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Deep learning (Machine learning)

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Methodologies and recipes to regularize any machine learning and deep learning model using cutting-edge technologies such as stable diffusion, Dall-E and GPT-3 Purchase of the print or Kindle book includes a free PDF eBook Key Features Learn to diagnose the need for regularization in any machine learning model Regularize different ML models using a variety of techniques and methods Enhance the functionality of your models using state of the art computer vision and NLP techniques Book Description Regularization is an infallible way to produce accurate results with unseen data, however, applying regularization is challenging as it is available in multiple forms and applying the appropriate technique to every model is a must. The Regularization Cookbook provides you with the appropriate tools and methods to handle any case, with ready-to-use working codes as well as theoretical explanations. After an introduction to regularization and methods to diagnose when to use it, you'll start implementing regularization techniques on linear models, such as linear and logistic regression, and tree-based models, such as random forest and gradient boosting. You'll then be introduced to specific regularization methods based on data, high cardinality features, and imbalanced datasets. In the last five chapters, you'll discover regularization for deep learning models. After reviewing general methods that apply to any type of neural network, you'll dive into more NLP-specific methods for RNNs and transformers, as well as using BERT or GPT-3. By the end, you'll explore regularization for computer vision, covering CNN specifics, along with the use of generative models such as stable diffusion and Dall-E. By the end of this book, you'll be armed with different regularization techniques to apply to your ML and DL models. What you will learn Diagnose overfitting and the need for regularization Regularize common linear models such as logistic regression Understand regularizing tree-based models such as XGBoos Uncover the secrets of structured data to regularize ML models Explore general techniques to regularize deep learning models Discover specific regularization techniques for NLP problems using transformers Understand the regularization in computer vision models and CNN architectures Apply cutting-edge computer vision regularization with generative models Who this book is for This book is for data scientists, machine learning engineers, and machine learning enthusiasts, looking to get hands-on knowledge to improve the performances of their models. Basic knowledge of Python is a prerequisite.

Machine learning. --- Deep learning (Machine learning)

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Data Scientist Pocket Guide by Mohamed Sabri is a comprehensive reference designed for data scientists at all levels, from beginners to experienced professionals. It is structured as a dictionary or glossary covering over 600 concepts and processes in machine learning and deep learning. The book aims to provide clear and reliable explanations of data science terminology, fostering a deeper understanding of foundational concepts beyond mere coding skills. It serves as a quick reference guide for data scientists seeking to clarify terms and concepts they encounter in their work. The guide is intended to be a practical resource that can be consulted as needed, rather than read sequentially, making it a valuable tool for professionals in the field.

Machine learning. --- Deep learning (Machine learning)

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Deep learning enables previously unattainable feats in automation, but extracting real-world business value from it is a daunting task. This book will teach you how to build complex deep learning models and gain intuition for structuring your data to accomplish your deep learning objectives. This deep learning book explores every aspect of the deep learning life cycle, from planning and data preparation to model deployment and governance, using real-world scenarios that will take you through creating, deploying, and managing advanced solutions. You’ll also learn how to work with image, audio, text, and video data using deep learning architectures, as well as optimize and evaluate your deep learning models objectively to address issues such as bias, fairness, adversarial attacks, and model transparency. As you progress, you’ll harness the power of AI platforms to streamline the deep learning life cycle and leverage Python libraries and frameworks such as PyTorch, ONNX, Catalyst, MLFlow, Captum, Nvidia Triton, Prometheus, and Grafana to execute efficient deep learning architectures, optimize model performance, and streamline the deployment processes. You’ll also discover the transformative potential of large language models (LLMs) for a wide array of applications. By the end of this book, you'll have mastered deep learning techniques to unlock its full potential for your endeavors.

Deep learning (Machine learning) --- Industrial applications.

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A vital guide to building the platforms and systems that bring deep learning models to production. In Designing Deep Learning Systems you will learn how to: Transfer your software development skills to deep learning systems Recognize and solve common engineering challenges for deep learning systems Understand the deep learning development cycle Automate training for models in TensorFlow and PyTorch Optimize dataset management, training, model serving and hyperparameter tuning Pick the right open-source project for your platform Deep learning systems are the components and infrastructure essential to supporting a deep learning model in a production environment. Written especially for software engineers with minimal knowledge of deep learning's design requirements, Designing Deep Learning Systems is full of hands-on examples that will help you transfer your software development skills to creating these deep learning platforms. You'll learn how to build automated and scalable services for core tasks like dataset management, model training/serving, and hyperparameter tuning. This book is the perfect way to step into an exciting--and lucrative--career as a deep learning engineer. About the Technology To be practically usable, a deep learning model must be built into a software platform. As a software engineer, you need a deep understanding of deep learning to create such a system. Th is book gives you that depth. About the Book Designing Deep Learning Systems: A software engineer's guide teaches you everything you need to design and implement a production-ready deep learning platform. First, it presents the big picture of a deep learning system from the developer's perspective, including its major components and how they are connected. Then, it carefully guides you through the engineering methods you'll need to build your own maintainable, efficient, and scalable deep learning platforms. What's Inside The deep learning development cycle Automate training in TensorFlow and PyTorch Dataset management, model serving, and hyperparameter tuning A hands-on deep learning lab About the Reader For software developers and engineering-minded data scientists. Examples in Java and Python. About the Authors Chi Wang is a principal software developer in the Salesforce Einstein group. Donald Szeto was the co-founder and CTO of PredictionIO. Quotes Read it once to get the big picture and then return to it again and again when building systems, designing components, and making crucial choices to satisfy all the teams that use them. - From the Foreword by Silvio Savarese and Caiming Xiong, Salesforce Written by true industry experts. Their insights are invaluable for software engineers looking to design and implement maintainable platforms for DL model development that meet the highest standards of efficiency and scalability. - Simon Chan, Firsthand Alliance Invaluable and timely insights for teams expanding their DL systems. This book anticipates the needs of a diverse set of organizations, and its content can be easily tailored to your current situation or your personal interests. - Weiping Peng, Airbnb.

Deep learning (Machine learning) --- Machine learning.