A new progression fantasy epic featuring a detailed magic system, kingdom building, and plenty of action.A regression ritual gone wrong. A mage reborn in an ancient era. A world to rebuild. Kai of the Sorcerer's Tower was one of the last Mages left, barely surviving in a world contaminated with dead mana and ruled by creatures of the dark.To save his world and fulfill his master's last wish, he puts himself through a forbidden ritual, risking his soul being ripped into pieces to make his way into the past, to a golden era with abundant mana.But things go wrong. He overshoots and finds himself in an ancient time thousands of years earlier, within the body of a young lord named Arzan.All is not well, though. The young lord was murdered by mysterious forces before Kai took over, beasts are rampant, and even larger threats loom on the horizon.There's a lot to do for Kai to regain his former strength in this time of primitive technology. Then again, he might just be able to change that and makes things even better than they ever were.Don't miss the start of this Progression Fantasy Epic about a desperate mage sent into the past to rescue his apocalyptic world. Features a detailed magic system, kingdom building, three-dimensional characters, plenty of action, and everything you love about progression fantasy! Read more

None

"Flowers for Algernon" is a poignant science fiction novel by Daniel Keyes that explores the complexities of human intelligence, love, and the ethical implications of scientific experimentation. The story revolves around Charlie Gordon, a mentally challenged man in his thirties, who is chosen to undergo an experimental surgery aimed at increasing his intelligence. As the procedure is conducted, readers are introduced to Charlie's journey through progress and regression, told through a series of journal entries that reveal his evolving self-awareness and emotional depth.The title references Algernon, a laboratory mouse who undergoes the same procedure and serves as both a companion and a metaphor for Charlie's own struggles. As Charlie’s intelligence blossoms, he grapples with new relationships, the isolation it brings, and the painful awareness of his past and present. However, the narrative takes a heart-wrenching turn as the effects of the surgery begin to fade, prompting profound questions about the nature of intellect, the value of human connections, and the inevitability of change.Keyes’ profound exploration of human dignity, the importance of memory, and the quest for understanding makes "Flowers for Algernon" an enduring classic that resonates deeply with readers. It's a powerful reflection on what it means to be truly human and the fragility of our existence. Read more

None

None

None

None

None

None

Python Programming

Quick Python tour: Build a simple demo that includes data representation, object-oriented programming, object persistence, GUIs, and website basics
System programming: Explore system interface tools and techniques for command-line scripting, processing files and folders, running programs in parallel, and more
GUI programming: Learn to use Python’s tkinter widget library
Internet programming: Access client-side network protocols and email tools, use CGI scripts, and learn website implementation techniques
More ways to apply Python: Implement data structures, parse text-based information, interface with databases, and extend and embed Python

Machine Learning

How to download free datasets
What tools and machine learning libraries you need
Data scrubbing techniques, including one-hot encoding, binning and dealing with missing data
Preparing data for analysis, including k-fold Validation
Regression analysis to create trend lines
Clustering, including k-means and k-nearest Neighbors
The basics of Neural Networks
Bias/Variance to improve your machine learning model
Decision Trees to decode classification
How to build your first Machine Learning Model to predict house values using Python

Through a recent series of breakthroughs, deep learning has boosted the entire field of machine learning. Now, even programmers who know close to nothing about this technology can use simple, efficient tools to implement programs capable of learning from data. This bestselling book uses concrete examples, minimal theory, and production-ready Python frameworks (Scikit-Learn, Keras, and TensorFlow) to help you gain an intuitive understanding of the concepts and tools for building intelligent systems. With this updated third edition, author Aurélien Géron explores a range of techniques, starting with simple linear regression and progressing to deep neural networks. Numerous code examples and exercises throughout the book help you apply what you've learned. Programming experience is all you need to get started. Use Scikit-learn to track an example ML project end to end Explore several models, including support vector machines, decision trees, random forests, and ensemble methods Exploit unsupervised learning techniques such as dimensionality reduction, clustering, and anomaly detection Dive into neural net architectures, including convolutional nets, recurrent nets, generative adversarial networks, autoencoders, diffusion models, and transformers Use TensorFlow and Keras to build and train neural nets for computer vision, natural language processing, generative models, and deep reinforcement learning Read more

None

A young man wanting to explore past-life regression discovers this is his first lifetime on Earth. All his other existences were on alien worlds and in other dimensions. As this unique case is explored further, he finds out that his association with extraterrestrials did not cease with these other lives. The interaction with UFOs and aliens has continued during all his present life, although the information was protectively hidden by his subconscious. This strange case reveals the motives behind present-day involvement between UFOs and humans and the hopes and expectations of our creators for their garden. We are the garden - the gardeners are extraterrestrials from other worlds. Read more

La historia real de un psiquiatra, su joven paciente y la terapia de regresión que cambió sus vidas para siempre. Un punto de encuentro entre ciencia y metafísica. El doctor Brian Weiss, jefe de psiquiatría del hospital Mount Sinai de Miami, relata en éste, si primer libro, una asombrosa experiencia que cambió por completo su propia vida y su visión de la psicoterapia. Una de sus pacientes, Catherine, recordó bajo hipnosis varias de sus vidas pasadas y pudo encontrar en ellas el origen de muchos de los traumas que sufría. Catherine se curó, pero ocurrió algo todavía más importante: logró ponerse en contacto con los Maestros, espíritus superiores que habitan los estados entre dos vidas. Ellos le comunicaron importantes mensajes de sabiduría y de conocimiento. Este relato, profundamente conmovedor, punto de encuentro entre ciencia y metafísica, constituyó un extraordinario best seller y sigue siendo de obligada lectura en un mundo convulsionado, en especial para los que buscan un sentido espiritual.ENGLISH DESCRIPTIONThe true story about a psychiatrist, his young patient, and a regression therapy that changed their lives forever. It is a meeting point between science and metaphysics.   Dr. Brian Weiss, head of psychiatry at the Mount Sinai hospital in Miami, narrates in this his first book, the astounding events that change his life and his vision of what is psychotherapy. Catherine, one of his patients, while under hypnosis was able to find many of her past lives and there, found the origins of various of the traumas that she was currently going through.   Catherine was cured, but something even more important happened: she managed to get in touch with the Masters, higher spirits that live in between two lives; they passed down important messages of wisdom and knowledge to her. This deeply moving story and a meeting point between science and metaphysics, was an extraordinary best seller and continues to be a must-read in a troubled world, especially for those who seek spiritual guidance. Read more

Essential #1 best-selling reference for more than 20 years sold in almost every college bookstore in the country. The core statistics course is a critical and difficult class for many and develops a foundation that will support use of statistics in higher level courses in so many subject areas and majors. This six-page laminated guide is filled with more need-to-know facts per page than any book or website and will continue to support in future study. The equations, tables, and graphs that matter most accompany concisely written explanations in an outline format for easy access. This reference tool for students and professionals is proven to work selling millions since it's release. With the answers you need at your fingertips this durable tool will last your entire career and is an incredible value. 6 page laminated guide includes: Terminology Frequency Distribution Measures of Central Tendency Measures of Dispersion Graphing Techniques Probability Random Variables Continuous Random Variables Statistical Inference Biased & Unbiased Estimators Standard Error of the Mean Hypothesis Testing Central Limit Theorem Inference for Population Mean Using the Z-Statistic Inference for Population Mean using the t-Statistic Confidence Intervals Comparing Population Means Comparing Variances Analysis of Variance (ANOVA) Proportions Correlation Chi-Square Tests Hypothesis Test for Linear Correlation Regression Simple Linear Regression Significance of a Regression Model Linear Determination Residuals Read more

None

This book of the bestselling and widely acclaimed Python Machine Learning series is a comprehensive guide to machine and deep learning using PyTorch's simple to code framework.Purchase of the print or Kindle book includes a free eBook in PDF format.Key FeaturesLearn applied machine learning with a solid foundation in theoryClear, intuitive explanations take you deep into the theory and practice of Python machine learningFully updated and expanded to cover PyTorch, transformers, XGBoost, graph neural networks, and best practicesBook DescriptionMachine Learning with PyTorch and Scikit-Learn is a comprehensive guide to machine learning and deep learning with PyTorch. It acts as both a step-by-step tutorial and a reference you'll keep coming back to as you build your machine learning systems.Packed with clear explanations, visualizations, and examples, the book covers all the essential machine learning techniques in depth. While some books teach you only to follow instructions, with this machine learning book, we teach the principles allowing you to build models and applications for yourself.Why PyTorch?PyTorch is the Pythonic way to learn machine learning, making it easier to learn and simpler to code with. This book explains the essential parts of PyTorch and how to create models using popular libraries, such as PyTorch Lightning and PyTorch Geometric.You will also learn about generative adversarial networks (GANs) for generating new data and training intelligent agents with reinforcement learning. Finally, this new edition is expanded to cover the latest trends in deep learning, including graph neural networks and large-scale transformers used for natural language processing (NLP).This PyTorch book is your companion to machine learning with Python, whether you're a Python developer new to machine learning or want to deepen your knowledge of the latest developments.What you will learnExplore frameworks, models, and techniques for machines to 'learn' from dataUse scikit-learn for machine learning and PyTorch for deep learningTrain machine learning classifiers on images, text, and moreBuild and train neural networks, transformers, and boosting algorithmsDiscover best practices for evaluating and tuning modelsPredict continuous target outcomes using regression analysisDig deeper into textual and social media data using sentiment analysisWho this book is forIf you have a good grasp of Python basics and want to start learning about machine learning and deep learning, then this is the book for you. This is an essential resource written for developers and data scientists who want to create practical machine learning and deep learning applications using scikit-learn and PyTorch.Before you get started with this book, you’ll need a good understanding of calculus, as well as linear algebra.Table of ContentsGiving Computers the Ability to Learn from DataTraining Simple Machine Learning Algorithms for ClassificationA Tour of Machine Learning Classifiers Using Scikit-LearnBuilding Good Training Datasets – Data PreprocessingCompressing Data via Dimensionality ReductionLearning Best Practices for Model Evaluation and Hyperparameter TuningCombining Different Models for Ensemble LearningApplying Machine Learning to Sentiment AnalysisPredicting Continuous Target Variables with Regression AnalysisWorking with Unlabeled Data – Clustering AnalysisImplementing a Multilayer Artificial Neural Network from Scratch(N.B. Please use the Look Inside option to see further chapters) Read more

None

An Introduction to Statistical Learning provides an accessible overview of the field of statistical learning, an essential toolset for making sense of the vast and complex data sets that have emerged in fields ranging from biology to finance, marketing, and astrophysics in the past twenty years. This book presents some of the most important modeling and prediction techniques, along with relevant applications. Topics include linear regression, classification, resampling methods, shrinkage approaches, tree-based methods, support vector machines, clustering, deep learning, survival analysis, multiple testing, and more. Color graphics and real-world examples are used to illustrate the methods presented. This book is targeted at statisticians and non-statisticians alike, who wish to use cutting-edge statistical learning techniques to analyze their data. Four of the authors co-wrote An Introduction to Statistical Learning, With Applications in R(ISLR), which has become a mainstay of undergraduate and graduate classrooms worldwide, as well as an important reference book for data scientists. One of the keys to its success was that each chapter contains a tutorial on implementing the analyses and methods presented in the R scientific computing environment. However, in recent years Python has become a popular language for data science, and there has been increasing demand for a Python-based alternative to ISLR. Hence, this book (ISLP) covers the same materials as ISLR but with labs implemented in Python. These labs will be useful both for Python novices, as well as experienced users. Read more

None

None

None

None

Statistical methods are a key part of data science, yet few data scientists have formal statistical training. Courses and books on basic statistics rarely cover the topic from a data science perspective. The second edition of this popular guide adds comprehensive examples in Python, provides practical guidance on applying statistical methods to data science, tells you how to avoid their misuse, and gives you advice on what’s important and what’s not.Many data science resources incorporate statistical methods but lack a deeper statistical perspective. If you’re familiar with the R or Python programming languages and have some exposure to statistics, this quick reference bridges the gap in an accessible, readable format.With this book, you’ll learn:Why exploratory data analysis is a key preliminary step in data scienceHow random sampling can reduce bias and yield a higher-quality dataset, even with big dataHow the principles of experimental design yield definitive answers to questionsHow to use regression to estimate outcomes and detect anomaliesKey classification techniques for predicting which categories a record belongs toStatistical machine learning methods that "learn" from dataUnsupervised learning methods for extracting meaning from unlabeled data. Read more

From Joshua Angrist, winner of the Nobel Prize in Economics, and Jörn-Steffen Pischke, an accessible and fun guide to the essential tools of econometric researchApplied econometrics, known to aficionados as 'metrics, is the original data science. 'Metrics encompasses the statistical methods economists use to untangle cause and effect in human affairs. Through accessible discussion and with a dose of kung fu–themed humor, Mastering 'Metrics presents the essential tools of econometric research and demonstrates why econometrics is exciting and useful.The five most valuable econometric methods, or what the authors call the Furious Five--random assignment, regression, instrumental variables, regression discontinuity designs, and differences in differences--are illustrated through well-crafted real-world examples (vetted for awesomeness by Kung Fu Panda's Jade Palace). Does health insurance make you healthier? Randomized experiments provide answers. Are expensive private colleges and selective public high schools better than more pedestrian institutions? Regression analysis and a regression discontinuity design reveal the surprising truth. When private banks teeter, and depositors take their money and run, should central banks step in to save them? Differences-in-differences analysis of a Depression-era banking crisis offers a response. Could arresting O. J. Simpson have saved his ex-wife's life? Instrumental variables methods instruct law enforcement authorities in how best to respond to domestic abuse.Wielding econometric tools with skill and confidence, Mastering 'Metrics uses data and statistics to illuminate the path from cause to effect.Shows why econometrics is importantExplains econometric research through humorous and accessible discussionOutlines empirical methods central to modern econometric practiceWorks through interesting and relevant real-world examples Read more

None

The fundamental mathematical tools needed to understand machine learning include linear algebra, analytic geometry, matrix decompositions, vector calculus, optimization, probability and statistics. These topics are traditionally taught in disparate courses, making it hard for data science or computer science students, or professionals, to efficiently learn the mathematics. This self contained textbook bridges the gap between mathematical and machine learning texts, introducing the mathematical concepts with a minimum of prerequisites. It uses these concepts to derive four central machine learning methods: linear regression, principal component analysis, Gaussian mixture models and support vector machines. For students and others with a mathematical background, these derivations provide a starting point to machine learning texts. For those learning the mathematics for the first time, the methods help build intuition and practical experience with applying mathematical concepts. Every chapter includes worked examples and exercises to test understanding. Programming tutorials are offered on the book's web site. Read more

None

None

Make better decisions with this easy deconstructed guide to data analytics.Want to add data analytics to your skill stack? Having trouble finding where to start?Cell-by-cell, bit-by-bit, this audiobook teaches you the vocabulary, tools, and basic algorithms to think like a data scientist.Like putting together a complex Lego set, each section connects and adds individual blocks of knowledge to build your data literacy. This linear structure to unpacking data analytics takes you from zero to confidently analyzing and discussing data problems.Who is this audiobook for? This audiobook is ideal for anyone interested in making sense of data analytics without the assumption that you understand data science terminology or advanced math. If you've tried to learn data analytics before and failed, this audiobook is for you.Practical approach. This audiobook takes a hands-on approach to learning. This includes practical examples, visual examples, as well as two bonus coding exercises in Python, including free video content to walk you through both exercises. By the end of the audiobook, you will have the practical knowledge to tackle real data problems in your organization or daily life.What you will learn:How to recognize the common data types every data scientist needs to masterWhere to store your data, including big dataNew trends in data analytics, including what is alternative data and why not many people know about itHow to explain the distinction between data mining, machine learning, and analytics to your colleaguesWhen and how to use regression analysis, classification, clustering, association analysis, and natural language processingHow to make better business decisions using data visualization and business intelligencePLEASE NOTE: When you purchase this title, the accompanying PDF will be available in your Audible Library along with the audio. Read more