Learn Numpy
DOWNLOAD
Download Learn Numpy PDF/ePub or read online books in Mobi eBooks. Click Download or Read Online button to get Learn Numpy book now. This website allows unlimited access to, at the time of writing, more than 1.5 million titles, including hundreds of thousands of titles in various foreign languages. If the content not found or just blank you must refresh this page
Learn Numpy
DOWNLOAD
Author : Diego Rodrigues
language : en
Publisher: StudioD21
Release Date : 2025-07-13
Learn Numpy written by Diego Rodrigues and has been published by StudioD21 this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-07-13 with Business & Economics categories.
LEARN NumPy Master Data Processing and Advanced Calculations in Python This book is ideal for students and professionals seeking to master NumPy for data analysis, scientific automation, and advanced computing in Python. With a practical approach and applied examples, you will learn to perform vectorized operations, efficiently handle multidimensional arrays, and integrate with leading libraries such as Pandas, SciPy, TensorFlow, PyTorch, and machine learning frameworks. The content includes techniques for processing large data volumes, performance optimization for deep learning, statistical analysis, linear algebra, broadcasting, slicing, aggregation, boolean filters, and integration with Jupyter, Anaconda, AWS, Google Colab, Databricks, and cloud environments. Prepare to build scalable solutions in data science, financial automation, engineering, academic research, and high-performance industrial projects. Includes: • Vectorized operations and advanced array manipulation • Integration with Pandas, SciPy, TensorFlow, and PyTorch • Techniques in linear algebra, statistics, and numerical analysis • Performance optimization for big data and deep learning • Visualization practices with Matplotlib, Seaborn, and Plotly • Workflow automation in cloud, edge, and HPC clusters • Best practices for scientific and industrial projects Master NumPy and boost your career in data science, engineering, artificial intelligence, and automation with robust, fast, and integrated solutions. numpy, python, data analysis, multidimensional arrays, linear algebra, deep learning, pandas, scipy, tensorflow, big data, scientific computing, automation, jupyter, cloud, databricks
Learn Python
DOWNLOAD
Author : Damon Parker
language : en
Publisher: Damon Parker
Release Date : 2021-06-12
Learn Python written by Damon Parker and has been published by Damon Parker this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-06-12 with Computers categories.
Python programming language has rendered itself as the language of choice for coding beginners and advanced software programmers alike. This book is written to help you master the basic concepts of Python coding and how you can utilize your coding skills to analyze a large volume of data and uncover valuable information that can otherwise be easily lost in the volume. It was designed primarily to emphasize the readability of the programming code, and its syntax enables programmers to convey ideas using fewer lines of code. Python programming language increases the speed of operation while allowing for higher efficiency in creating system integrations. Some of the highlights of the book include: - Key features and advantages of learning to code Python as well as the history of how Python programming was created - Step-by-step instructions on how to install Python on your operating systems (Windows, Mac, and Linux) - The concept of Python data types is presented in exquisite detail with various examples of each data type - How to create Python variables - Comprehensive lists of a variety of built-in functions and methods supported by Python - Basic concepts of writing efficient and effective Python codes, focusing on various programming elements - How to write if and else statements to retrieve desired information from your data - For and While loops are explained with explicit details in an easy-to-understand language - Basic concepts of big data analysis and machine learning algorithms - A brief overview of various renowned machine learning libraries All the concepts are explained with standard Python coding syntax supported with relevant examples and followed by exercises to help you test and verify your understanding of those concepts. Finally, as an added bonus you will learn some Python tips and tricks to take your machine learning programming game to the next level. Remember, knowledge is power, and with the great power you will gather from this book, you will be armed to make sound personal and professional technological choices. Your Python programming skillset will improve drastically, and you will be poised to develop your very own machine learning model! Don't you think it can be that easy? If you really want to have proof of all this, don't waste any more time! Grab your copy now!
Learn Tensorflow
DOWNLOAD
Author : Diego Rodrigues
language : en
Publisher: StudioD21
Release Date : 2024-12-12
Learn Tensorflow written by Diego Rodrigues and has been published by StudioD21 this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-12-12 with Business & Economics categories.
LEARN TENSORFLOW Master AI Model Development with Scalability and Precision. From Fundamentals to Practical Applications. This comprehensive guide is aimed at developers and students who want to create robust, high-performance, and scalable solutions with TensorFlow. You will learn to apply deep learning efficiently, master data pipelines, build advanced models, and deploy them professionally into production. Includes: • Tensor manipulation and model structuring with Keras • Building and training CNNs, RNNs, Transformers, and GANs • Regularization techniques, hyperparameter tuning, and performance optimization • Practical implementation with tf.data, TensorBoard, and TensorFlow Lite • Deployment with TensorFlow Serving, IoT integration, and use of GPUs and TPUs • Real-world cases in NLP, computer vision, healthcare, and enterprise systems By the end, you'll be fully equipped to develop TensorFlow applications for critical scenarios and scalable environments with technical excellence. tensorflow, keras, deep learning, cnn, rnn, gpu, deployment, iot, scalable models
Machine Learning For Ios Developers
DOWNLOAD
Author : Abhishek Mishra
language : en
Publisher: John Wiley & Sons
Release Date : 2020-02-12
Machine Learning For Ios Developers written by Abhishek Mishra and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-02-12 with Computers categories.
Harness the power of Apple iOS machine learning (ML) capabilities and learn the concepts and techniques necessary to be a successful Apple iOS machine learning practitioner! Machine earning (ML) is the science of getting computers to act without being explicitly programmed. A branch of Artificial Intelligence (AI), machine learning techniques offer ways to identify trends, forecast behavior, and make recommendations. The Apple iOS Software Development Kit (SDK) allows developers to integrate ML services, such as speech recognition and language translation, into mobile devices, most of which can be used in multi-cloud settings. Focusing on Apple’s ML services, Machine Learning for iOS Developers is an up-to-date introduction to the field, instructing readers to implement machine learning in iOS applications. Assuming no prior experience with machine learning, this reader-friendly guide offers expert instruction and practical examples of ML integration in iOS. Organized into two sections, the book’s clearly-written chapters first cover fundamental ML concepts, the different types of ML systems, their practical uses, and the potential challenges of ML solutions. The second section teaches readers to use models—both pre-trained and user-built—with Apple’s CoreML framework. Source code examples are provided for readers to download and use in their own projects. This book helps readers: Understand the theoretical concepts and practical applications of machine learning used in predictive data analytics Build, deploy, and maintain ML systems for tasks such as model validation, optimization, scalability, and real-time streaming Develop skills in data acquisition and modeling, classification, and regression. Compare traditional vs. ML approaches, and machine learning on handsets vs. machine learning as a service (MLaaS) Implement decision tree based models, an instance-based machine learning system, and integrate Scikit-learn & Keras models with CoreML Machine Learning for iOS Developers is a must-have resource software engineers and mobile solutions architects wishing to learn ML concepts and implement machine learning on iOS Apps.
Python Machine Learning By Example
DOWNLOAD
Author : Yuxi (Hayden) Liu
language : en
Publisher: Packt Publishing Ltd
Release Date : 2017-05-31
Python Machine Learning By Example written by Yuxi (Hayden) Liu and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-05-31 with Computers categories.
Take tiny steps to enter the big world of data science through this interesting guide About This Book Learn the fundamentals of machine learning and build your own intelligent applications Master the art of building your own machine learning systems with this example-based practical guide Work with important classification and regression algorithms and other machine learning techniques Who This Book Is For This book is for anyone interested in entering the data science stream with machine learning. Basic familiarity with Python is assumed. What You Will Learn Exploit the power of Python to handle data extraction, manipulation, and exploration techniques Use Python to visualize data spread across multiple dimensions and extract useful features Dive deep into the world of analytics to predict situations correctly Implement machine learning classification and regression algorithms from scratch in Python Be amazed to see the algorithms in action Evaluate the performance of a machine learning model and optimize it Solve interesting real-world problems using machine learning and Python as the journey unfolds In Detail Data science and machine learning are some of the top buzzwords in the technical world today. A resurging interest in machine learning is due to the same factors that have made data mining and Bayesian analysis more popular than ever. This book is your entry point to machine learning. This book starts with an introduction to machine learning and the Python language and shows you how to complete the setup. Moving ahead, you will learn all the important concepts such as, exploratory data analysis, data preprocessing, feature extraction, data visualization and clustering, classification, regression and model performance evaluation. With the help of various projects included, you will find it intriguing to acquire the mechanics of several important machine learning algorithms – they are no more obscure as they thought. Also, you will be guided step by step to build your own models from scratch. Toward the end, you will gather a broad picture of the machine learning ecosystem and best practices of applying machine learning techniques. Through this book, you will learn to tackle data-driven problems and implement your solutions with the powerful yet simple language, Python. Interesting and easy-to-follow examples, to name some, news topic classification, spam email detection, online ad click-through prediction, stock prices forecast, will keep you glued till you reach your goal. Style and approach This book is an enticing journey that starts from the very basics and gradually picks up pace as the story unfolds. Each concept is first succinctly defined in the larger context of things, followed by a detailed explanation of their application. Every concept is explained with the help of a project that solves a real-world problem, and involves hands-on work—giving you a deep insight into the world of machine learning. With simple yet rich language—Python—you will understand and be able to implement the examples with ease.
Practical Deep Learning 2nd Edition
DOWNLOAD
Author : Ronald T. Kneusel
language : en
Publisher: No Starch Press
Release Date : 2025-07-08
Practical Deep Learning 2nd Edition written by Ronald T. Kneusel and has been published by No Starch Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-07-08 with Computers categories.
Deep learning made simple. Dip into deep learning without drowning in theory with this fully updated edition of Practical Deep Learning from experienced author and AI expert Ronald T. Kneusel. After a brief review of basic math and coding principles, you’ll dive into hands-on experiments and learn to build working models for everything from image analysis to creative writing, and gain a thorough understanding of how each technique works under the hood. Whether you’re a developer looking to add AI to your toolkit or a student seeking practical machine learning skills, this book will teach you: How neural networks work and how they’re trained How to use classical machine learning models How to develop a deep learning model from scratch How to evaluate models with industry-standard metrics How to create your own generative AI models Each chapter emphasizes practical skill development and experimentation, building to a case study that incorporates everything you’ve learned to classify audio recordings. Examples of working code you can easily run and modify are provided, and all code is freely available on GitHub. With Practical Deep Learning, second edition, you’ll gain the skills and confidence you need to build real AI systems that solve real problems. New to this edition: Material on computer vision, fine-tuning and transfer learning, localization, self-supervised learning, generative AI for novel image creation, and large language models for in-context learning, semantic search, and retrieval-augmented generation (RAG).
Python Machine Learning
DOWNLOAD
Author : Brady Ellison
language : en
Publisher:
Release Date :
Python Machine Learning written by Brady Ellison and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on with Computers categories.
Ready to discover the Machine Learning world? Machine learning paves the path into the future and it’s powered by Python. All industries can benefit from machine learning and artificial intelligence whether we’re talking about private businesses, healthcare, infrastructure, banking, or social media. What exactly does it do for us and what does a machine learning specialist do? Machine learning professionals create and implement special algorithms that can learn from existing data to make an accurate prediction on new never before seen data. Python Machine Learning presents you a step-by-step guide on how to create machine learning models that lead to valuable results. The book focuses on machine learning theory as much as practical examples. You will learn how to analyse data, use visualization methods, implement regression and classification models, and how to harness the power of neural networks. By purchasing this book, your machine learning journey becomes a lot easier. While a minimal level of Python programming is recommended, the algorithms and techniques are explained in such a way that you don’t need to be intimidated by mathematics. The Topics Covered Include: Machine learning fundamentals How to set up the development environment How to use Python libraries and modules like Scikit-learn, TensorFlow, Matplotlib, and NumPy How to explore data How to solve regression and classification problems Decision trees k-means clustering Feed-forward and recurrent neural networks Get your copy now
Pattern Recognition Machine Learning Ml Using Python
DOWNLOAD
Author : Dr. G. Prabaharan
language : en
Publisher: RK Publication
Release Date : 2024-05-28
Pattern Recognition Machine Learning Ml Using Python written by Dr. G. Prabaharan and has been published by RK Publication this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-05-28 with Computers categories.
Pattern Recognition & Machine Learning Using Python to understanding the fundamentals of pattern recognition and machine learning, with a hands-on approach using Python. This bridges theoretical concepts with practical applications, covering algorithms, data preprocessing, and model evaluation. It includes topics such as supervised and unsupervised learning, feature selection, and deep learning techniques. Ideal for students, researchers, and professionals, the emphasizes real-world examples and Python implementations to enhance learning and skill development in data-driven problem-solving.
Scikit Learn Cookbook
DOWNLOAD
Author : Julian Avila
language : en
Publisher: Packt Publishing Ltd
Release Date : 2017-11-16
Scikit Learn Cookbook written by Julian Avila and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-11-16 with Computers categories.
Learn to use scikit-learn operations and functions for Machine Learning and deep learning applications. About This Book Handle a variety of machine learning tasks effortlessly by leveraging the power of scikit-learn Perform supervised and unsupervised learning with ease, and evaluate the performance of your model Practical, easy to understand recipes aimed at helping you choose the right machine learning algorithm Who This Book Is For Data Analysts already familiar with Python but not so much with scikit-learn, who want quick solutions to the common machine learning problems will find this book to be very useful. If you are a Python programmer who wants to take a dive into the world of machine learning in a practical manner, this book will help you too. What You Will Learn Build predictive models in minutes by using scikit-learn Understand the differences and relationships between Classification and Regression, two types of Supervised Learning. Use distance metrics to predict in Clustering, a type of Unsupervised Learning Find points with similar characteristics with Nearest Neighbors. Use automation and cross-validation to find a best model and focus on it for a data product Choose among the best algorithm of many or use them together in an ensemble. Create your own estimator with the simple syntax of sklearn Explore the feed-forward neural networks available in scikit-learn In Detail Python is quickly becoming the go-to language for analysts and data scientists due to its simplicity and flexibility, and within the Python data space, scikit-learn is the unequivocal choice for machine learning. This book includes walk throughs and solutions to the common as well as the not-so-common problems in machine learning, and how scikit-learn can be leveraged to perform various machine learning tasks effectively. The second edition begins with taking you through recipes on evaluating the statistical properties of data and generates synthetic data for machine learning modelling. As you progress through the chapters, you will comes across recipes that will teach you to implement techniques like data pre-processing, linear regression, logistic regression, K-NN, Naive Bayes, classification, decision trees, Ensembles and much more. Furthermore, you'll learn to optimize your models with multi-class classification, cross validation, model evaluation and dive deeper in to implementing deep learning with scikit-learn. Along with covering the enhanced features on model section, API and new features like classifiers, regressors and estimators the book also contains recipes on evaluating and fine-tuning the performance of your model. By the end of this book, you will have explored plethora of features offered by scikit-learn for Python to solve any machine learning problem you come across. Style and Approach This book consists of practical recipes on scikit-learn that target novices as well as intermediate users. It goes deep into the technical issues, covers additional protocols, and many more real-live examples so that you are able to implement it in your daily life scenarios.
Learn Amazon Sagemaker
DOWNLOAD
Author : Julien Simon
language : en
Publisher: Packt Publishing Ltd
Release Date : 2021-11-26
Learn Amazon Sagemaker written by Julien Simon and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-11-26 with Computers categories.
Swiftly build and deploy machine learning models without managing infrastructure and boost productivity using the latest Amazon SageMaker capabilities such as Studio, Autopilot, Data Wrangler, Pipelines, and Feature Store Key FeaturesBuild, train, and deploy machine learning models quickly using Amazon SageMakerOptimize the accuracy, cost, and fairness of your modelsCreate and automate end-to-end machine learning workflows on Amazon Web Services (AWS)Book Description Amazon SageMaker enables you to quickly build, train, and deploy machine learning models at scale without managing any infrastructure. It helps you focus on the machine learning problem at hand and deploy high-quality models by eliminating the heavy lifting typically involved in each step of the ML process. This second edition will help data scientists and ML developers to explore new features such as SageMaker Data Wrangler, Pipelines, Clarify, Feature Store, and much more. You'll start by learning how to use various capabilities of SageMaker as a single toolset to solve ML challenges and progress to cover features such as AutoML, built-in algorithms and frameworks, and writing your own code and algorithms to build ML models. The book will then show you how to integrate Amazon SageMaker with popular deep learning libraries, such as TensorFlow and PyTorch, to extend the capabilities of existing models. You'll also see how automating your workflows can help you get to production faster with minimum effort and at a lower cost. Finally, you'll explore SageMaker Debugger and SageMaker Model Monitor to detect quality issues in training and production. By the end of this Amazon book, you'll be able to use Amazon SageMaker on the full spectrum of ML workflows, from experimentation, training, and monitoring to scaling, deployment, and automation. What you will learnBecome well-versed with data annotation and preparation techniquesUse AutoML features to build and train machine learning models with AutoPilotCreate models using built-in algorithms and frameworks and your own codeTrain computer vision and natural language processing (NLP) models using real-world examplesCover training techniques for scaling, model optimization, model debugging, and cost optimizationAutomate deployment tasks in a variety of configurations using SDK and several automation toolsWho this book is for This book is for software engineers, machine learning developers, data scientists, and AWS users who are new to using Amazon SageMaker and want to build high-quality machine learning models without worrying about infrastructure. Knowledge of AWS basics is required to grasp the concepts covered in this book more effectively. A solid understanding of machine learning concepts and the Python programming language will also be beneficial.