Alberto Boschetti's Books

Python Data Science Essentials

By: Alberto Boschetti,Luca Massaron

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Python Data Science Essentials

By: Alberto Boschetti,Luca Massaron

Become an efficient data science practitioner by understanding Python's key concepts About This Book Quickly get familiar with data science using Python 3.5 Save time (and effort) with all the essential tools explained Create effective data science projects and avoid common pitfalls with the help of examples and hints dictated by experience Who This Book Is For If you are an aspiring data scientist and you have at least a working knowledge of data analysis and Python, this book will get you started in data science. Data analysts with experience of R or MATLAB will also find the book to be a comprehensive reference to enhance their data manipulation and machine learning skills. What You Will Learn Set up your data science toolbox using a Python scientific environment on Windows, Mac, and Linux Get data ready for your data science project Manipulate, fix, and explore data in order to solve data science problems Set up an experimental pipeline to test your data science hypotheses Choose the most effective and scalable learning algorithm for your data science tasks Optimize your machine learning models to get the best performance Explore and cluster graphs, taking advantage of interconnections and links in your data In Detail Fully expanded and upgraded, the second edition of Python Data Science Essentials takes you through all you need to know to suceed in data science using Python. Get modern insight into the core of Python data, including the latest versions of Jupyter notebooks, NumPy, pandas and scikit-learn. Look beyond the fundamentals with beautiful data visualizations with Seaborn and ggplot, web development with Bottle, and even the new frontiers of deep learning with Theano and TensorFlow. Dive into building your essential Python 3.5 data science toolbox, using a single-source approach that will allow to to work with Python 2.7 as well. Get to grips fast with data munging and preprocessing, and all the techniques you need to load, analyse, and process your data. Finally, get a complete overview of principal machine learning algorithms, graph analysis techniques, and all the visualization and deployment instruments that make it easier to present your results to an audience of both data science experts and business users. Style and approach The book is structured as a data science project. You will always benefit from clear code and simplified examples to help you understand the underlying mechanics and real-world datasets.

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373

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Packt Publishing Ltd

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ISBN 10

1786462834

ISBN 13

9781786462831

Large Scale Machine Learning with Python

By: Bastiaan Sjardin,Luca Massaron,Alberto Boschetti

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Large Scale Machine Learning with Python

By: Bastiaan Sjardin,Luca Massaron,Alberto Boschetti

Learn to build powerful machine learning models quickly and deploy large-scale predictive applications About This Book Design, engineer and deploy scalable machine learning solutions with the power of Python Take command of Hadoop and Spark with Python for effective machine learning on a map reduce framework Build state-of-the-art models and develop personalized recommendations to perform machine learning at scale Who This Book Is For This book is for anyone who intends to work with large and complex data sets. Familiarity with basic Python and machine learning concepts is recommended. Working knowledge in statistics and computational mathematics would also be helpful. What You Will Learn Apply the most scalable machine learning algorithms Work with modern state-of-the-art large-scale machine learning techniques Increase predictive accuracy with deep learning and scalable data-handling techniques Improve your work by combining the MapReduce framework with Spark Build powerful ensembles at scale Use data streams to train linear and non-linear predictive models from extremely large datasets using a single machine In Detail Large Python machine learning projects involve new problems associated with specialized machine learning architectures and designs that many data scientists have yet to tackle. But finding algorithms and designing and building platforms that deal with large sets of data is a growing need. Data scientists have to manage and maintain increasingly complex data projects, and with the rise of big data comes an increasing demand for computational and algorithmic efficiency. Large Scale Machine Learning with Python uncovers a new wave of machine learning algorithms that meet scalability demands together with a high predictive accuracy. Dive into scalable machine learning and the three forms of scalability. Speed up algorithms that can be used on a desktop computer with tips on parallelization and memory allocation. Get to grips with new algorithms that are specifically designed for large projects and can handle bigger files, and learn about machine learning in big data environments. We will also cover the most effective machine learning techniques on a map reduce framework in Hadoop and Spark in Python. Style and Approach This efficient and practical title is stuffed full of the techniques, tips and tools you need to ensure your large scale Python machine learning runs swiftly and seamlessly. Large-scale machine learning tackles a different issue to what is currently on the market. Those working with Hadoop clusters and in data intensive environments can now learn effective ways of building powerful machine learning models from prototype to production. This book is written in a style that programmers from other languages (R, Julia, Java, Matlab) can follow.

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420

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Packt Publishing Ltd

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ISBN 10

1785888021

ISBN 13

9781785888021

Python Data Science Essentials

By: Alberto Boschetti,Luca Massaron

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Python Data Science Essentials

By: Alberto Boschetti,Luca Massaron

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Packt Publishing Ltd

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1786462834

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9781786462831

TensorFlow Deep Learning Projects

10 real-world projects on computer vision, machine translation, chatbots, and reinforcement learning

By: Alexey Grigorev,Rajalingappaa Shanmugamani,Alberto Boschetti,Luca Massaron,Abhishek Thakur

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TensorFlow Deep Learning Projects

10 real-world projects on computer vision, machine translation, chatbots, and reinforcement learning

By: Alexey Grigorev,Rajalingappaa Shanmugamani,Alberto Boschetti,Luca Massaron,Abhishek Thakur

Leverage the power of Tensorflow to design deep learning systems for a variety of real-world scenarios Key Features Build efficient deep learning pipelines using the popular Tensorflow framework Train neural networks such as ConvNets, generative models, and LSTMs Includes projects related to Computer Vision, stock prediction, chatbots and more Book Description TensorFlow is one of the most popular frameworks used for machine learning and, more recently, deep learning. It provides a fast and efficient framework for training different kinds of deep learning models, with very high accuracy. This book is your guide to master deep learning with TensorFlow with the help of 10 real-world projects. TensorFlow Deep Learning Projects starts with setting up the right TensorFlow environment for deep learning. Learn to train different types of deep learning models using TensorFlow, including Convolutional Neural Networks, Recurrent Neural Networks, LSTMs, and Generative Adversarial Networks. While doing so, you will build end-to-end deep learning solutions to tackle different real-world problems in image processing, recommendation systems, stock prediction, and building chatbots, to name a few. You will also develop systems that perform machine translation, and use reinforcement learning techniques to play games. By the end of this book, you will have mastered all the concepts of deep learning and their implementation with TensorFlow, and will be able to build and train your own deep learning models with TensorFlow confidently. What you will learn Set up the TensorFlow environment for deep learning Construct your own ConvNets for effective image processing Use LSTMs for image caption generation Forecast stock prediction accurately with an LSTM architecture Learn what semantic matching is by detecting duplicate Quora questions Set up an AWS instance with TensorFlow to train GANs Train and set up a chatbot to understand and interpret human input Build an AI capable of playing a video game by itself –and win it! Who this book is for This book is for data scientists, machine learning developers as well as deep learning practitioners, who want to build interesting deep learning projects that leverage the power of Tensorflow. Some understanding of machine learning and deep learning, and familiarity with the TensorFlow framework is all you need to get started with this book.

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310

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Packt Publishing Ltd

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ISBN 10

1788398386

ISBN 13

9781788398381

Large Scale Machine Learning with Python

By: Bastiaan Sjardin,Luca Massaron,Alberto Boschetti

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Large Scale Machine Learning with Python

By: Bastiaan Sjardin,Luca Massaron,Alberto Boschetti

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420

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Packt Publishing Ltd

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1785888021

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9781785888021

Python: Real World Machine Learning

By: Prateek Joshi,John Hearty,Bastiaan Sjardin,Luca Massaron,Alberto Boschetti

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Python: Real World Machine Learning

By: Prateek Joshi,John Hearty,Bastiaan Sjardin,Luca Massaron,Alberto Boschetti

Learn to solve challenging data science problems by building powerful machine learning models using Python About This Book Understand which algorithms to use in a given context with the help of this exciting recipe-based guide This practical tutorial tackles real-world computing problems through a rigorous and effective approach Build state-of-the-art models and develop personalized recommendations to perform machine learning at scale Who This Book Is For This Learning Path is for Python programmers who are looking to use machine learning algorithms to create real-world applications. It is ideal for Python professionals who want to work with large and complex datasets and Python developers and analysts or data scientists who are looking to add to their existing skills by accessing some of the most powerful recent trends in data science. Experience with Python, Jupyter Notebooks, and command-line execution together with a good level of mathematical knowledge to understand the concepts is expected. Machine learning basic knowledge is also expected. What You Will Learn Use predictive modeling and apply it to real-world problems Understand how to perform market segmentation using unsupervised learning Apply your new-found skills to solve real problems, through clearly-explained code for every technique and test Compete with top data scientists by gaining a practical and theoretical understanding of cutting-edge deep learning algorithms Increase predictive accuracy with deep learning and scalable data-handling techniques Work with modern state-of-the-art large-scale machine learning techniques Learn to use Python code to implement a range of machine learning algorithms and techniques In Detail Machine learning is increasingly spreading in the modern data-driven world. It is used extensively across many fields such as search engines, robotics, self-driving cars, and more. Machine learning is transforming the way we understand and interact with the world around us. In the first module, Python Machine Learning Cookbook, you will learn how to perform various machine learning tasks using a wide variety of machine learning algorithms to solve real-world problems and use Python to implement these algorithms. The second module, Advanced Machine Learning with Python, is designed to take you on a guided tour of the most relevant and powerful machine learning techniques and you'll acquire a broad set of powerful skills in the area of feature selection and feature engineering. The third module in this learning path, Large Scale Machine Learning with Python, dives into scalable machine learning and the three forms of scalability. It covers the most effective machine learning techniques on a map reduce framework in Hadoop and Spark in Python. This Learning Path will teach you Python machine learning for the real world. The machine learning techniques covered in this Learning Path are at the forefront of commercial practice. This Learning Path combines some of the best that Packt has to offer in one complete, curated package. It includes content from the following Packt products: Python Machine Learning Cookbook by Prateek Joshi Advanced Machine Learning with Python by John Hearty Large Scale Machine Learning with Python by Bastiaan Sjardin, Alberto Boschetti, Luca Massaron Style and approach This course is a smooth learning path that will teach you how to get started with Python machine learning for the real world, and develop solutions to real-world problems. Through this comprehensive course, you'll learn to create the most effective machine learning techniques from scratch and more!

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941

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Packt Publishing Ltd

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ISBN 10

1787120678

ISBN 13

9781787120679

Burned Alive

Bruno, Galileo and the Inquisition

By: Alberto A. Martinez

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Burned Alive

Bruno, Galileo and the Inquisition

By: Alberto A. Martinez

In 1600, the Catholic Inquisition condemned the philosopher and cosmologist Giordano Bruno for heresy, and he was then burned alive in the Campo de’ Fiori in Rome. Historians, scientists, and philosophical scholars have traditionally held that Bruno’s theological beliefs led to his execution, denying any link between his study of the nature of the universe and his trial. But in Burned Alive, Alberto A. Martínez draws on new evidence to claim that Bruno’s cosmological beliefs—that the stars are suns surrounded by planetary worlds like our own, and that the Earth moves because it has a soul—were indeed the primary factor in his condemnation. Linking Bruno’s trial to later confrontations between the Inquisition and Galileo in 1616 and 1633, Martínez shows how some of the same Inquisitors who judged Bruno challenged Galileo. In particular, one clergyman who authored the most critical reports used by the Inquisition to condemn Galileo in 1633 immediately thereafter wrote an unpublished manuscript in which he denounced Galileo and other followers of Copernicus for their beliefs about the universe: that many worlds exist and that the Earth moves because it has a soul. Challenging the accepted history of astronomy to reveal Bruno as a true innovator whose contributions to the science predate those of Galileo, this book shows that is was cosmology, not theology, that led Bruno to his death.

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466

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Reaktion Books

Published Date

ISBN 10

1780239408

ISBN 13

9781780239408

Python Data Science Essentials

A practitioner’s guide covering essential data science principles, tools, and techniques, 3rd Edition

By: Alberto Boschetti,Luca Massaron

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Python Data Science Essentials

A practitioner’s guide covering essential data science principles, tools, and techniques, 3rd Edition

By: Alberto Boschetti,Luca Massaron

Gain useful insights from your data using popular data science tools Key FeaturesA one-stop guide to Python libraries such as pandas and NumPyComprehensive coverage of data science operations such as data cleaning and data manipulationChoose scalable learning algorithms for your data science tasksBook Description Fully expanded and upgraded, the latest edition of Python Data Science Essentials will help you succeed in data science operations using the most common Python libraries. This book offers up-to-date insight into the core of Python, including the latest versions of the Jupyter Notebook, NumPy, pandas, and scikit-learn. The book covers detailed examples and large hybrid datasets to help you grasp essential statistical techniques for data collection, data munging and analysis, visualization, and reporting activities. You will also gain an understanding of advanced data science topics such as machine learning algorithms, distributed computing, tuning predictive models, and natural language processing. Furthermore, You’ll also be introduced to deep learning and gradient boosting solutions such as XGBoost, LightGBM, and CatBoost. By the end of the book, you will have gained a complete overview of the principal machine learning algorithms, graph analysis techniques, and all the visualization and deployment instruments that make it easier to present your results to an audience of both data science experts and business users What you will learnSet up your data science toolbox on Windows, Mac, and LinuxUse the core machine learning methods offered by the scikit-learn libraryManipulate, fix, and explore data to solve data science problemsLearn advanced explorative and manipulative techniques to solve data operationsOptimize your machine learning models for optimized performanceExplore and cluster graphs, taking advantage of interconnections and links in your dataWho this book is for If you’re a data science entrant, data analyst, or data engineer, this book will help you get ready to tackle real-world data science problems without wasting any time. Basic knowledge of probability/statistics and Python coding experience will assist you in understanding the concepts covered in this book.

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466

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Packt Publishing Ltd

Published Date

ISBN 10

1789531896

ISBN 13

9781789531893

When Flags Collide

By: Alberto Moretti

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When Flags Collide

By: Alberto Moretti

ENGLISH - ITALIAN TEXT Amalia is a heroin, a mother and a wife: she retraces the events of her family through three generations. She welcomes their inheritance in a hard struggle to survive between a Country's rural age at its sunset and a working-class Milan in which the war is perceived by apocalyptic aerial bombardments and alarm sirens. Of the war she talks about the anxiety and the horror: she faces losses and mourning with an aching and courageous heart, with the determination to build a future for her and her little daughter and with the certainty of the return of her never forgotten hero, Commander Guido. He, in the meanwhile, is engaged with his patrol in an epic crossing of the Sahara desert through Libya, Tunisia and Algeria, trying to bring his men to safety.

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288

Publisher

Lulu.com

Published Date

ISBN 10

1387670077

ISBN 13

9781387670079

Palladio in Venice

By: Alberto Weissmüller

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Palladio in Venice

By: Alberto Weissmüller

Andrea Palladio (1508-1580) was one of the most influential architects of the western world. This volume presents his artistic career in relation to the last thirty years of his life, concentrating mainly on his activity in the city of the Lagoon. His ef

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182

Publisher

Grafiche Vianello srl

Published Date

ISBN 10

8872001749

ISBN 13

9788872001745

How to Create a Sustainable Food Industry

A Practical Guide to Perfect Food

By: Melissa Barrett,Massimo Marino,Francesca Brkic,Carlo Alberto Pratesi

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How to Create a Sustainable Food Industry

A Practical Guide to Perfect Food

By: Melissa Barrett,Massimo Marino,Francesca Brkic,Carlo Alberto Pratesi

This book presents a practical guide to help businesses navigate the complex topics of sustainability in the food industry. The book takes you on a journey along the food value chain, from farm to fork, exploring key opportunities to increase positive impacts and circularity at each step of the journey. Written by a team of authors with decades of experience in the food industry and academia, it provides guidance on how to analyse sustainability across the value chain and life cycle of a food product and how to design, implement and communicate strategies to customers. Furthermore, the book shows that there are not always straightforward solutions, but rather choices and trade-offs that require an understanding of what is best suited to the product, customers and business in question. It demystifies a variety of topics, such as local sourcing, regenerative agriculture, plant-based protein and the environmental impact of meat production, and draws on a wide range of case studies from across the globe, to provide concrete, real-world examples. While a perfect food system may not exist, informed decisions can go a long way to reshape and transform the food industry as we know it. This book will be of great interest to professionals working in the food and agriculture industries, as well as students and scholars of sustainable food systems and sustainable business.

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169

Publisher

Taylor & Francis

Published Date

ISBN 10

1000995429

ISBN 13

9781000995428

Python: Real World Machine Learning

By: Prateek Joshi,John Hearty,Bastiaan Sjardin,Luca Massaron,Alberto Boschetti

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Python: Real World Machine Learning

By: Prateek Joshi,John Hearty,Bastiaan Sjardin,Luca Massaron,Alberto Boschetti

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941

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Packt Publishing Ltd

Published Date

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1787120678

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9781787120679

TensorFlow Deep Learning Projects

10 real-world projects on computer vision, machine translation, chatbots, and reinforcement learning

By: Alexey Grigorev,Rajalingappaa Shanmugamani,Alberto Boschetti,Luca Massaron,Abhishek Thakur

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TensorFlow Deep Learning Projects

10 real-world projects on computer vision, machine translation, chatbots, and reinforcement learning

By: Alexey Grigorev,Rajalingappaa Shanmugamani,Alberto Boschetti,Luca Massaron,Abhishek Thakur

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310

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Packt Publishing Ltd

Published Date

ISBN 10

1788398386

ISBN 13

9781788398381

Advances in Mechanical Systems Dynamics

By: Matteo Massaro,Giovanni Boschetti,Alberto Doria

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Advances in Mechanical Systems Dynamics

By: Matteo Massaro,Giovanni Boschetti,Alberto Doria

Modern dynamics was established many centuries ago by Galileo and Newton before the beginning of the industrial era. Presently, we are in the presence of the fourth industrial revolution, and mechanical systems are increasingly being integrated with electronic, electrical, and fluidic systems. This trend is present not only in the industrial environment, which will soon be characterized by the cyber-physical systems of industry 4.0, but also in other environments like mobility, health and bio-engineering, food and natural resources, safety, and sustainable living. In this context, purely mechanical systems with quasi-static behavior will become less common and the state-of-the-art will soon be represented by integrated mechanical systems, which need accurate dynamic models to predict their behavior. Therefore, mechanical system dynamics are going to play an increasingly central role. Significant research efforts are needed to improve the identification of the mechanical properties of systems in order to develop models that take non-linearity into account, and to develop efficient simulation tools. This Special Issue aims at disseminating the latest research achievements, findings, and ideas in mechanical systems dynamics, with particular emphasis on applications that are strongly integrated with other systems and require a multi-physical approach.

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236

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3039281895

ISBN 13

9783039281893

Book's by Alberto Boschetti

Python Data Science Essentials

Python Data Science Essentials

Large Scale Machine Learning with Python

Large Scale Machine Learning with Python

Python Data Science Essentials

Python Data Science Essentials

TensorFlow Deep Learning Projects

TensorFlow Deep Learning Projects

Large Scale Machine Learning with Python

Large Scale Machine Learning with Python

Python: Real World Machine Learning

Python: Real World Machine Learning

Burned Alive

Burned Alive

Python Data Science Essentials

Python Data Science Essentials

When Flags Collide

When Flags Collide

Palladio in Venice

Palladio in Venice

How to Create a Sustainable Food Industry

How to Create a Sustainable Food Industry

Python: Real World Machine Learning

Python: Real World Machine Learning

TensorFlow Deep Learning Projects

TensorFlow Deep Learning Projects

Advances in Mechanical Systems Dynamics

Advances in Mechanical Systems Dynamics

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