by
Romano, Fabrizio, author.
Call Number
005.133 23ENG20230201
Publication Date
2021
Summary
Get up and running with Python through concise tutorials and practical projects in this fully updated edition Key Features Discover how to think like a Python programmer Extensively revised with richer examples, Python 3.9 syntax, and new chapters on APIs and packaging and distributing Python code Learn the fundamentals of Python through real-world projects in API development, GUI programming, and data science Book Description Learn Python Programming, Third Edition is both a theoretical and practical introduction to Python, an extremely flexible and powerful programming language that can be applied to many disciplines. This book will make learning Python easy and give you a thorough understanding of the language. You'll learn how to write programs, build modern APIs, and work with data by using renowned Python data science libraries. This revised edition covers the latest updates on API management, packaging applications, and testing. There is also broader coverage of context managers and an updated data science chapter. The book empowers you to take ownership of writing your software and become independent in fetching the resources you need. You will have a clear idea of where to go and how to build on what you have learned from the book. Through examples, the book explores a wide range of applications and concludes by building real-world Python projects based on the concepts you have learned. What you will learn Get Python up and running on Windows, Mac, and Linux Write elegant, reusable, and efficient code in any situation Avoid common pitfalls like duplication, complicated design, and over-engineering Understand when to use the functional or object-oriented approach to programming Build a simple API with FastAPI and program GUI applications with Tkinter Get an initial overview of more complex topics such as data persistence and cryptography Fetch, clean, and manipulate data, making efficient use of Python's built-in data structures Who this book is for This book is for anyone who has some programming experience, but not necessarily with Python. Some knowledge of basic programming concepts will come in handy, although it is not a requirement.
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Electronic Resources
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4.4726
by
Molina, Alessandro, author.
Call Number
005.133 23
Publication Date
2018
Format:
Electronic Resources
Relevance:
2.8068
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by
Amr, Tarek, author.
Call Number
006.31 23
Publication Date
2020
Summary
This book covers the theory and practice of building data-driven solutions. Includes the end-to-end process, using supervised and unsupervised algorithms. With each algorithm, you will learn the data acquisition and data engineering methods, the apt metrics, and the available hyper-parameters. You will learn how to deploy the models in production.
Format:
Electronic Resources
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2.5971
by
Antic, Zhenya.
Call Number
006.35 23
Publication Date
2021
Summary
Leverage your natural language processing skills to make sense of text. With this book, you'll learn fundamental and advanced NLP techniques in Python that will help you to make your data fit for application in a wide variety of industries. You'll also find recipes for overcoming common challenges in implementing NLP pipelines.
Format:
Electronic Resources
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2.4768
by
Chopra, Rohan, author.
Call Number
006.35 23
Publication Date
2020
Summary
Make NLP easy by building chatbots and models, and executing various NLP tasks to gain data-driven insights from raw text data Key Features Get familiar with key natural language processing (NLP) concepts and terminology Explore the functionalities and features of popular NLP tools Learn how to use Python programming and third-party libraries to perform NLP tasks Book Description Do you want to learn how to communicate with computer systems using Natural Language Processing (NLP) techniques, or make a machine understand human sentiments? Do you want to build applications like Siri, Alexa, or chatbots, even if you've never done it before? With The Natural Language Processing Workshop, you can expect to make consistent progress as a beginner, and get up to speed in an interactive way, with the help of hands-on activities and fun exercises. The book starts with an introduction to NLP. You'll study different approaches to NLP tasks, and perform exercises in Python to understand the process of preparing datasets for NLP models. Next, you'll use advanced NLP algorithms and visualization techniques to collect datasets from open websites, and to summarize and generate random text from a document. In the final chapters, you'll use NLP to create a chatbot that detects positive or negative sentiment in text documents such as movie reviews. By the end of this book, you'll be equipped with the essential NLP tools and techniques you need to solve common business problems that involve processing text. What you will learn Obtain, verify, clean and transform text data into a correct format for use Use methods such as tokenization and stemming for text extraction Develop a classifier to classify comments in Wikipedia articles Collect data from open websites with the help of web scraping Train a model to detect topics in a set of documents using topic modeling Discover techniques to represent text as word and document vectors Who this book is for This book is for beginner to mid-level data scientists, machine learning developers, and NLP enthusiasts. A basic understanding of machine learning and NLP is required to help you grasp the topics in this workshop more quickly.
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Electronic Resources
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2.4153
by
Freeman, Richard Takashi, author.
Call Number
005.133 23
Publication Date
2019
Summary
Here we show how an end-to-end serverless application can be built at scale in a production environment with a few lines of Python configuration. We show you how to set up, configure and create different parts of the stack, including using the AWS Management Console and AWS Serverless Application Model (SAM). We also provide Python code, which ...
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Electronic Resources
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2.2943
by
George, Nathan, author.
Call Number
005.7 23
Publication Date
2021
Summary
The book provides a one-stop solution for getting into data science with Python and teaches how to extract insights from data.
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Electronic Resources
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2.2615
8.
by
Saitoh, Koki, author.
Call Number
006.31 23
Publication Date
2021
Summary
Discover ways to implement various deep learning algorithms by leveraging Python and other technologies. Deep learning is rapidly becoming the most preferred way of solving data problems. This is thanks, in part, to its huge variety of mathematical algorithms and their ability to find patterns that are otherwise invisible to us. Deep Learning from the Basics begins with a fast-paced introduction to deep learning with Python, its definition, characteristics, and applications. You'll learn how to use the Python interpreter and the script files in your applications, and utilize NumPy and Matplotlib in your deep learning models. As you progress through the book, you'll discover backpropagation--an efficient way to calculate the gradients of weight parameters--and study multilayer perceptrons and their limitations, before, finally, implementing a three-layer neural network and calculating multidimensional arrays. By the end of the book, you'll have the knowledge to apply the relevant technologies in deep learning. What you will learn: Use Python with minimum external sources to implement deep learning programs; Study the various deep learning and neural network theories; Learn how to determine learning coefficients and the initial values of weights; Implement trends such as Batch Normalization, Dropout, and Adam; Explore applications like automatic driving, image generation, and reinforcement learning. Who this book is for: Deep Learning from the Basics is designed for data scientists, data analysts, and developers who want to use deep learning techniques to develop efficient solutions. This book is ideal for those who want a deeper understanding as well as an overview of the technologies. Some working knowledge of Python is a must. Knowledge of NumPy and pandas will be beneficial, but not essential.
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Electronic Resources
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2.2467
by
Dey, Sandipan, author.
Call Number
005.133 23
Publication Date
2020
Format:
Electronic Resources
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2.1905
by
Sarkar, Dipayan.
Call Number
006.31 23
Publication Date
2019
Summary
This book uses a recipe-based approach to showcase the power of machine learning algorithms to build ensemble models using Python libraries. Through this book, you will be able to pick up the code, understand in depth how it works, execute and implement it efficiently. This will be a desk reference to implement a wide range of tasks and solve ...
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Electronic Resources
Relevance:
2.0999
by
Venturi, Luca.
Call Number
629.046028637 23
Publication Date
2020
Summary
This book will give you insights into the technologies that drive the autonomous car revolution. To get started, all you need is basic knowledge of computer vision and Python.
Format:
Electronic Resources
Relevance:
2.0186
by
Dop, Thomas, author.
Call Number
006.35 23
Publication Date
2020
Format:
Electronic Resources
Relevance:
1.9848
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