Tag: Neural

Number Systems for Deep Neural Network Architectures


Free Download Number Systems for Deep Neural Network Architectures
English | 2024 | ISBN: 3031381327 | 164 Pages | PDF EPUB (True) | 11.3 MB
This book provides readers a comprehensive introduction to alternative number systems for more efficient representations of Deep Neural Network (DNN) data. Various number systems (conventional/unconventional) exploited for DNNs are discussed, including Floating Point (FP), Fixed Point (FXP), Logarithmic Number System (LNS), Residue Number System (RNS), Block Floating Point Number System (BFP), Dynamic Fixed-Point Number System (DFXP) and Posit Number System (PNS). The authors explore the impact of these number systems on the performance and hardware design of DNNs, highlighting the challenges associated with each number system and various solutions that are proposed for addressing them.

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AI Workshop – Build a Neural Network with PyTorch Lightning


Free Download AI Workshop – Build a Neural Network with PyTorch Lightning
Released 12/2023
MP4 | Video: h264, 1280×720 | Audio: AAC, 44.1 KHz, 2 Ch
Skill Level: Intermediate | Genre: eLearning | Language: English + srt | Duration: 1h 30m | Size: 176 MB
If you’re looking for hands-on AI practice, this workshop-style coding course was designed for you. Join instructor Janani Ravi as she shows you how to build a neural network with PyTorch Lightning, the open-source library from Python that provides an interface for the popular deep learning framework PyTorch. Explore the core components of building a neural network with PyTorch, including setting up the virtual environment, loading and exploring data, preprocessing data for training, creating and training a simple neural network, setting up the Dataset and DataLoader, visualizing losses, and much more. Along the way, Janani covers the basics of using modules in PyTorch Lightning to build, train, and evaluate both regression and classification models.

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