are a finance professional or a data professional working in wealth management and are curious about how robo-advisors work, this book is for you. It will be helpful to have a basic understanding of Python and investing concepts. This is a great handbook for developers interested in building their own robo-advisor to manage personal investments or build a platform for their business to operate, as well as for product managers and business leaders in financial services looking to lease, buy, or build a robo-advisor.
R 1,368.00
Hands-On Data Preprocessing in Python
Get your raw data cleaned up and ready for processing to design better data analytic solutionsKey FeaturesDevelop the skills to perform data cleaning, data integration, data reduction, and data transformationMake the most of your raw data with powerful data transformation and massaging techniquesPerform thorough data cleaning, including dealing with missing values and outliersBook DescriptionHands-On Data Preprocessing is a primer on the best data cleaning and preprocessing techniques, written by an expert who's developed college-level courses on data preprocessing and related subjects. With this book, you'll be equipped with the optimum data preprocessing techniques from multiple perspectives, ensuring that you get the best possible insights from your data. You'll learn about different technical and analytical aspects of data preprocessing – data collection, data cleaning, data integration, data reduction, and data transformation – and get to grips with implementing them using the open source Python programming environment. The hands-on examples and easy-to-follow chapters will help you gain a comprehensive articulation of data preprocessing, its whys and hows, and identify opportunities where data analytics could lead to more effective decision making. As you progress through the chapters, you'll also understand the role of data management systems and technologies for effective analytics and how to use APIs to pull data. By the end of this Python data preprocessing book, you'll be able to use Python to read, manipulate, and analyze data; perform data cleaning, integration, reduction, and transformation techniques, and handle outliers or missing values to effectively prepare data for analytic tools.What you will learnUse Python to perform analytics functions on your dataUnderstand the role of databases and how to effectively pull data from databasesPerform data preprocessing steps defined by your analytics goalsRecognize and resolve data integration challengesIdentify the need for data reduction and execute itDetect opportunities to improve analytics with data transformationWho this book is forThis book is for junior and senior data analysts, business intelligence professionals, engineering undergraduates, and data enthusiasts looking to perform preprocessing and data cleaning on large amounts of data. You don't need any prior experience with data preprocessing to get started with this book. However, basic programming skills, such as working with variables, conditionals, and loops, along with beginner-level knowledge of Python and simple analytics experience, are a prerequisite.
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Hands-On GPU Programming with Python and CUDA
Build real-world applications with Python 2.7, CUDA 9, and CUDA 10. We suggest the use of Python 2.7 over Python 3.x, since Python 2.7 has stable support across all the libraries we use in this book.Key FeaturesExpand your background in GPU programming—PyCUDA, scikit-cuda, and NsightEffectively use CUDA libraries such as cuBLAS, cuFFT, and cuSolverApply GPU programming to modern data science applicationsBook DescriptionHands-On GPU Programming with Python and CUDA hits the ground running: you’ll start by learning how to apply Amdahl’s Law, use a code profiler to identify bottlenecks in your Python code, and set up an appropriate GPU programming environment. You’ll then see how to “query” the GPU’s features and copy arrays of data to and from the GPU’s own memory.As you make your way through the book, you’ll launch code directly onto the GPU and write full blown GPU kernels and device functions in CUDA C. You’ll get to grips with profiling GPU code effectively and fully test and debug your code using Nsight IDE. Next, you’ll explore some of the more well-known NVIDIA libraries, such as cuFFT and cuBLAS.With a solid background in place, you will now apply your new-found knowledge to develop your very own GPU-based deep neural network from scratch. You’ll then explore advanced topics, such as warp shuffling, dynamic parallelism, and PTX assembly. In the final chapter, you’ll see some topics and applications related to GPU programming that you may wish to pursue, including AI, graphics, and blockchain.By the end of this book, you will be able to apply GPU programming to problems related to data science and high-performance computing.What you will learnLaunch GPU code directly from PythonWrite effective and efficient GPU kernels and device functionsUse libraries such as cuFFT, cuBLAS, and cuSolverDebug and profile your code with Nsight and Visual ProfilerApply GPU programming to datascience problemsBuild a GPU-based deep neuralnetwork from scratchExplore advanced GPU hardware features, such as warp shufflingWho this book is forHands-On GPU Programming with Python and CUDA is for developers and data scientists who want to learn the basics of effective GPU programming to improve performance using Python code. You should have an understanding of first-year college or university-level engineering mathematics and physics, and have some experience with Python as well as in any C-based programming language such as C, C++, Go, or Java.
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Python for Tensor Flow
As part of the best-selling Pocket Primer series, this book is designed to prepare programmers for machine learning and deep learning/TensorFlow topics. It begins with a quick introduction to Python, followed by chapters that discuss NumPy, Pandas, Matplotlib, and scikit-learn. The final two chapters contain an assortment of TensorFlow 1.x code samples, including detailed code samples for TensorFlow Dataset (which is used heavily in TensorFlow 2 as well). A TensorFlow Dataset refers to the classes in the tf.data.Dataset namespace that enables programmers to construct a pipeline of data by means of method chaining so-called lazy operators, e.g., map(), filter(), batch(), and so forth, based on data from one or more data sources. Companion files with source code are available for downloading from the publisher. FEATURES, A practical introduction to Python, NumPy, Pandas, Matplotlib, and introductory aspects of TensorFlow 1.x, Contains relevant NumPy/Pandas code samples that are typical in machine learning topics, and also useful TensorFlow 1.x code samples for deep learning/TensorFlow topics, Includes many examples of TensorFlow Dataset APIs with lazy operators, e.g., map(), filter(), batch(), take() and also method chaining such operators, Assumes the reader has very limited experience, Includes companion files with all of the source code examples (download from the publisher).
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Python and Matplotlib Essentials for Scientists and Engineers
This book provides an introduction to the core features of the Python programming language and Matplotlib plotting routings for scientists and engineers (or students of either discipline) who want to use Python™ to analyse data, simulate physical processes, and render publication-quality plots. No previous programming experience is needed before reading the first page.Readers will learn the core features of the Python programming language in under a day. They will be able to immediately use Python to implement codes that solve their own problems and make beautiful plots and animations. Python code is extremely fast to prototype, allowing users to achieve results quickly and accurately. The examples within the book are available for download.Python and Matplotlib Essentials for Scientists and Engineers is accessible for motivated high-school students, but will likely be most useful for undergraduate and graduate students as well as working professionals who have some background with the basic mathematical concepts. This book is intended for technical people who want to get things done.
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