--- product_id: 99451326 title: "Natural Language Processing with PyTorch: Build Intelligent Language Applications Using Deep Learning" price: "€ 96.54" currency: EUR in_stock: true reviews_count: 13 url: https://www.desertcart.at/products/99451326-natural-language-processing-with-pytorch-build-intelligent-language-applications-using store_origin: AT region: Austria --- # Natural Language Processing with PyTorch: Build Intelligent Language Applications Using Deep Learning **Price:** € 96.54 **Availability:** ✅ In Stock ## Quick Answers - **What is this?** Natural Language Processing with PyTorch: Build Intelligent Language Applications Using Deep Learning - **How much does it cost?** € 96.54 with free shipping - **Is it available?** Yes, in stock and ready to ship - **Where can I buy it?** [www.desertcart.at](https://www.desertcart.at/products/99451326-natural-language-processing-with-pytorch-build-intelligent-language-applications-using) ## Best For - Customers looking for quality international products ## Why This Product - Free international shipping included - Worldwide delivery with tracking - 15-day hassle-free returns ## Description Natural Language Processing (NLP) provides boundless opportunities for solving problems in artificial intelligence, making products such as desertcart Alexa and Google Translate possible. If youâ??re a developer or data scientist new to NLP and deep learning, this practical guide shows you how to apply these methods using PyTorch, a Python-based deep learning library. Authors Delip Rao and Brian McMahon provide you with a solid grounding in NLP and deep learning algorithms and demonstrate how to use PyTorch to build applications involving rich representations of text specific to the problems you face. Each chapter includes several code examples and illustrations. Explore computational graphs and the supervised learning paradigm Master the basics of the PyTorch optimized tensor manipulation library Get an overview of traditional NLP concepts and methods Learn the basic ideas involved in building neural networks Use embeddings to represent words, sentences, documents, and other features Explore sequence prediction and generate sequence-to-sequence models Learn design patterns for building production NLP systems Review: Great for beginners in NLP and deep learning frameworks - I am new to NLP and deep learning frameworks, though I have advanced experience in structured data machine learning and basic experience coding my own baby neural nets from scratch in python. I was struggling to find a way into a deep learning framework like tensorflow or pytorch that would bridge the gap between my desire to take a particular problem formulation (inputs, activation functions, layers, output, loss function) and code it in a framework using best practice design patterns. Most framework tutorials I found start out more like a magic black box, and that's not the way I like to learn given my background. This book teaches NLP basics from the ground up along with a strong design pattern coded in python/pytorch. It teaches it seamlessly by starting from a simple example and continuing with other more advanced examples that keep using the same design pattern over and over again. For me, this is the best way to learn and remember. It has given me a foundation on how to sit down and code my own solution in an organized fashion using proper python object oriented practices. My biggest challenge moving from structured data problems to NLP is knowing how to represent the text input to a model and then feeling comfortable writing maintainable code that prepares the data according to that representation. The authors step you through this, and also even offer the code that munges the data into shape for each example, which I found helpful for rounding out my text-based python skills. I feel very confident I can build my own library or dive into other PyTorch/Fastai models knowing what is going on and what to look out for. So many helpful modeling and PyTorch details are given along the way. I'm truly grateful for this book to get me over the hump in the NLP space. Review: Solid for beginners - Pretty good introduction book for NLP newcomers like me. It kinda goes all the way from the beginning (perceptron and basic pytorch) so if you have never been exposed to NLP this is the right book for you ## Technical Specifications | Specification | Value | |---------------|-------| | Best Sellers Rank | #485,558 in Books ( See Top 100 in Books ) #51 in Data Mining (Books) #77 in Data Processing #87 in Natural Language Processing (Books) | | Customer Reviews | 4.1 out of 5 stars 68 Reviews | ## Images ![Natural Language Processing with PyTorch: Build Intelligent Language Applications Using Deep Learning - Image 1](https://m.media-amazon.com/images/I/91Tzd4q9DhL.jpg) ## Customer Reviews ### ⭐⭐⭐⭐⭐ Great for beginners in NLP and deep learning frameworks *by M***E on March 12, 2019* I am new to NLP and deep learning frameworks, though I have advanced experience in structured data machine learning and basic experience coding my own baby neural nets from scratch in python. I was struggling to find a way into a deep learning framework like tensorflow or pytorch that would bridge the gap between my desire to take a particular problem formulation (inputs, activation functions, layers, output, loss function) and code it in a framework using best practice design patterns. Most framework tutorials I found start out more like a magic black box, and that's not the way I like to learn given my background. This book teaches NLP basics from the ground up along with a strong design pattern coded in python/pytorch. It teaches it seamlessly by starting from a simple example and continuing with other more advanced examples that keep using the same design pattern over and over again. For me, this is the best way to learn and remember. It has given me a foundation on how to sit down and code my own solution in an organized fashion using proper python object oriented practices. My biggest challenge moving from structured data problems to NLP is knowing how to represent the text input to a model and then feeling comfortable writing maintainable code that prepares the data according to that representation. The authors step you through this, and also even offer the code that munges the data into shape for each example, which I found helpful for rounding out my text-based python skills. I feel very confident I can build my own library or dive into other PyTorch/Fastai models knowing what is going on and what to look out for. So many helpful modeling and PyTorch details are given along the way. I'm truly grateful for this book to get me over the hump in the NLP space. ### ⭐⭐⭐⭐ Solid for beginners *by Y***G on July 11, 2020* Pretty good introduction book for NLP newcomers like me. It kinda goes all the way from the beginning (perceptron and basic pytorch) so if you have never been exposed to NLP this is the right book for you ### ⭐⭐⭐⭐⭐ great book *by E***D on February 22, 2026* great book ## Frequently Bought Together - Natural Language Processing with PyTorch: Build Intelligent Language Applications Using Deep Learning - Natural Language Processing with Transformers, Revised Edition - Deep Learning for Coders with fastai and PyTorch: AI Applications Without a PhD --- ## Why Shop on Desertcart? - 🛒 **Trusted by 1.3+ Million Shoppers** — Serving international shoppers since 2016 - 🌍 **Shop Globally** — Access 737+ million products across 21 categories - 💰 **No Hidden Fees** — All customs, duties, and taxes included in the price - 🔄 **15-Day Free Returns** — Hassle-free returns (30 days for PRO members) - 🔒 **Secure Payments** — Trusted payment options with buyer protection - ⭐ **TrustPilot Rated 4.5/5** — Based on 8,000+ happy customer reviews **Shop now:** [https://www.desertcart.at/products/99451326-natural-language-processing-with-pytorch-build-intelligent-language-applications-using](https://www.desertcart.at/products/99451326-natural-language-processing-with-pytorch-build-intelligent-language-applications-using) --- *Product available on Desertcart Austria* *Store origin: AT* *Last updated: 2026-06-03*