Accelerate Model Training with PyTorch 2.X. Build more accurate models by boosting the model training process

Maicon Melo Alves, Lúcia Maria de Assumpçao Drummond

Penned by an expert in High-Performance Computing (HPC) with over 25 years of experience, this book is your guide to enhancing the performance of model training using PyTorch, one of the most widely adopted machine learning frameworks.You’ll start by understanding how model complexity impacts training time before discovering distinct levels of performance tuning to expedite the training process. You’ll also learn how to use a new PyTorch feature to compile the model and train it faster, alongside learning how to benefit from specialized libraries to optimize the training process on the CPU. As you progress, you’ll gain insights into building an efficient data pipeline to keep accelerators occupied during the entire training execution and explore strategies for reducing model complexity and adopting mixed precision to minimize computing time and memory consumption. The book will get you acquainted with distributed training and show you how to use PyTorch to harness the computing power of multicore systems and multi-GPU environments available on single or multiple machines. By the end of this book, you’ll be equipped with a suite of techniques, approaches, and strategies to speed up training , so you can focus on what really matters—building stunning models!

Active Machine Learning with Python. Refine and elevate data quality over quantity with active learning

Margaux Masson-Forsythe

Building accurate machine learning models requires quality data—lots of it. However, for most teams, assembling massive datasets is time-consuming, expensive, or downright impossible. Led by Margaux Masson-Forsythe, a seasoned ML engineer and advocate for surgical data science and climate AI advancements, this hands-on guide to active machine learning demonstrates how to train robust models with just a fraction of the data using Python's powerful active learning tools.You’ll master the fundamental techniques of active learning, such as membership query synthesis, stream-based sampling, and pool-based sampling and gain insights for designing and implementing active learning algorithms with query strategy and Human-in-the-Loop frameworks. Exploring various active machine learning techniques, you’ll learn how to enhance the performance of computer vision models like image classification, object detection, and semantic segmentation and delve into a machine AL method for selecting the most informative frames for labeling large videos, addressing duplicated data. You’ll also assess the effectiveness and efficiency of active machine learning systems through performance evaluation.By the end of the book, you’ll be able to enhance your active learning projects by leveraging Python libraries, frameworks, and commonly used tools.

Advanced Deep Learning with Keras. Apply deep learning techniques, autoencoders, GANs, variational autoencoders, deep reinforcement learning, policy gradients, and more

Rowel Atienza

Recent developments in deep learning, including Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), and Deep Reinforcement Learning (DRL) are creating impressive AI results in our news headlines - such as AlphaGo Zero beating world chess champions, and generative AI that can create art paintings that sell for over $400k because they are so human-like.Advanced Deep Learning with Keras is a comprehensive guide to the advanced deep learning techniques available today, so you can create your own cutting-edge AI. Using Keras as an open-source deep learning library, you'll find hands-on projects throughout that show you how to create more effective AI with the latest techniques.The journey begins with an overview of MLPs, CNNs, and RNNs, which are the building blocks for the more advanced techniques in the book. You’ll learn how to implement deep learning models with Keras and TensorFlow 1.x, and move forwards to advanced techniques, as you explore deep neural network architectures, including ResNet and DenseNet, and how to create autoencoders. You then learn all about GANs, and how they can open new levels of AI performance. Next, you’ll get up to speed with how VAEs are implemented, and you’ll see how GANs and VAEs have the generative power to synthesize data that can be extremely convincing to humans - a major stride forward for modern AI. To complete this set of advanced techniques, you'll learn how to implement DRL such as Deep Q-Learning and Policy Gradient Methods, which are critical to many modern results in AI.

Advanced Deep Learning with Python. Design and implement advanced next-generation AI solutions using TensorFlow and PyTorch

Ivan Vasilev

In order to build robust deep learning systems, you’ll need to understand everything from how neural networks work to training CNN models. In this book, you’ll discover newly developed deep learning models, methodologies used in the domain, and their implementation based on areas of application. You’ll start by understanding the building blocks and the math behind neural networks, and then move on to CNNs and their advanced applications in computer vision. You'll also learn to apply the most popular CNN architectures in object detection and image segmentation. Further on, you’ll focus on variational autoencoders and GANs. You’ll then use neural networks to extract sophisticated vector representations of words, before going on to cover various types of recurrent networks, such as LSTM and GRU. You’ll even explore the attention mechanism to process sequential data without the help of recurrent neural networks (RNNs). Later, you’ll use graph neural networks for processing structured data, along with covering meta-learning, which allows you to train neural networks with fewer training samples. Finally, you’ll understand how to apply deep learning to autonomous vehicles.By the end of this book, you’ll have mastered key deep learning concepts and the different applications of deep learning models in the real world.

Advanced Deep Learning with R. Become an expert at designing, building, and improving advanced neural network models using R

Bharatendra Rai

Deep learning is a branch of machine learning based on a set of algorithms that attempt to model high-level abstractions in data. Advanced Deep Learning with R will help you understand popular deep learning architectures and their variants in R, along with providing real-life examples for them. This deep learning book starts by covering the essential deep learning techniques and concepts for prediction and classification. You will learn about neural networks, deep learning architectures, and the fundamentals for implementing deep learning with R. The book will also take you through using important deep learning libraries such as Keras-R and TensorFlow-R to implement deep learning algorithms within applications. You will get up to speed with artificial neural networks, recurrent neural networks, convolutional neural networks, long short-term memory networks, and more using advanced examples. Later, you'll discover how to apply generative adversarial networks (GANs) to generate new images; autoencoder neural networks for image dimension reduction, image de-noising and image correction and transfer learning to prepare, define, train, and model a deep neural network. By the end of this book, you will be ready to implement your knowledge and newly acquired skills for applying deep learning algorithms in R through real-world examples.

Advanced Machine Learning with R. Tackle data analytics and machine learning challenges and build complex applications with R 3.5

Cory Lesmeister, Dr. Sunil Kumar Chinnamgari

R is one of the most popular languages when it comes to exploring the mathematical side of machine learning and easily performing computational statistics. This Learning Path shows you how to leverage the R ecosystem to build efficient machine learning applications that carry out intelligent tasks within your organization. You’ll work through realistic projects such as building powerful machine learning models with ensembles to predict employee attrition. Next, you’ll explore different clustering techniques to segment customers using wholesale data and even apply TensorFlow and Keras-R for performing advanced computations. Each chapter will help you implement advanced machine learning algorithms using real-world examples. You’ll also be introduced to reinforcement learning along with its use cases and models. Finally, this Learning Path will provide you with a glimpse into how some of these black box models can be diagnosed and understood. By the end of this Learning Path, you’ll be equipped with the skills you need to deploy machine learning techniques in your own projects.

Agile Machine Learning with DataRobot. Automate each step of the machine learning life cycle, from understanding problems to delivering value

Bipin Chadha, Sylvester Juwe

DataRobot enables data science teams to become more efficient and productive. This book helps you to address machine learning (ML) challenges with DataRobot's enterprise platform, enabling you to extract business value from data and rapidly create commercial impact for your organization.You'll begin by learning how to use DataRobot's features to perform data prep and cleansing tasks automatically. The book then covers best practices for building and deploying ML models, along with challenges faced while scaling them to handle complex business problems. Moving on, you'll perform exploratory data analysis (EDA) tasks to prepare your data to build ML models and ways to interpret results. You'll also discover how to analyze the model's predictions and turn them into actionable insights for business users. Next, you'll create model documentation for internal as well as compliance purposes and learn how the model gets deployed as an API. In addition, you'll find out how to operationalize and monitor the model's performance. Finally, you'll work with examples on time series forecasting, NLP, image processing, MLOps, and more using advanced DataRobot capabilities.By the end of this book, you'll have learned to use DataRobot's AutoML and MLOps features to scale ML model building by avoiding repetitive tasks and common errors.

Algorytmy Data Science. Siedmiodniowy przewodnik. Wydanie II

David Natingga

Data science jest interdyscyplinarną dziedziną naukową łączącą osiągnięcia uczenia maszynowego, statystyki i eksploracji danych. Umożliwia wydobywanie nowej wiedzy z istniejących danych poprzez stosowanie odpowiednich algorytmów i analizy statystycznej. Stworzono dotąd wiele algorytmów tej kategorii i wciąż powstają nowe. Stanowią one podstawę konstruowania modeli umożliwiających wyodrębnianie określonych informacji z danych odzwierciedlających zjawiska zachodzące w świecie rzeczywistym, pozwalają też na formułowanie prognoz ich przebiegu w przyszłości. Algorytmy data science są postrzegane jako ogromna szansa na zdobycie przewagi konkurencyjnej, a ich znaczenie stale rośnie. Ta książka jest zwięzłym przewodnikiem po algorytmach uczenia maszynowego. Jej cel jest prosty: w ciągu siedmiu dni masz opanować solidne podstawy siedmiu najważniejszych dla uczenia maszynowego algorytmów. Opisom poszczególnych algorytmów towarzyszą przykłady ich implementacji w języku Python, a praktyczne ćwiczenia, które znajdziesz na końcu każdego rozdziału, ułatwią Ci lepsze zrozumienie omawianych zagadnień. Co więcej, dzięki książce nauczysz się właściwie identyfikować problemy z zakresu data science. W konsekwencji dobieranie odpowiednich metod i narzędzi do ich rozwiązywania okaże się dużo łatwiejsze. W tej książce: efektywne implementacje algorytmów uczenia maszynowego w języku Python klasyfikacja danych przy użyciu twierdzenia Bayesa, drzew decyzyjnych i lasów losowych podział danych na klastery za pomocą algorytmu k-średnich stosowanie analizy regresji w parametryzacji modeli przewidywań analiza szeregów czasowych pod kątem trendów i sezonowości danych Algorytmy data science: poznaj, zrozum, zastosuj!

Algorytmy sztucznej inteligencji. Ilustrowany przewodnik

Rishal Hurbans

Sztuczna inteligencja ma umożliwiać wykorzystywanie danych i algorytmów do podejmowania lepszych decyzji, rozwiązywania trudnych problemów i automatyzowania złożonych zadań. Ma też zwiększać produktywność człowieka. Obecnie sztuczna inteligencja z rozmachem wkracza do kolejnych dziedzin. Budzi zachwyt, ale też kontrowersje i obawy. Nowe narzędzia, choćby były tworzone z najlepszymi intencjami, zawsze mogą zostać wykorzystane w niewłaściwy czy szkodliwy sposób. Oznacza to, że każdy, kto rozwija nowe technologie, powinien to robić odpowiedzialnie. Aby to było możliwe, trzeba dobrze zrozumieć podstawy działania sztucznej inteligencji - algorytmy. To praktyczny przewodnik po algorytmach sztucznej inteligencji. Skorzystają z niego programiści i inżynierowie, którzy chcą zrozumieć zagadnienia i algorytmy związane ze sztuczną inteligencją na podstawie praktycznych przykładów i wizualnych wyjaśnień. Książka pokazuje, jak radzić sobie z takimi zadaniami programistycznymi jak wykrywanie oszustw bankowych czy sterowanie pojazdem autonomicznym. Pierwsze rozdziały dotyczą podstawowych koncepcji i algorytmów i stają się punktem wyjścia do bardziej złożonych tematów: wydajnych algorytmów przeszukiwania oraz poszukiwania rozwiązań w środowisku konkurencyjnym. Przedstawiono tu zagadnienia uczenia maszynowego, w tym proces przygotowania danych, modelowania i testowania. Omówiono też zasady uczenia przez wzmacnianie za pomocą algorytmu Q-learning. W książce: kategorie i znaczenie algorytmów sztucznej inteligencji. inteligentne przeszukiwanie w procesie podejmowania decyzji algorytmy genetyczne i inteligencja rozproszona uczenie maszynowe i sieci neuronowe uczenie przez wzmacnianie Zrozum algorytmy, a pojmiesz istotę sztucznej inteligencji!

Algorytmy uczenia maszynowego. Zaawansowane techniki implementacji

Giuseppe Bonaccorso

Imponujący rozwój standardowych algorytmów przy ciągłej obniżce cen sprzętu i udostępnianiu coraz to szybszych komponentów przyczynił się do zrewolucjonizowania wielu gałęzi przemysłu. Obecnie uczenie maszynowe pozwala automatyzować procesy, które do niedawna musiały być zarządzane przez człowieka. Zadania, które jeszcze dekadę temu stanowiły nieprzekraczalną przeszkodę, dziś są wykonywane przez zwykły komputer osobisty. W efekcie dzięki technologii oraz dostępnym wysokopoziomowym otwartym platformom każdy, kto zainteresuje się uczeniem maszynowym, może projektować i wdrażać niezwykle potężne modele. Celem tej książki jest przybliżenie profesjonalistom tajników złożonych algorytmów uczenia maszynowego i zasad ich stosowania w praktyce. Poza praktycznymi informacjami dotyczącymi działania algorytmów i ich wdrożeń znalazły się tu również niezbędne podstawy teoretyczne. Opisano klasyczne modele uczenia nadzorowanego, nienadzorowanego i półnadzorowanego. Wskazano, w jakich sytuacjach okazują się one najbardziej przydatne. Zaprezentowano techniki wydobywania danych za pomocą modeli bayesowskich, algorytmu MCMC, a także dzięki stosowaniu ukrytych modeli Markowa. Omówiono zestaw przydatnych do uczenia maszynowego narzędzi, takich jak biblioteki: scikit-learn, Keras i TensorFlow. Najciekawsze zagadnienia: najważniejsze koncepcje teoretyczne uczenia maszynowego modelowanie probabilistyczne i uczenie hebbowskie zaawansowane koncepcje modeli neuronowych modele generatywne, takie jak splotowe sieci GAN i sieci Wassersteina głębokie sieci przekonań zaawansowane algorytmy: TD(tylda), aktor-krytyk, SARSA i Q-uczenie Uczenie maszynowe - już dziś zaimplementuj rozwiązania przyszłości!

Analityk danych. Przewodnik po data science, statystyce i uczeniu maszynowym

Alex J. Gutman, Jordan Goldmeier

Musisz spojrzeć prawdzie w oczy: epoka danych to nie tylko imponujące możliwości, ale również obietnice bez pokrycia. Firmy wdrażają rozwiązania, które mają je wyręczać w podejmowaniu decyzji. Menedżerowie zatrudniają analityków, którzy nimi nie są. Specjaliści w dziedzinie data science są zatrudniani w organizacjach, które nie są na nich gotowe. Dyrektorzy wysłuchują technicznego żargonu i udają, że go rozumieją. Efekt? Pieniądze idą w błoto. Oto praktyczny przewodnik po nauce o danych w miejscu pracy. Dowiesz się stąd wszystkiego, co ważne na początku Twojej drogi jako danologa: od osobowości, z którymi przyjdzie Ci pracować, przez detale analizy danych, po matematykę stojącą za algorytmami i uczeniem maszynowym. Nauczysz się myśleć krytycznie o danych i otrzymanych wynikach, będziesz też inteligentnie o tym mówić. Jednym zdaniem: zrozumiesz dane i związane z nimi wyzwania na głębszym, profesjonalnym poziomie. To książka dla każdego, kto chce przestawić firmę na tory data science. Eric Weber, kierownik ds. eksperymentów i badań metrycznych, Yelp Naucz się: myśleć statystycznie i rozumieć rolę zmienności w podejmowaniu decyzji zadawać właściwe pytania na temat statystyk i wyników analiz sensownie korzystać z rozwiązań uczenia maszynowego i sztucznej inteligencji unikać typowych błędów podczas pracy z danymi i ich interpretowania Data science? Odsiejesz piasek od złota!

Apache Mahout Essentials. Implement top-notch machine learning algorithms for classification, clustering, and recommendations with Apache Mahout

Jayani Withanawasam

Apache Spark 2.x Machine Learning Cookbook. Over 100 recipes to simplify machine learning model implementations with Spark

Siamak Amirghodsi, Shuen Mei, Meenakshi Rajendran, Broderick Hall

Machine learning aims to extract knowledge from data, relying on fundamental concepts in computer science, statistics, probability, and optimization. Learning about algorithms enables a wide range of applications, from everyday tasks such as product recommendations and spam filtering to cutting edge applications such as self-driving cars and personalized medicine. You will gain hands-on experience of applying these principles using Apache Spark, a resilient cluster computing system well suited for large-scale machine learning tasks.This book begins with a quick overview of setting up the necessary IDEs to facilitate the execution of code examples that will be covered in various chapters. It also highlights some key issues developers face while working with machine learning algorithms on the Spark platform. We progress by uncovering the various Spark APIs and the implementation of ML algorithms with developing classification systems, recommendation engines, text analytics, clustering, and learning systems. Toward the final chapters, we’ll focus on building high-end applications and explain various unsupervised methodologies and challenges to tackle when implementing with big data ML systems.

Apache Spark Deep Learning Cookbook. Over 80 best practice recipes for the distributed training and deployment of neural networks using Keras and TensorFlow

Ahmed Sherif, Amrith Ravindra

Organizations these days need to integrate popular big data tools such as Apache Spark with highly efficient deep learning libraries if they’re looking to gain faster and more powerful insights from their data. With this book, you’ll discover over 80 recipes to help you train fast, enterprise-grade, deep learning models on Apache Spark.Each recipe addresses a specific problem, and offers a proven, best-practice solution to difficulties encountered while implementing various deep learning algorithms in a distributed environment. The book follows a systematic approach, featuring a balance of theory and tips with best practice solutions to assist you with training different types of neural networks such as convolutional neural networks (CNNs) and recurrent neural networks (RNNs). You’ll also have access to code written in TensorFlow and Keras that you can run on Spark to solve a variety of deep learning problems in computer vision and natural language processing (NLP), or tweak to tackle other problems encountered in deep learning.By the end of this book, you'll have the skills you need to train and deploy state-of-the-art deep learning models on Apache Spark.

Applied Data Visualization with R and ggplot2. Create useful, elaborate, and visually appealing plots

Dr. Tania Moulik

Applied Data Visualization with R and ggplot2 introduces you to the world of data visualization by taking you through the basic features of ggplot2. To start with, you’ll learn how to set up the R environment, followed by getting insights into the grammar of graphics and geometric objects before you explore the plotting techniques.You’ll discover what layers, scales, coordinates, and themes are, and study how you can use them to transform your data into aesthetical graphs. Once you’ve grasped the basics, you’ll move on to studying simple plots such as histograms and advanced plots such as superimposing and density plots. You’ll also get to grips with plotting trends, correlations, and statistical summaries.By the end of this book, you’ll have created data visualizations that will impress your clients.

Applied Deep Learning and Computer Vision for Self-Driving Cars. Build autonomous vehicles using deep neural networks and behavior-cloning techniques

Sumit Ranjan, Dr. S. Senthamilarasu

Thanks to a number of recent breakthroughs, self-driving car technology is now an emerging subject in the field of artificial intelligence and has shifted data scientists' focus to building autonomous cars that will transform the automotive industry. This book is a comprehensive guide to use deep learning and computer vision techniques to develop autonomous cars. Starting with the basics of self-driving cars (SDCs), this book will take you through the deep neural network techniques required to get up and running with building your autonomous vehicle. Once you are comfortable with the basics, you'll delve into advanced computer vision techniques and learn how to use deep learning methods to perform a variety of computer vision tasks such as finding lane lines, improving image classification, and so on. You will explore the basic structure and working of a semantic segmentation model and get to grips with detecting cars using semantic segmentation. The book also covers advanced applications such as behavior-cloning and vehicle detection using OpenCV, transfer learning, and deep learning methodologies to train SDCs to mimic human driving.By the end of this book, you'll have learned how to implement a variety of neural networks to develop your own autonomous vehicle using modern Python libraries.

Applied Deep Learning with Keras. Solve complex real-life problems with the simplicity of Keras

Ritesh Bhagwat, Mahla Abdolahnejad, Matthew Moocarme

Though designing neural networks is a sought-after skill, it is not easy to master. With Keras, you can apply complex machine learning algorithms with minimum code.Applied Deep Learning with Keras starts by taking you through the basics of machine learning and Python all the way to gaining an in-depth understanding of applying Keras to develop efficient deep learning solutions. To help you grasp the difference between machine and deep learning, the book guides you on how to build a logistic regression model, first with scikit-learn and then with Keras. You will delve into Keras and its many models by creating prediction models for various real-world scenarios, such as disease prediction and customer churning. You’ll gain knowledge on how to evaluate, optimize, and improve your models to achieve maximum information. Next, you’ll learn to evaluate your model by cross-validating it using Keras Wrapper and scikit-learn. Following this, you’ll proceed to understand how to apply L1, L2, and dropout regularization techniques to improve the accuracy of your model. To help maintain accuracy, you’ll get to grips with applying techniques including null accuracy, precision, and AUC-ROC score techniques for fine tuning your model.By the end of this book, you will have the skills you need to use Keras when building high-level deep neural networks.

Applied Geospatial Data Science with Python. Leverage geospatial data analysis and modeling to find unique solutions to environmental problems

David S. Jordan

Data scientists, when presented with a myriad of data, can often lose sight of how to present geospatial analyses in a meaningful way so that it makes sense to everyone. Using Python to visualize data helps stakeholders in less technical roles to understand the problem and seek solutions. The goal of this book is to help data scientists and GIS professionals learn and implement geospatial data science workflows using Python.Throughout this book, you’ll uncover numerous geospatial Python libraries with which you can develop end-to-end spatial data science workflows. You’ll learn how to read, process, and manipulate spatial data effectively. With data in hand, you’ll move on to crafting spatial data visualizations to better understand and tell the story of your data through static and dynamic mapping applications. As you progress through the book, you’ll find yourself developing geospatial AI and ML models focused on clustering, regression, and optimization. The use cases can be leveraged as building blocks for more advanced work in a variety of industries.By the end of the book, you’ll be able to tackle random data, find meaningful correlations, and make geospatial data models.

Applied Unsupervised Learning with R. Uncover hidden relationships and patterns with k-means clustering, hierarchical clustering, and PCA

Alok Malik, Bradford Tuckfield

Starting with the basics, Applied Unsupervised Learning with R explains clustering methods, distribution analysis, data encoders, and features of R that enable you to understand your data better and get answers to your most pressing business questions. This book begins with the most important and commonly used method for unsupervised learning - clustering - and explains the three main clustering algorithms - k-means, divisive, and agglomerative. Following this, you'll study market basket analysis, kernel density estimation, principal component analysis, and anomaly detection. You'll be introduced to these methods using code written in R, with further instructions on how to work with, edit, and improve R code. To help you gain a practical understanding, the book also features useful tips on applying these methods to real business problems, including market segmentation and fraud detection. By working through interesting activities, you'll explore data encoders and latent variable models. By the end of this book, you will have a better understanding of different anomaly detection methods, such as outlier detection, Mahalanobis distances, and contextual and collective anomaly detection.

Artificial Intelligence and Machine Learning Fundamentals. Develop real-world applications powered by the latest AI advances

Zsolt Nagy

Machine learning and neural networks are pillars on which you can build intelligent applications. Artificial Intelligence and Machine Learning Fundamentals begins by introducing you to Python and discussing AI search algorithms. You will cover in-depth mathematical topics, such as regression and classification, illustrated by Python examples.As you make your way through the book, you will progress to advanced AI techniques and concepts, and work on real-life datasets to form decision trees and clusters. You will be introduced to neural networks, a powerful tool based on Moore's law.By the end of this book, you will be confident when it comes to building your own AI applications with your newly acquired skills!

Artificial Intelligence By Example. Develop machine intelligence from scratch using real artificial intelligence use cases

Denis Rothman

Artificial intelligence has the potential to replicate humans in every field. Artificial Intelligence By Example serves as a starting point for you to understand how AI is built, with the help of intriguing examples and case studies.Artificial Intelligence By Example will make you an adaptive thinker and help you apply concepts to real-life scenarios. Using some of the most interesting AI examples, right from a simple chess engine to a cognitive chatbot, you will learn how to tackle the machine you are competing with. You will study some of the most advanced machine learning models, understand how to apply AI to blockchain and IoT, and develop emotional quotient in chatbots using neural networks.You will move on to designing AI solutions in a simple manner rather than get confused by complex architectures and techniques. This comprehensive guide will be a starter kit for you to develop AI applications on your own.By the end of this book, you will have understood the fundamentals of AI and worked through a number of case studies that will help you develop your business vision.

Artificial Intelligence for Big Data. Complete guide to automating Big Data solutions using Artificial Intelligence techniques

Anand Deshpande, Manish Kumar

In this age of big data, companies have larger amount of consumer data than ever before, far more than what the current technologies can ever hope to keep up with. However, Artificial Intelligence closes the gap by moving past human limitations in order to analyze data.With the help of Artificial Intelligence for big data, you will learn to use Machine Learning algorithms such as k-means, SVM, RBF, and regression to perform advanced data analysis. You will understand the current status of Machine and Deep Learning techniques to work on Genetic and Neuro-Fuzzy algorithms. In addition, you will explore how to develop Artificial Intelligence algorithms to learn from data, why they are necessary, and how they can help solve real-world problems.By the end of this book, you'll have learned how to implement various Artificial Intelligence algorithms for your big data systems and integrate them into your product offerings such as reinforcement learning, natural language processing, image recognition, genetic algorithms, and fuzzy logic systems.

Artificial Intelligence for Robotics. Build intelligent robots that perform human tasks using AI techniques

Francis X. Govers

Artificial Intelligence for Robotics starts with an introduction to Robot Operating Systems (ROS), Python, robotic fundamentals, and the software and tools that are required to start out with robotics. You will learn robotics concepts that will be useful for making decisions, along with basic navigation skills.As you make your way through the chapters, you will learn about object recognition and genetic algorithms, which will teach your robot to identify and pick up an irregular object. With plenty of use cases throughout, you will explore natural language processing (NLP) and machine learning techniques to further enhance your robot. In the concluding chapters, you will learn about path planning and goal-oriented programming, which will help your robot prioritize tasks.By the end of this book, you will have learned to give your robot an artificial personality using simulated intelligence.

Automated Machine Learning. Hyperparameter optimization, neural architecture search, and algorithm selection with cloud platforms

Adnan Masood, Ahmed Sherif

Every machine learning engineer deals with systems that have hyperparameters, and the most basic task in automated machine learning (AutoML) is to automatically set these hyperparameters to optimize performance. The latest deep neural networks have a wide range of hyperparameters for their architecture, regularization, and optimization, which can be customized effectively to save time and effort.This book reviews the underlying techniques of automated feature engineering, model and hyperparameter tuning, gradient-based approaches, and much more. You'll discover different ways of implementing these techniques in open source tools and then learn to use enterprise tools for implementing AutoML in three major cloud service providers: Microsoft Azure, Amazon Web Services (AWS), and Google Cloud Platform. As you progress, you’ll explore the features of cloud AutoML platforms by building machine learning models using AutoML. The book will also show you how to develop accurate models by automating time-consuming and repetitive tasks in the machine learning development lifecycle.By the end of this machine learning book, you’ll be able to build and deploy AutoML models that are not only accurate, but also increase productivity, allow interoperability, and minimize feature engineering tasks.