Practical Deep Learning Projects With Source Book Pdf
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Practical Java Machine Learning
Build machine learning (ML) solutions for Java development. This book shows you that when designing ML apps, data is the key driver and must be considered throughout all phases of the project life cycle. Practical Java Machine Learning helps you understand the importance of data and how to organize it for use within your ML project. You will be introduced to tools which can help you identify and manage your data including JSON, visualization, NoSQL databases, and cloud platforms including Google Cloud Platform and Amazon Web Services. Practical Java Machine Learning includes multiple projects, with particular focus on the Android mobile platform and features such as sensors, camera, and connectivity, each of which produce data that can power unique machine learning solutions. You will learn to build a variety of applications that demonstrate the capabilities of the Google Cloud Platform machine learning API, including data visualizationfor Java; document classification using the Weka ML environment; audio file classification for Android using ML with spectrogram voice data; and machine learning using device sensor data. After reading this book, you will come away with case study examples and projects that you can take away as templates for re-use and exploration for your own machine learning programming projects with Java. What You Will Learn Identify, organize, and architect the data required for ML projects Deploy ML solutions in conjunction with cloud providers such as Google and Amazon Determine which algorithm is the most appropriate for a specific ML problem Implement Java ML solutions on Android mobile devices Create Java ML solutions to work with sensor data Build Java streaming based solutions Who This Book Is For Experienced Java developers who have not implemented machine learning techniques before.
Mastering Machine Learning: A Friendly Guide to Understanding How AI Learns
If you've ever wondered how Netflix always knows what you want to watch… If you've felt overwhelmed by the buzz around artificial intelligence but wished someone would just explain it simply… If you're a student, professional, or curious mind looking to use AI without needing a tech degree… This book is for you. Demystifying the Smart Tech Behind Chatbots, Face Recognition, and Predictive Magic—For Curious Minds of All Ages Mastering Machine Learning: A Friendly Guide to Understanding How AI Learns is your god-sent crash course into the invisible power behind the tech we use every day. It’s not just a book—it’s your personal guide to unlocking smart solutions for everyday problems. Packed with: ✅ Tips & Tricks anyone can use, with step-by-step guides for building your own smart tools ✅ Real-life stories of how machine learning has transformed homes, classrooms, and businesses ✅ Eye-popping illustrations & relatable analogies that make complex ideas surprisingly easy ✅ DIY projects & cheat sheets for hands-on learning—even if you’re tech-shy ✅ Ethical insights to help you use AI responsibly and wisely ✅ Bonus content on how sci-fi inspired today’s smart tech Whether you're a curious teen, a creative entrepreneur, or a life-long learner, this book is your backstage pass into the world of learning machines—and how they can help you learn, grow, and thrive. GET YOUR COPY TODAY! 🚀
Machine Learning Engineering with MLflow
Author: Natu Lauchande
language: en
Publisher: Packt Publishing Ltd
Release Date: 2021-08-27
Get up and running, and productive in no time with MLflow using the most effective machine learning engineering approach Key FeaturesExplore machine learning workflows for stating ML problems in a concise and clear manner using MLflowUse MLflow to iteratively develop a ML model and manage it Discover and work with the features available in MLflow to seamlessly take a model from the development phase to a production environmentBook Description MLflow is a platform for the machine learning life cycle that enables structured development and iteration of machine learning models and a seamless transition into scalable production environments. This book will take you through the different features of MLflow and how you can implement them in your ML project. You will begin by framing an ML problem and then transform your solution with MLflow, adding a workbench environment, training infrastructure, data management, model management, experimentation, and state-of-the-art ML deployment techniques on the cloud and premises. The book also explores techniques to scale up your workflow as well as performance monitoring techniques. As you progress, you'll discover how to create an operational dashboard to manage machine learning systems. Later, you will learn how you can use MLflow in the AutoML, anomaly detection, and deep learning context with the help of use cases. In addition to this, you will understand how to use machine learning platforms for local development as well as for cloud and managed environments. This book will also show you how to use MLflow in non-Python-based languages such as R and Java, along with covering approaches to extend MLflow with Plugins. By the end of this machine learning book, you will be able to produce and deploy reliable machine learning algorithms using MLflow in multiple environments. What you will learnDevelop your machine learning project locally with MLflow's different featuresSet up a centralized MLflow tracking server to manage multiple MLflow experimentsCreate a model life cycle with MLflow by creating custom modelsUse feature streams to log model results with MLflowDevelop the complete training pipeline infrastructure using MLflow featuresSet up an inference-based API pipeline and batch pipeline in MLflowScale large volumes of data by integrating MLflow with high-performance big data librariesWho this book is for This book is for data scientists, machine learning engineers, and data engineers who want to gain hands-on machine learning engineering experience and learn how they can manage an end-to-end machine learning life cycle with the help of MLflow. Intermediate-level knowledge of the Python programming language is expected.