Hands On Python And Pytorch
Download Hands On Python And Pytorch PDF/ePub or read online books in Mobi eBooks. Click Download or Read Online button to get Hands On Python And Pytorch book now. This website allows unlimited access to, at the time of writing, more than 1.5 million titles, including hundreds of thousands of titles in various foreign languages.
Hands-On Python and PyTorch
Author: Sarful Hassan
language: en
Publisher: Independently Published
Release Date: 2025-02-04
Hands-On Python and PyTorch: A Practical Guide to Deep Learning Master Deep Learning with Python and PyTorch Are you ready to dive into the world of deep learning and AI? Hands-On Python and PyTorch: A Practical Guide to Deep Learning is your step-by-step companion to mastering neural networks, machine learning models, and real-world AI applications with Python and PyTorch. Why This Book? ✅ Comprehensive & Hands-On - Covers everything from basic PyTorch operations to advanced deep learning techniques. ✅ Real-World Applications - Learn to build image classifiers, NLP models, GANs, and reinforcement learning systems. ✅ AI & Deep Learning Integration - Understand how PyTorch works with TensorFlow, OpenCV, and other AI frameworks. ✅ Optimized for Python - Uses Python 3.x for efficient and scalable implementation. ✅ Beginner to Expert Guide - Suitable for students, developers, data scientists, and AI enthusiasts looking to master PyTorch and deep learning. What You'll Learn ✔️ Setting up PyTorch and Python for deep learning projects ✔️ Core PyTorch concepts: Tensors, Autograd, and Modules ✔️ Building and training neural networks from scratch ✔️ Advanced optimization techniques and model tuning ✔️ Real-time applications in computer vision, NLP, and reinforcement learning ✔️ Deploying AI models efficiently for production Who Should Read This Book?
Hands-On One-shot Learning with Python
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.
Hands-on Machine Learning with Python
Here is the perfect comprehensive guide for readers with basic to intermediate level knowledge of machine learning and deep learning. It introduces tools such as NumPy for numerical processing, Pandas for panel data analysis, Matplotlib for visualization, Scikit-learn for machine learning, and Pytorch for deep learning with Python. It also serves as a long-term reference manual for the practitioners who will find solutions to commonly occurring scenarios. The book is divided into three sections. The first section introduces you to number crunching and data analysis tools using Python with in-depth explanation on environment configuration, data loading, numerical processing, data analysis, and visualizations. The second section covers machine learning basics and Scikit-learn library. It also explains supervised learning, unsupervised learning, implementation, and classification of regression algorithms, and ensemble learning methods in an easy manner with theoretical and practical lessons. The third section explains complex neural network architectures with details on internal working and implementation of convolutional neural networks. The final chapter contains a detailed end-to-end solution with neural networks in Pytorch. After completing Hands-on Machine Learning with Python, you will be able to implement machine learning and neural network solutions and extend them to your advantage. What You'll Learn Review data structures in NumPy and Pandas Demonstrate machine learning techniques and algorithm Understand supervised learning and unsupervised learning Examine convolutional neural networks and Recurrent neural networks Get acquainted with scikit-learn and PyTorch Predict sequences in recurrent neural networks and long short term memory Who This Book Is For Data scientists, machine learning engineers, and software professionals with basic skills in Python programming.