PyTorch recipes : a problem-solution approach / Pradeepta Mishra
نوع المادة : نصاللغة: الإنجليزية الناشر:California: Apress, 2019تاريخ حقوق النشر: ©2019وصف:xx, 184 pages : illustration ; 25 cmنوع المحتوى:- text
- unmediated
- volume
- 1484242580
- 9781484242582
- 9781484242575
- QA76.87 .M574 2019
نوع المادة | المكتبة الحالية | رقم الطلب | رقم النسخة | حالة | تاريخ الإستحقاق | الباركود | |
---|---|---|---|---|---|---|---|
كتاب | UAE Federation Library | مكتبة اتحاد الإمارات General Collection | المجموعات العامة | QA76.87 .M574 2019 (إستعراض الرف(يفتح أدناه)) | C.1 | Library Use Only | داخل المكتبة فقط | 30030000005416 | ||
كتاب | UAE Federation Library | مكتبة اتحاد الإمارات General Collection | المجموعات العامة | QA76.87 .M574 2019 (إستعراض الرف(يفتح أدناه)) | C.2 | المتاح | 30030000005417 |
Browsing UAE Federation Library | مكتبة اتحاد الإمارات shelves, Shelving location: General Collection | المجموعات العامة إغلاق مستعرض الرف(يخفي مستعرض الرف)
QA76.87 .L587 2019 Artificial neural networks with Java : tools for building neural network applications / | QA76.87 M39 1996 Mathematical perspectives on neural networks / | QA76.87 M39 1996 Mathematical perspectives on neural networks / | QA76.87 .M574 2019 PyTorch recipes : a problem-solution approach / | QA76.87 .M574 2019 PyTorch recipes : a problem-solution approach / | QA76.87 M85 1991 Neural networks : an introduction [multimedia] / | QA76.87 .S97 2011 System and circuit design for biologically-inspired intelligent learning / |
Includes index
Introduction to PyTorch, Tensors, and Tensor operations -- Probability distributions using PyTorch -- CNN and RNN using PyTorch -- Introduction to neural networks using PyTorch -- Supervised learning using PyTorch -- Fine-tuning deep learning models using PyTorch -- Natural language processing using PyTorch
Get up to speed with the deep learning concepts of Pytorch using a problem-solution approach. Starting with an introduction to PyTorch, you'll get familiarized with tensors, a type of data structure used to calculate arithmetic operations and also learn how they operate. You will then take a look at probability distributions using PyTorch and get acquainted with its concepts. Further you will dive into transformations and graph computations with PyTorch. Along the way you will take a look at common issues faced with neural network implementation and tensor differentiation, and get the best solutions for them. Moving on to algorithms; you will learn how PyTorch works with supervised and unsupervised algorithms. You will see how convolutional neural networks, deep neural networks, and recurrent neural networks work using PyTorch. In conclusion you will get acquainted with natural language processing and text processing using PyTorch. You will: Master tensor operations for dynamic graph-based calculations using PyTorch Create PyTorch transformations and graph computations for neural networks Carry out supervised and unsupervised learning using PyTorch Work with deep learning algorithms such as CNN and RNN Build LSTM models in PyTorch Use PyTorch for text processing