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Aprendizaje Automatico Con Python
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Book Synopsis Aprendizaje automático y profundo en python by : Carlos Pineda
Download or read book Aprendizaje automático y profundo en python written by Carlos Pineda and published by Ediciones de la U. This book was released on 2022-10-04 with total page 256 pages. Available in PDF, EPUB and Kindle. Book excerpt: Es una herramienta de apoyo y de consulta para toda aquella persona interesada en dominar los fundamentos del aprendizaje automático y profundo, a tal punto que le permita aprender lo necesario para desarrollar sus propios modelos de aprendizaje aptos para realizar predicciones con base en los datos, para ello el autor hará uso en la mayoría de los casos de explicaciones teóricas y prácticas, que permitan al lector afianzar sus ideas y fortalecer su aprendizaje. El libro se encuentra dividido en dos partes la primera enfocada en el machine learning y sus diferentes algoritmos de regresión y clasificación, clustering, entre otros. La segunda parte comprende varias técnicas de deep learning donde estudiaremos diferentes arquitecturas de redes neuronales como: redes densamente conectadas, redes convolucionales y redes recurrentes.
Book Synopsis Aprendizaje automático y profundo en python by : Carlos Pineda Pertuz
Download or read book Aprendizaje automático y profundo en python written by Carlos Pineda Pertuz and published by Ediciones de la U. This book was released on 2022-01-28 with total page 342 pages. Available in PDF, EPUB and Kindle. Book excerpt: Es una herramienta de apoyo y de consulta para toda aquella persona interesada en dominar los fundamentos del aprendizaje automático y profundo, a tal punto que le permita aprender lo necesario para desarrollar sus propios modelos de aprendizaje aptos para realizar predicciones con base en los datos, para ello el autor hará uso en la mayoría de los casos de explicaciones teóricas y prácticas, que permitan al lector afianzar sus ideas y fortalecer su aprendizaje. El libro se encuentra dividido en dos partes la primera enfocada en el machine learning y sus diferentes algoritmos de regresión y clasificación, clustering, entre otros. La segunda parte comprende varias técnicas de deep learning donde estudiaremos diferentes arquitecturas de redes neuronales como: redes densamente conectadas, redes convolucionales y redes recurrentes.
Book Synopsis Aprendizaje Automático Con Python by : Paige Jacobs
Download or read book Aprendizaje Automático Con Python written by Paige Jacobs and published by . This book was released on 2019-04-07 with total page 198 pages. Available in PDF, EPUB and Kindle. Book excerpt: Hay diferentes procesos que están incluidos dentro del significado del término "aprendizaje." Si se refiere a un diccionario y busca el significado de aprendizaje, entonces se encontrará con diferentes frases como "para adquirir conocimiento, comprensión o habilidad, a través de estudio, experiencia o instrucción "y" el cambio en las tendencias de comportamiento a través de la experiencia ."Si lo mira de manera superficial, con respecto a las máquinas, es seguro decir que cualquier cambio en la estructura de la máquina, los datos almacenados en la memoria o su composición de datos, con el fin de mejorar la eficiencia y la eficacia de la máquina. El rendimiento es un signo innegable de aprendizaje en una máquina. Cuando comienza a profundizar en este tema, solo un par de estos cambios se pueden clasificar como aprendizaje automático.En este libro, aprenderá sobre el aprendizaje automático utilizando Python. La información que se proporciona en cada uno de los capítulos mejorará su comprensión de la programación de aprendizaje automático mediante Python. Los códigos de muestra junto con los estudios de caso le permitirán probar su conocimiento.¡Comience con sus conocimientos de Machine Learning con Python con este libro!
Book Synopsis Python Machine Learning by : Vahid Mirjalili
Download or read book Python Machine Learning written by Vahid Mirjalili and published by Marcombo. This book was released on 2020-03-27 with total page 548 pages. Available in PDF, EPUB and Kindle. Book excerpt: El aprendizaje automático está invadiendo el mundo del software. Si quieres entender y trabajar la vanguardia del aprendizaje automático, las redes neuronales y el aprendizaje profundo, esta segunda edición del bestseller Python Machine Learning, es tu libro. Modernizado y ampliado para incluir las tecnologías de código abierto más recientes, como scikit-learn, Keras y TensorFlow, este manual proporciona el conocimiento práctico y las técnicas necesarias para crear eficaces aplicaciones de aprendizaje automático y aprendizaje profundo en Python. El conocimiento y la experiencia únicos de Sebastian Raschka y Vahid Mirjalili presentan los algoritmos de aprendizaje automático y aprendizaje profundo, antes de continuar con temas avanzados en análisis de datos. Combinan los principios teóricos del aprendizaje automático con un enfoque práctico de codificación para una comprensión completa de la teoría del aprendizaje automático y la implementación con Python. Aprenderás a: Explorar y entender los frameworks clave para la ciencia de datos, el aprendizaje automático y el aprendizaje profundo Formular nuevas preguntas sobre datos con modelos de aprendizaje automático y redes neuronales Aprovechar el poder de las últimas librerías de código abierto de Python para aprendizaje automático Dominar la implementación de redes neuronales profundas con la librería de TensorFlow Incrustar modelos de aprendizaje automáticos en aplicacions web accesibles Predecir resultados objetivos continuos con análisis de regresión Descubrir patrones ocultos y estructuras en datos con agrupamientos Analizar imágenes mediante técnicas de aprendizaje profundo Profundizar en datos de medios sociales y textuales con el análisis de sentimientos
Book Synopsis Aprendizaje automático y profundo en Python by : Carlos M. Pineda Pertuz
Download or read book Aprendizaje automático y profundo en Python written by Carlos M. Pineda Pertuz and published by Ra-Ma Editorial. This book was released on 2022-04-11 with total page 347 pages. Available in PDF, EPUB and Kindle. Book excerpt: Esta obra pretende ser una herramienta de apoyo y de consulta para estudiantes y profesionales interesados en dominar los fundamentos del aprendizaje automático y profundo, y así poder desarrollar sus propios modelos de aprendizaje aptos para realizar predicciones con base en los datos, para ello el autor combina explicaciones teóricas con ejemplos prácticos. El libro se inicia con explicaciones sobre el lenguaje Python, para luego abarcar los algoritmos más destacados dentro del aprendizaje de máquina. El contenido se encuentra dividido en dos partes: la primera enfocada en el machine learning y sus diferentes algoritmos de regresión y clasificación, clustering, entre otros. La segunda parte comprende varias técnicas de deep learning donde estudiaremos diferentes arquitecturas de redes neuronales como: redes densamente conectadas, redes convolucionales y redes recurrentes. Desde la web del libro podrá descargar los ejemplos y ejercicios que se desarrollan en el libro lo que facilitara al lector a asimilar lo aprendido.
Book Synopsis Aprendizaje Automatico En Python by : Brian Walker
Download or read book Aprendizaje Automatico En Python written by Brian Walker and published by . This book was released on 2019-10-25 with total page 202 pages. Available in PDF, EPUB and Kindle. Book excerpt: Antes de entrar en el mundo del aprendizaje automático, tienes que empezar por lo básico si acabas de empezar con la programación. Python es una de las mejores plataformas para empezar, ya que sirve como un núcleo de técnicas informáticas modernas como el aprendizaje profundo, el aprendizaje automático y las redes neuronales. En este libro, aprenderá exactamente qué ventajas tiene Python sobre otros idiomas. También aprenderá cómo configurar Python en su sistema y código y ejecutar programas básicos con la ayuda de códigos de muestra proporcionados a lo largo del libro.Desde la sintaxis hasta las funciones, los tipos de datos y las instrucciones condicionales, aprendizaje automático en python está bien redondeado para ayudarle en el recorrido de la programación . ¡Compra este libro ahora para aprender más!
Book Synopsis Aprendizaje automático con Python by : Ethan Williams
Download or read book Aprendizaje automático con Python written by Ethan Williams and published by Independently Published. This book was released on 2019-11-10 with total page 230 pages. Available in PDF, EPUB and Kindle. Book excerpt: Vivimos en un mundo de diluvio de datos donde se generan gigabytes de datos a diario. Es posible que estos datos no sean muy útiles para nuestras aplicaciones diarias. Los principales contratiempos en el uso de dichos datos pueden deberse a la presencia de lagunas en los enlaces de datos generados previamente o los datos pueden ser demasiado vastos para la mente humana limitada. El aprendizaje automático en este libro presenta algunas de las soluciones a los problemas anteriores. Al ser una guía introductoria, espere aprender los diversos conceptos básicos involucrados en el aprendizaje automatico y Python.Este libro proporciona una idea del nuevo mundo de big data, luego le corresponde aprender más sobre aprendizaje automatizado. Podrá obtener respuestas a las siguientes preguntas: - ¿Qué es el aprendizaje automático y qué implica?- ¿Cómo puedo aplicar el aprendizaje automático para echar un vistazo al nuevo mundo, potenciar mi empresa o descubrir cómo piensa Internet sobre mi trabajo de investigación académica?Este es uno de los mejores idiomas que puedes elegir para comenzar a aprender y al final tener una carrera exitosa. Sé que vas a tener una experiencia muy agradable en este lenguaje de programación. En resumen, dado que el lenguaje de programación fue de código abierto, esperamos muchos avances y desarrollos en el lenguaje que lo harán más simple y fácil de usar en los próximos años.Prepárese para aprender todo lo necesario para ser un experto en el campo del aprendizaje automático
Book Synopsis Aprendizaje automático y profundo en Python by : Carlos M. Pineda Pertuz
Download or read book Aprendizaje automático y profundo en Python written by Carlos M. Pineda Pertuz and published by . This book was released on 2022 with total page 341 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Aprendizaje Automatizado Con Python by : Alexander Cane
Download or read book Aprendizaje Automatizado Con Python written by Alexander Cane and published by . This book was released on 2019-10-11 with total page 202 pages. Available in PDF, EPUB and Kindle. Book excerpt: Antes de entrar en el mundo del aprendizaje autom�tico, tienes que empezar por lo b�sico si acabas de empezar con la programaci�n. Python es una de las mejores plataformas para empezar, ya que sirve como un n�cleo de t�cnicas inform�ticas modernas como el aprendizaje profundo, el aprendizaje autom�tico Y las redes neuronales. En este libro, aprender� exactamente qu� ventajas tiene Python sobre otros lenguajes. Tambi�n aprender� c�mo configurar Python en su sistema y c�digo y ejecutar programas b�sicos con la ayuda de c�digos de muestra proporcionados a lo largo del libro.Desde la sintaxis hasta las funciones, los tipos de datos y las instrucciones condicionales, Aprendizaje automatizado con phyton est� bien hecho para ayudarle en el recorrido de codificaci�n. �Compra este libro ahora para aprender m�s!
Book Synopsis Big data, machine learning y data science en python by : José Manuel Ortega Candel
Download or read book Big data, machine learning y data science en python written by José Manuel Ortega Candel and published by Ra-Ma Editorial. This book was released on 2022-11-25 with total page 343 pages. Available in PDF, EPUB and Kindle. Book excerpt: El libro está dirigido aquellos lectores que estén trabajando en proyecto relacionados con big data y busquen identificar las características de una solución de Big Data, los datos asociados a estas soluciones, la infraestructura requerida, y las técnicas de procesamiento de esos datos. Entre los principales objetivos podemos destacar: Introducir los conceptos de ciencias de datos y machine learning. Introducir las principales librerías que podemos encontrar en Python para aplicar técnicas de machine learning a los datos. Dar a conocer los pasos para construir un modelo de machine learning, desde la adquisición de datos, pasando por la generación de funciones, hasta la selección de modelos. Dar a conocer los principales algoritmos para resolver problemas de machine learning. Introducir scikit-learn como herramienta para resolver problemas de machine learning. Introducir pyspark como herramienta para aplicar técnicas de big data y map-reduce. Introducir los sistemas de recomendación basados en contenidos. El libro trata de seguir un enfoque teórico-práctico con el objetivo de afianzar los conocimientos mediante la creación y ejecución de scripts desde la consola de Python. Además, complementa los contenidos con un repositorio alojado en el Material Adicional donde se pueden encontrar los ejemplos que se analizan a lo largo del libro para facilitar al lector las pruebas y asimilación de los contenidos teóricos. Desde la web del libro podrá descargar los ejemplos y ejercicios que se desarrollan en el libro lo que facilitara al lector a asimilar lo aprendido.
Book Synopsis Learn TensorFlow 2.0 by : Pramod Singh
Download or read book Learn TensorFlow 2.0 written by Pramod Singh and published by Apress. This book was released on 2019-12-17 with total page 177 pages. Available in PDF, EPUB and Kindle. Book excerpt: Learn how to use TensorFlow 2.0 to build machine learning and deep learning models with complete examples. The book begins with introducing TensorFlow 2.0 framework and the major changes from its last release. Next, it focuses on building Supervised Machine Learning models using TensorFlow 2.0. It also demonstrates how to build models using customer estimators. Further, it explains how to use TensorFlow 2.0 API to build machine learning and deep learning models for image classification using the standard as well as custom parameters. You'll review sequence predictions, saving, serving, deploying, and standardized datasets, and then deploy these models to production. All the code presented in the book will be available in the form of executable scripts at Github which allows you to try out the examples and extend them in interesting ways. What You'll LearnReview the new features of TensorFlow 2.0Use TensorFlow 2.0 to build machine learning and deep learning models Perform sequence predictions using TensorFlow 2.0Deploy TensorFlow 2.0 models with practical examples Who This Book Is For Data scientists, machine and deep learning engineers.
Author :John Anderson Publisher :Createspace Independent Publishing Platform ISBN 13 :9781724731968 Total Pages :224 pages Book Rating :4.7/5 (319 download)
Book Synopsis Hands on Machine Learning with Python by : John Anderson
Download or read book Hands on Machine Learning with Python written by John Anderson and published by Createspace Independent Publishing Platform. This book was released on 2018-08-06 with total page 224 pages. Available in PDF, EPUB and Kindle. Book excerpt: ***** BUY NOW (will soon return to 24.77 $***** MONEY BACK GUARANTEE BY AMAZON (See Below FAQ) *****Are you thinking of learning more about Machine Learning using Python? (For Beginners)This book is for you. It would seek to explain you all need to know about machine learning and its application using Python in an intuitive way. From AI Sciences Publisher Our books may be the best one for beginners; it's a step-by-step guide for any person who wants to start learning Artificial Intelligence and Data Science from scratch. It will help you in preparing a solid foundation and learn any other high-level courses.To get the most out of the concepts that would be covered, readers are advised to adopt a hands on approach which would lead to better mental representations. Target UsersThe book designed for a variety of target audiences. The most suitable users would include: Anyone who is intrigued by how algorithms arrive at predictions but has no previous knowledge of the field. Software developers and engineers with a strong programming background but seeking to break into the field of machine learning. Seasoned professionals in the field of artificial intelligence and machine learning who desire a bird's eye view of current techniques and approaches. What's Inside This Book? Overview of Python Programming Language Statistics Probability The Data Science Process Machine Learning Supervised Learning Algorithms Unsupervised Learning Algorithms Semi-supervised Learning Algorithms Reinforcement Learning Algorithms Overfitting and Underfitting Python Data Science Tools Jupyter Notebook Numerical Python (Numpy) Pandas Scientific Python (Scipy) Matplotlib Scikit-Learn K-Nearest Neighbors Naive Bayes Simple and Multiple Linear Regression Logistic Regression Generalized Linear Models Decision Trees and Random Forest Neural Networks Perceptrons Backpropagation Clustering K-means with Scikit-Learn Bottom-up Hierarchical Clustering K-means Clustering Network Analysis Betweenness centrality Eigenvector Centrality Recommender Systems Multi-Class Classification Popular Classification Algorithms Support Vector Machine Deep Learning using TensorFlow Deep Learning Case Studies Frequently Asked Questions Q: Is this book for me and do I need programming experience?A: If you want to smash Machine Learning from scratch, this book is for you. If you already wrote a few lines of code and recognize basic programming statements, you'll be OK. Q: Does this book include everything I need to become a Machine Learning expert?A: Unfortunately, no. This book is designed for readers taking their first steps in Machine Learning and further learning will be required beyond this book to master all aspects of Machine Learning. Q: Can I have a refund if this book doesn't fit for me?A: Yes, Amazon refund you if you aren't satisfied, for more information about the amazon refund service please go to the amazon help platform. We will also be happy to help you if you send us an email (email address inside the book).***** MONEY BACK GUARANTEE BY AMAZON ***** Editorial Reviews"This book succeeds in covering most important techniques in a clear, intuitive way that is perfect for newbies and those seeking to improve their practice in the Machine LearningFields VERY QUICKLY ." --Adrian B. Machine Learning Researcher Consulting AI company
Book Synopsis Explainable AI with Python by : Leonida Gianfagna
Download or read book Explainable AI with Python written by Leonida Gianfagna and published by Springer Nature. This book was released on 2021-04-28 with total page 202 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides a full presentation of the current concepts and available techniques to make “machine learning” systems more explainable. The approaches presented can be applied to almost all the current “machine learning” models: linear and logistic regression, deep learning neural networks, natural language processing and image recognition, among the others. Progress in Machine Learning is increasing the use of artificial agents to perform critical tasks previously handled by humans (healthcare, legal and finance, among others). While the principles that guide the design of these agents are understood, most of the current deep-learning models are "opaque" to human understanding. Explainable AI with Python fills the current gap in literature on this emerging topic by taking both a theoretical and a practical perspective, making the reader quickly capable of working with tools and code for Explainable AI. Beginning with examples of what Explainable AI (XAI) is and why it is needed in the field, the book details different approaches to XAI depending on specific context and need. Hands-on work on interpretable models with specific examples leveraging Python are then presented, showing how intrinsic interpretable models can be interpreted and how to produce “human understandable” explanations. Model-agnostic methods for XAI are shown to produce explanations without relying on ML models internals that are “opaque.” Using examples from Computer Vision, the authors then look at explainable models for Deep Learning and prospective methods for the future. Taking a practical perspective, the authors demonstrate how to effectively use ML and XAI in science. The final chapter explains Adversarial Machine Learning and how to do XAI with adversarial examples.
Book Synopsis Applied Machine Learning with Python by : Andrea Giussani
Download or read book Applied Machine Learning with Python written by Andrea Giussani and published by . This book was released on 2021 with total page 182 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Practical Machine Learning with Python by : Dipanjan Sarkar
Download or read book Practical Machine Learning with Python written by Dipanjan Sarkar and published by Apress. This book was released on 2017-12-22 with total page 530 pages. Available in PDF, EPUB and Kindle. Book excerpt: Master the essential skills needed to recognize and solve complex problems with machine learning and deep learning. Using real-world examples that leverage the popular Python machine learning ecosystem, this book is your perfect companion for learning the art and science of machine learning to become a successful practitioner. The concepts, techniques, tools, frameworks, and methodologies used in this book will teach you how to think, design, build, and execute machine learning systems and projects successfully. Practical Machine Learning with Python follows a structured and comprehensive three-tiered approach packed with hands-on examples and code. Part 1 focuses on understanding machine learning concepts and tools. This includes machine learning basics with a broad overview of algorithms, techniques, concepts and applications, followed by a tour of the entire Python machine learning ecosystem. Brief guides for useful machine learning tools, libraries and frameworks are also covered. Part 2 details standard machine learning pipelines, with an emphasis on data processing analysis, feature engineering, and modeling. You will learn how to process, wrangle, summarize and visualize data in its various forms. Feature engineering and selection methodologies will be covered in detail with real-world datasets followed by model building, tuning, interpretation and deployment. Part 3 explores multiple real-world case studies spanning diverse domains and industries like retail, transportation, movies, music, marketing, computer vision and finance. For each case study, you will learn the application of various machine learning techniques and methods. The hands-on examples will help you become familiar with state-of-the-art machine learning tools and techniques and understand what algorithms are best suited for any problem. Practical Machine Learning with Python will empower you to start solving your own problems with machine learning today! What You'll Learn Execute end-to-end machine learning projects and systems Implement hands-on examples with industry standard, open source, robust machine learning tools and frameworks Review case studies depicting applications of machine learning and deep learning on diverse domains and industries Apply a wide range of machine learning models including regression, classification, and clustering. Understand and apply the latest models and methodologies from deep learning including CNNs, RNNs, LSTMs and transfer learning. Who This Book Is For IT professionals, analysts, developers, data scientists, engineers, graduate students
Book Synopsis Building Machine Learning Systems with Python by : Luis Pedro Coelho
Download or read book Building Machine Learning Systems with Python written by Luis Pedro Coelho and published by Packt Publishing Ltd. This book was released on 2018-07-31 with total page 394 pages. Available in PDF, EPUB and Kindle. Book excerpt: Get more from your data by creating practical machine learning systems with Python Key Features Develop your own Python-based machine learning system Discover how Python offers multiple algorithms for modern machine learning systems Explore key Python machine learning libraries to implement in your projects Book Description Machine learning allows systems to learn things without being explicitly programmed to do so. Python is one of the most popular languages used to develop machine learning applications, which take advantage of its extensive library support. This third edition of Building Machine Learning Systems with Python addresses recent developments in the field by covering the most-used datasets and libraries to help you build practical machine learning systems. Using machine learning to gain deeper insights from data is a key skill required by modern application developers and analysts alike. Python, being a dynamic language, allows for fast exploration and experimentation. This book shows you exactly how to find patterns in your raw data. You will start by brushing up on your Python machine learning knowledge and being introduced to libraries. You'll quickly get to grips with serious, real-world projects on datasets, using modeling and creating recommendation systems. With Building Machine Learning Systems with Python, you’ll gain the tools and understanding required to build your own systems, all tailored to solve real-world data analysis problems. By the end of this book, you will be able to build machine learning systems using techniques and methodologies such as classification, sentiment analysis, computer vision, reinforcement learning, and neural networks. What you will learn Build a classification system that can be applied to text, images, and sound Employ Amazon Web Services (AWS) to run analysis on the cloud Solve problems related to regression using scikit-learn and TensorFlow Recommend products to users based on their past purchases Understand different ways to apply deep neural networks on structured data Address recent developments in the field of computer vision and reinforcement learning Who this book is for Building Machine Learning Systems with Python is for data scientists, machine learning developers, and Python developers who want to learn how to build increasingly complex machine learning systems. You will use Python's machine learning capabilities to develop effective solutions. Prior knowledge of Python programming is expected.
Author :Jonathan Adam Publisher :Createspace Independent Publishing Platform ISBN 13 :9781725929982 Total Pages :130 pages Book Rating :4.9/5 (299 download)
Book Synopsis Python Machine Learning from Scratch by : Jonathan Adam
Download or read book Python Machine Learning from Scratch written by Jonathan Adam and published by Createspace Independent Publishing Platform. This book was released on 2016-08-24 with total page 130 pages. Available in PDF, EPUB and Kindle. Book excerpt: ***** BUY NOW (will soon return to 25.89 $)******Free eBook for customers who purchase the print book from Amazon****** Are you thinking of learning more about Machine Learning using Python? (For Beginners) This book would seek to explain common terms and algorithms in an intuitive way. The author used a progressive approach whereby we start out slowly and improve on the complexity of our solutions. From AI Sciences Publisher Our books may be the best one for beginners; it's a step-by-step guide for any person who wants to start learning Artificial Intelligence and Data Science from scratch. It will help you in preparing a solid foundation and learn any other high-level courses.To get the most out of the concepts that would be covered, readers are advised to adopt a hands on approach which would lead to better mental representations. Step By Step Guide and Visual Illustrations and Examples This book and the accompanying examples, you would be well suited to tackle problems which pique your interests using machine learning.Instead of tough math formulas, this book contains several graphs and images which detail all important Machine Learning concepts and their applications. Target Users The book designed for a variety of target audiences. The most suitable users would include: Anyone who is intrigued by how algorithms arrive at predictions but has no previous knowledge of the field. Software developers and engineers with a strong programming background but seeking to break into the field of machine learning. Seasoned professionals in the field of artificial intelligence and machine learning who desire a bird's eye view of current techniques and approaches. What's Inside This Book? Supervised Learning Algorithms Unsupervised Learning Algorithms Semi-supervised Learning Algorithms Reinforcement Learning Algorithms Overfitting and underfitting correctness The Bias-Variance Trade-off Feature Extraction and Selection A Regression Example: Predicting Boston Housing Prices Import Libraries: How to forecast and Predict Popular Classification Algorithms Introduction to K Nearest Neighbors Introduction to Support Vector Machine Example of Clustering Running K-means with Scikit-Learn Introduction to Deep Learning using TensorFlow Deep Learning Compared to Other Machine Learning Approaches Applications of Deep Learning How to run the Neural Network using TensorFlow Cases of Study with Real Data Sources & References Frequently Asked Questions Q: Is this book for me and do I need programming experience?A: If you want to smash Machine Learning from scratch, this book is for you. If you already wrote a few lines of code and recognize basic programming statements, you'll be OK.Q: Does this book include everything I need to become a Machine Learning expert?A: Unfortunately, no. This book is designed for readers taking their first steps in Machine Learning and further learning will be required beyond this book to master all aspects of Machine Learning.Q: Can I have a refund if this book is not fitted for me?A: Yes, Amazon refund you if you aren't satisfied, for more information about the amazon refund service please go to the amazon help platform. We will also be happy to help you if you send us an email at [email protected] Sciences Company offers you a free eBooks at http://aisciences.net/free/
