Hands On Transfer Learning With Python
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Hands On Transfer Learning With Python
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Author : Dipanjan Sarkar
language : en
Publisher: Packt Publishing Ltd
Release Date : 2018-08-31
Hands On Transfer Learning With Python written by Dipanjan Sarkar and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-08-31 with Computers categories.
Deep learning simplified by taking supervised, unsupervised, and reinforcement learning to the next level using the Python ecosystem Key Features Build deep learning models with transfer learning principles in Python implement transfer learning to solve real-world research problems Perform complex operations such as image captioning neural style transfer Book Description Transfer learning is a machine learning (ML) technique where knowledge gained during training a set of problems can be used to solve other similar problems. The purpose of this book is two-fold; firstly, we focus on detailed coverage of deep learning (DL) and transfer learning, comparing and contrasting the two with easy-to-follow concepts and examples. The second area of focus is real-world examples and research problems using TensorFlow, Keras, and the Python ecosystem with hands-on examples. The book starts with the key essential concepts of ML and DL, followed by depiction and coverage of important DL architectures such as convolutional neural networks (CNNs), deep neural networks (DNNs), recurrent neural networks (RNNs), long short-term memory (LSTM), and capsule networks. Our focus then shifts to transfer learning concepts, such as model freezing, fine-tuning, pre-trained models including VGG, inception, ResNet, and how these systems perform better than DL models with practical examples. In the concluding chapters, we will focus on a multitude of real-world case studies and problems associated with areas such as computer vision, audio analysis and natural language processing (NLP). By the end of this book, you will be able to implement both DL and transfer learning principles in your own systems. What you will learn Set up your own DL environment with graphics processing unit (GPU) and Cloud support Delve into transfer learning principles with ML and DL models Explore various DL architectures, including CNN, LSTM, and capsule networks Learn about data and network representation and loss functions Get to grips with models and strategies in transfer learning Walk through potential challenges in building complex transfer learning models from scratch Explore real-world research problems related to computer vision and audio analysis Understand how transfer learning can be leveraged in NLP Who this book is for Hands-On Transfer Learning with Python is for data scientists, machine learning engineers, analysts and developers with an interest in data and applying state-of-the-art transfer learning methodologies to solve tough real-world problems. Basic proficiency in machine learning and Python is required.
Hands On Transfer Learning With Tensorflow 2 X
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Author : CHANSUNG. PARK
language : en
Publisher:
Release Date : 2020
Hands On Transfer Learning With Tensorflow 2 X written by CHANSUNG. PARK and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020 with categories.
Hands On Deep Learning With Python
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Author : Rogers Isaacson
language : en
Publisher: Independently Published
Release Date : 2025-04-14
Hands On Deep Learning With Python written by Rogers Isaacson and has been published by Independently Published this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-04-14 with Computers categories.
Unlock the world of deep learning with Hands-On Deep Learning with Python. This in-depth guide will teach you how to build, train, and optimize neural networks, convolutional networks, and recurrent networks for real-world applications using Python. Whether you're a beginner looking to break into the world of AI or an experienced developer seeking to deepen your knowledge of deep learning techniques, this book provides step-by-step instructions and practical examples to help you implement cutting-edge models. Python, along with powerful libraries like TensorFlow, Keras, and PyTorch, is the most popular ecosystem for deep learning. This book will show you how to use these libraries to build state-of-the-art neural networks for a wide range of applications, from image classification and object detection to natural language processing and time-series forecasting. Inside, you'll learn: The fundamentals of deep learning, including what neural networks are, how they work, and the different types of networks (e.g., feedforward, convolutional, and recurrent) How to set up and use popular Python libraries for deep learning, such as TensorFlow, Keras, and PyTorch The principles behind training neural networks, including backpropagation, optimization algorithms, and loss functions How to build and train Convolutional Neural Networks (CNNs) for image recognition, classification, and segmentation tasks The basics of Recurrent Neural Networks (RNNs) and Long Short-Term Memory networks (LSTMs) for sequential data like text, speech, and time-series forecasting Advanced deep learning techniques, including transfer learning, data augmentation, and hyperparameter tuning How to evaluate model performance using metrics such as accuracy, precision, recall, and confusion matrices How to deploy deep learning models into production for real-time use Real-world case studies and projects that help you apply deep learning to various domains like healthcare, finance, and entertainment By the end of this book, you'll have the skills to implement advanced deep learning models using Python and apply them to solve practical problems. Hands-On Deep Learning with Python will empower you to tackle challenges in AI and machine learning and start building your own deep learning applications. Key Features: Step-by-step guidance for building neural networks, CNNs, and RNNs Hands-on projects using real-world datasets to practice and reinforce your learning Learn to implement deep learning techniques using Python libraries like TensorFlow, Keras, and PyTorch Advanced deep learning techniques like transfer learning, hyperparameter tuning, and model evaluation Practical advice for deploying deep learning models into real-world applications Start your deep learning journey today with Hands-On Deep Learning with Python and learn how to build, train, and deploy state-of-the-art neural networks for real-world problems.
Hands On Transfer Learning With Tensorflow 2 0
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Author : Margaret Maynard-Reid
language : en
Publisher:
Release Date : 2020
Hands On Transfer Learning With Tensorflow 2 0 written by Margaret Maynard-Reid and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020 with categories.
Hands-on implementation with the power of TensorFlow 2.0 About This Video Refresh your knowledge of CNN with in-depth explanations of how transfer learning works Use transfer learning for both image and text classification, and compare/contrast with the training from scratch approach Learn new features of TensorFlow 2.0, tf.keras, TensorFlow Hub, TensorFlow Model Maker, and on-device training In Detail Transfer learning involves using a pre-trained model on a new problem. It is currently very popular in the field of Deep Learning because it enables you to train Deep Neural Networks with comparatively little data. In Transfer learning, knowledge of an already trained Machine Learning model is applied to a different but related problem. The general idea is to use knowledge, which a model has learned from a task where a lot of labeled training data is available, in a new task where we don't have a lot of data. Instead of starting the learning process from scratch, you start from patterns that have been learned by solving a related task. In this course, learn how to implement transfer learning to solve a different set of machine learning problems by reusing pre-trained models to train other models. Hands-on examples with transfer learning will get you started, and allow you to master how and why it is extensively used in different deep learning domains. You will implement practical use cases of transfer learning in CNN and RNN such as using image classifiers, text classification, sentimental analysis, and much more. You'll be shown how to train models and how a pre-trained model is used to train similar untrained models in order to apply the transfer learning process even further. Allowing you to implement advanced use cases and learn how transfer learning is gaining momentum when it comes to solving real-world problems in deep learning. By the end of this course, you will not only be able to build machine learning models, but have mastered transferring with tf.keras, TensorFlow Hub, and TensorFlow Lite tools.
Hands On Neural Networks
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Author : Leonardo De Marchi
language : en
Publisher: Packt Publishing Ltd
Release Date : 2019-05-30
Hands On Neural Networks written by Leonardo De Marchi and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-05-30 with Computers categories.
Design and create neural networks with deep learning and artificial intelligence principles using OpenAI Gym, TensorFlow, and Keras Key FeaturesExplore neural network architecture and understand how it functionsLearn algorithms to solve common problems using back propagation and perceptronsUnderstand how to apply neural networks to applications with the help of useful illustrationsBook Description Neural networks play a very important role in deep learning and artificial intelligence (AI), with applications in a wide variety of domains, right from medical diagnosis, to financial forecasting, and even machine diagnostics. Hands-On Neural Networks is designed to guide you through learning about neural networks in a practical way. The book will get you started by giving you a brief introduction to perceptron networks. You will then gain insights into machine learning and also understand what the future of AI could look like. Next, you will study how embeddings can be used to process textual data and the role of long short-term memory networks (LSTMs) in helping you solve common natural language processing (NLP) problems. The later chapters will demonstrate how you can implement advanced concepts including transfer learning, generative adversarial networks (GANs), autoencoders, and reinforcement learning. Finally, you can look forward to further content on the latest advancements in the field of neural networks. By the end of this book, you will have the skills you need to build, train, and optimize your own neural network model that can be used to provide predictable solutions. What you will learnLearn how to train a network by using backpropagationDiscover how to load and transform images for use in neural networksStudy how neural networks can be applied to a varied set of applicationsSolve common challenges faced in neural network developmentUnderstand the transfer learning concept to solve tasks using Keras and Visual Geometry Group (VGG) networkGet up to speed with advanced and complex deep learning concepts like LSTMs and NLP Explore innovative algorithms like GANs and deep reinforcement learningWho this book is for If you are interested in artificial intelligence and deep learning and want to further your skills, then this intermediate-level book is for you. Some knowledge of statistics will help you get the most out of this book.
Hands On One Shot Learning With Python
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Author : Shruti Jadon
language : en
Publisher: Packt Publishing Ltd
Release Date : 2020-04-10
Hands On One Shot Learning With Python written by Shruti Jadon and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-04-10 with Computers categories.
Get to grips with building powerful deep learning models using PyTorch and scikit-learn Key FeaturesLearn how you can speed up the deep learning process with one-shot learningUse Python and PyTorch to build state-of-the-art one-shot learning modelsExplore architectures such as Siamese networks, memory-augmented neural networks, model-agnostic meta-learning, and discriminative k-shot learningBook Description One-shot learning has been an active field of research for scientists trying to develop a cognitive machine that mimics human learning. With this book, you'll explore key approaches to one-shot learning, such as metrics-based, model-based, and optimization-based techniques, all with the help of practical examples. Hands-On One-shot Learning with Python will guide you through the exploration and design of deep learning models that can obtain information about an object from one or just a few training samples. The book begins with an overview of deep learning and one-shot learning and then introduces you to the different methods you can use to achieve it, such as deep learning architectures and probabilistic models. Once you've got to grips with the core principles, you'll explore real-world examples and implementations of one-shot learning using PyTorch 1.x on datasets such as Omniglot and MiniImageNet. Finally, you'll explore generative modeling-based methods and discover the key considerations for building systems that exhibit human-level intelligence. By the end of this book, you'll be well-versed with the different one- and few-shot learning methods and be able to use them to build your own deep learning models. What you will learnGet to grips with the fundamental concepts of one- and few-shot learningWork with different deep learning architectures for one-shot learningUnderstand when to use one-shot and transfer learning, respectivelyStudy the Bayesian network approach for one-shot learningImplement one-shot learning approaches based on metrics, models, and optimization in PyTorchDiscover different optimization algorithms that help to improve accuracy even with smaller volumes of dataExplore various one-shot learning architectures based on classification and regressionWho this book is for If you're an AI researcher or a machine learning or deep learning expert looking to explore one-shot learning, this book is for you. It will help you get started with implementing various one-shot techniques to train models faster. Some Python programming experience is necessary to understand the concepts covered in this book.
Deep Learning With Python
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Author : GREYSON. CHESTERFIELD
language : en
Publisher: Independently Published
Release Date : 2025-03-16
Deep Learning With Python written by GREYSON. CHESTERFIELD and has been published by Independently Published this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-03-16 with Computers categories.
Deep Learning with Python: Build Neural Networks and AI Models from Scratch is a comprehensive, hands-on guide to mastering deep learning and neural network training using Python. Whether you're a beginner looking to dive into AI or an experienced practitioner seeking to improve your skills, this book will walk you through the concepts and tools needed to build deep learning models from scratch. Focusing on frameworks like TensorFlow and PyTorch, this book provides practical insights into developing, training, and deploying powerful neural networks. With clear explanations, step-by-step instructions, and real-world examples, you'll learn how to implement advanced AI models that can be applied to a wide range of problems in industries such as healthcare, finance, and more. Inside, you'll discover: Introduction to Deep Learning and Neural Networks: Learn the fundamentals of deep learning, neural networks, and the key components involved in building AI models. Understand the differences between shallow learning and deep learning, and the advantages of using deep neural networks for complex tasks. Setting Up Python for Deep Learning: Get started with the necessary tools and libraries, including TensorFlow, PyTorch, and Keras. Learn how to install and configure the tools, and understand the basics of Python for machine learning and deep learning. Building Your First Neural Network: Learn how to design and implement a simple feedforward neural network using TensorFlow and PyTorch. Discover how to train your network using backpropagation and gradient descent techniques. Activation Functions and Optimization: Explore the role of activation functions like ReLU, Sigmoid, and Tanh in neural networks, and learn how to optimize your models with techniques such as stochastic gradient descent, Adam, and more. Convolutional Neural Networks (CNNs): Dive into CNNs and learn how they are used for image recognition and computer vision tasks. Implement a CNN for tasks like object detection and image classification using TensorFlow and PyTorch. Recurrent Neural Networks (RNNs) and LSTMs: Understand how RNNs and Long Short-Term Memory (LSTM) networks are used for sequence data, such as time series forecasting and natural language processing. Learn how to implement and train these models for tasks like sentiment analysis and speech recognition. Transfer Learning and Pre-trained Models: Discover the power of transfer learning and how to leverage pre-trained models to build deep learning applications with less data and faster training times. Learn how to fine-tune models like VGG16, ResNet, and BERT for your specific needs. Regularization and Avoiding Overfitting: Learn techniques like dropout, batch normalization, and early stopping to prevent overfitting in your models. Understand how to improve the generalization of your neural networks for real-world applications. Model Evaluation and Fine-Tuning: Master the art of model evaluation using metrics like accuracy, precision, recall, and F1-score. Learn how to tune hyperparameters and optimize your deep learning models for better performance. Deploying Deep Learning Models: Learn how to deploy your trained deep learning models into production environments. Explore techniques for model saving, serving, and using cloud platforms like AWS and Google Cloud for model deployment. Practical Applications of Deep Learning: Gain hands-on experience with real-world deep learning applications, including image classification, sentiment analysis, stock price prediction, and healthcare diagnostics. By the end of this book, you'll have the skills to build and train complex neural networks and AI models from scratch. You'll be ready to apply deep learning to solve real-world problems and explore new AI possibilities.
International Conference On Future Technologies In Manufacturing Automation Design And Energy
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Author : A. Johnney Mertens
language : en
Publisher: Trans Tech Publications Ltd
Release Date : 2023-09-27
International Conference On Future Technologies In Manufacturing Automation Design And Energy written by A. Johnney Mertens and has been published by Trans Tech Publications Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-09-27 with Technology & Engineering categories.
Selected peer-reviewed full text papers from the 3rd International Conference on Future Technologies in Manufacturing, Automation, Design and Energy (ICOFT MADE 2022)
Deep Learning With Tensorflow And Keras In Python
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Author : Pythquill Publishing
language : en
Publisher: Independently Published
Release Date : 2025-06-27
Deep Learning With Tensorflow And Keras In Python written by Pythquill Publishing and has been published by Independently Published this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-06-27 with Computers categories.
What You Will Learn in This Book Build a Solid Foundation in Deep Learning: Gain a clear understanding of the core concepts of AI, machine learning, and deep learning, including the power of neural networks and the factors driving their current popularity. Master the TensorFlow and Keras Ecosystem: Learn to use TensorFlow 2.x and Keras to build, train, and evaluate your own deep learning models. You'll understand key components like Tensors, Layers, Optimizers, and Loss Functions, and learn to set up your own complete deep learning environment. Create Your Own Neural Networks: Get hands-on experience building foundational neural networks for classification and regression problems, learning the complete Keras workflow from defining models to making predictions and visualizing training history. Prevent Common Modeling Pitfalls: Understand the critical concepts of overfitting and underfitting and learn practical regularization techniques like Dropout, L1/L2 regularization, and Early Stopping to build more robust and generalizable models. Develop Advanced Computer Vision Solutions: Master Convolutional Neural Networks (CNNs) and their core components to build powerful image classification models. You'll learn how to work with image data, apply data augmentation, and use state-of-the-art pre-trained models with transfer learning. Work with Sequential Data like Text and Time Series: Dive into Recurrent Neural Networks (RNNs), including LSTMs and GRUs, to handle sequential data. You'll learn to prepare data for time series forecasting and perform sentiment analysis on text using word embeddings. Explore Cutting-Edge Architectures: Get a conceptual introduction to advanced models like Autoencoders for dimensionality reduction, Generative Adversarial Networks (GANs) for creating new data, and the groundbreaking Transformer architecture that powers modern NLP. Deploy Your Models to Production: Learn how to save your trained models in the recommended SavedModel format and explore different deployment strategies using TensorFlow Serving for web applications, TensorFlow Lite for mobile devices, and more. Enhance Model Performance and Interpretability: Discover techniques for hyperparameter tuning to optimize your models, and use tools like TensorBoard for visualization and debugging. You will also learn the basics of Explainable AI (XAI) to understand and interpret your model's predictions. Navigate the Ethical Landscape of AI: Understand the challenges of bias, fairness, and accountability in deep learning models and learn about responsible AI development practices.
Mitigating Bias In Machine Learning
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Author : Carlotta A. Berry
language : en
Publisher: McGraw Hill Professional
Release Date : 2024-10-18
Mitigating Bias In Machine Learning written by Carlotta A. Berry and has been published by McGraw Hill Professional this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-10-18 with Technology & Engineering categories.
This practical guide shows, step by step, how to use machine learning to carry out actionable decisions that do not discriminate based on numerous human factors, including ethnicity and gender. The authors examine the many kinds of bias that occur in the field today and provide mitigation strategies that are ready to deploy across a wide range of technologies, applications, and industries. Edited by engineering and computing experts, Mitigating Bias in Machine Learning includes contributions from recognized scholars and professionals working across different artificial intelligence sectors. Each chapter addresses a different topic and real-world case studies are featured throughout that highlight discriminatory machine learning practices and clearly show how they were reduced. Mitigating Bias in Machine Learning addresses: Ethical and Societal Implications of Machine Learning Social Media and Health Information Dissemination Comparative Case Study of Fairness Toolkits Bias Mitigation in Hate Speech Detection Unintended Systematic Biases in Natural Language Processing Combating Bias in Large Language Models Recognizing Bias in Medical Machine Learning and AI Models Machine Learning Bias in Healthcare Achieving Systemic Equity in Socioecological Systems Community Engagement for Machine Learning