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Neural Network Programming with TensorFlow
Neural Networks and their implementation decoded with TensorFlow About This Book Develop a strong background in neural network programming from scratch, using the popular Tensorflow library. Use Tensorflow to implement different kinds of neural networks – from simple feedforward neural networks to multilayered perceptrons, CNNs, RNNs and more. A highly practical guide including real-world datasets and use-cases to simplify your understanding of neural networks and their implementation. Who This Book Is For This book is meant for developers with a statistical background who want to work with neural networks. Though we will be using TensorFlow as the underlying library for neural networks, b...
Machine Learning with Spark
Create scalable machine learning applications to power a modern data-driven business using Spark 2.x About This Book Get to the grips with the latest version of Apache Spark Utilize Spark's machine learning library to implement predictive analytics Leverage Spark's powerful tools to load, analyze, clean, and transform your data Who This Book Is For If you have a basic knowledge of machine learning and want to implement various machine-learning concepts in the context of Spark ML, this book is for you. You should be well versed with the Scala and Python languages. What You Will Learn Get hands-on with the latest version of Spark ML Create your first Spark program with Scala and Python Set up ...
Generative Adversarial Networks Cookbook
Simplify next-generation deep learning by implementing powerful generative models using Python, TensorFlow and Keras Key FeaturesUnderstand the common architecture of different types of GANsTrain, optimize, and deploy GAN applications using TensorFlow and KerasBuild generative models with real-world data sets, including 2D and 3D dataBook Description Developing Generative Adversarial Networks (GANs) is a complex task, and it is often hard to find code that is easy to understand. This book leads you through eight different examples of modern GAN implementations, including CycleGAN, simGAN, DCGAN, and 2D image to 3D model generation. Each chapter contains useful recipes to build on a common ar...
Keras Deep Learning Cookbook
Leverage the power of deep learning and Keras to develop smarter and more efficient data models Key FeaturesUnderstand different neural networks and their implementation using KerasExplore recipes for training and fine-tuning your neural network modelsPut your deep learning knowledge to practice with real-world use-cases, tips, and tricksBook Description Keras has quickly emerged as a popular deep learning library. Written in Python, it allows you to train convolutional as well as recurrent neural networks with speed and accuracy. The Keras Deep Learning Cookbook shows you how to tackle different problems encountered while training efficient deep learning models, with the help of the popular...
Practical Convolutional Neural Networks
One stop guide to implementing award-winning, and cutting-edge CNN architectures Key Features Fast-paced guide with use cases and real-world examples to get well versed with CNN techniques Implement CNN models on image classification, transfer learning, Object Detection, Instance Segmentation, GANs and more Implement powerful use-cases like image captioning, reinforcement learning for hard attention, and recurrent attention models Book Description Convolutional Neural Network (CNN) is revolutionizing several application domains such as visual recognition systems, self-driving cars, medical discoveries, innovative eCommerce and more.You will learn to create innovative solutions around image a...
Teknologi Big Data : Pengantar dan Penerapan Teknologi Big Data di berbagai Bidang
Buku "Teknologi Big Data: Pengantar dan Penerapan di Berbagai Bidang" merangkum esensi teknologi Big Data dari awal hingga penerapannya dalam konteks kehidupan nyata. Melalui konsep dasar yang jelas seperti pengumpulan, ingest, dan penyimpanan data, pembaca diperkenalkan pada kerangka kerja Big Data yang penting untuk memahami kompleksitasnya. Setelah itu, buku ini menyajikan serangkaian studi kasus dan contoh aplikatif yang mencakup lingkungan, ekonomi, pendidikan, pemerintahan, transportasi, UMKM, industri, dan kesehatan, memberikan gambaran komprehensif tentang bagaimana teknologi Big Data merespon tantangan dan memperkaya solusi di berbagai bidang kehidupan. Dengan penekanan pada implementasi praktis dan pemahaman yang mendalam, buku ini tidak hanya menjadi panduan bermanfaat bagi pembaca yang ingin memahami Big Data, tetapi juga mengilhami untuk menjelajahi potensi inovatif teknologi ini dalam meningkatkan efisiensi dan menghasilkan dampak positif dalam masyarakat.
Machine Learning with Spark - Second Edition
Develop intelligent machine learning systems with SparkAbout This Book*Get to the grips with the latest version of Apache Spark*Utilize Spark's machine learning library to implement predictive analytics*Leverage Spark's powerful tools to load, analyze, clean, and transform your dataWho This Book Is ForIf you have a basic knowledge of machine learning and want to implement various machine-learning concepts in the context of Spark ML, this book is for you. You should be well versed with the Scala and Python languages.What You Will Learn*Get hands-on with the latest version of Spark ML*Create your first Spark program with Scala and Python*Set up and configure a development environment for Spark...
Machine Learning in Python
Learn a simpler and more effective way to analyze data and predict outcomes with Python Machine Learning in Python shows you how to successfully analyze data using only two core machine learning algorithms, and how to apply them using Python. By focusing on two algorithm families that effectively predict outcomes, this book is able to provide full descriptions of the mechanisms at work, and the examples that illustrate the machinery with specific, hackable code. The algorithms are explained in simple terms with no complex math and applied using Python, with guidance on algorithm selection, data preparation, and using the trained models in practice. You will learn a core set of Python program...
Data Science und AI
Im vorliegenden Buch soll eine praxisorientierte Einführung und ein aktueller Überblick darüber gegeben werden, was Data-Science und der Beruf Data-Scientist umfassen. Nach 4 Jahren seit Erscheinen der zweiten Auflage wurde die dritte Auflage notwendig, da sich Data-Science als Thema und vor allem die dazugehörende Softwaretechnologie weiterentwickelt. Spätestens mit der Veröffentlichung von ChatGPT ist das Thema künstliche Intelligenz in aller Munde und eine Einordnung von Data- Science, Machine Learning und Artificial Intelligence scheint dringend notwendig. Das Buch enthält neben einer Übersicht über Theorie und Praxis der Daten-Analyse nun auch Code-Beispiele in Python bzw. SQL und Cheat-Sheets zu ChatGPT und GenAI Tools.
Data Science
Das Thema Data-Science wird häufig diskutiert. Seit der ersten Auflage dieses Buches im Jahr 2017 hat sich an diesem Trend wenig verändert. Data-Scientisten (m/w/d) erfahren eine steigende Nachfrage auf dem Job-Markt, da immer mehr Unternehmen ihre Analytics-Abteilungen auf- bzw. ausbauen und hierfür entsprechende Mitarbeiter suchen. Hier stellt sich die Frage, worin eigentlich der Tätigkeitsbereich eines Data-Scientisten besteht. Das Aufgabenfeld ist nicht eindeutig definiert und reicht über künstliche Intelligenz, Machine-Learning, Data-Mining, Python-Programmierung bis zu Big Data. Im vorliegenden Buch soll eine praxisorientierte Einführung und ein aktueller Überblick darüber gegeben werden, was Data-Science und der Beruf Data-Scientist umfassen.
