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Caffe2 Quick Start Guide : Modular and Scalable Deep Learning Made Easy.
Caffe2 by Facebook is a popular and relatively lightweight deep learning framework. Caffe2 is known for speed, accuracy and high efficiency in training neural networks. Caffe2 is widely used in mobile apps. This book is a fast paced guide that will teach you how to train and deploy deep learning mod...
| Clasificación: | Libro Electrónico |
|---|---|
| Autor principal: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Birmingham :
Packt Publishing, Limited,
2019.
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| Temas: | |
| Acceso en línea: | Texto completo |
MARC
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| 100 | 1 | |a Nanjappa, Ashwin. | |
| 245 | 1 | 0 | |a Caffe2 Quick Start Guide : |b Modular and Scalable Deep Learning Made Easy. |
| 260 | |a Birmingham : |b Packt Publishing, Limited, |c 2019. | ||
| 300 | |a 1 online resource (127 pages) | ||
| 336 | |a text |b txt |2 rdacontent | ||
| 337 | |a computer |b c |2 rdamedia | ||
| 338 | |a online resource |b cr |2 rdacarrier | ||
| 588 | 0 | |a Print version record. | |
| 505 | 0 | |a Cover; Title Page; Copyright and Credits; About Packt; Contributors; Table of Contents; Preface; Chapter 1: Introduction and Installation; Introduction to deep learning; AI; ML; Deep learning; Introduction to Caffe2; Caffe2 and PyTorch; Hardware requirements; Software requirements; Building and installing Caffe2; Installing dependencies; Installing acceleration libraries; Building Caffe2; Installing Caffe2; Testing the Caffe2 Python API; Testing the Caffe2 C++ API; Summary; Chapter 2: Composing Networks; Operators; Example -- the MatMul operator; Difference between layers and operators | |
| 505 | 8 | |a Example -- a fully connected operatorBuilding a computation graph; Initializing Caffe2; Composing the model network; Sigmoid operator; Softmax operator; Adding input blobs to the workspace; Running the network; Building a multilayer perceptron neural network; MNIST problem; Building a MNIST MLP network; Initializing global constants; Composing network layers; ReLU layer; Set weights of network layers; Running the network; Summary; Chapter 3: Training Networks; Introduction to training; Components of a neural network; Structure of a neural network; Weights of a neural network; Training process | |
| 505 | 8 | |a Gradient descent variantsLeNet network; Convolution layer; Pooling layer; Training data; Building LeNet; Layer 1 -- Convolution; Layer 2 -- Max-pooling; Layers 3 and 4 -- Convolution and max-pooling; Layers 5 and 6 -- Fully connected and ReLU; Layer 7 and 8 -- Fully connected and Softmax; Training layers; Loss layer; Optimization layers; Accuracy layer; Summary; Chapter 4: Working with Caffe; The relationship between Caffe and Caffe2; Introduction to AlexNet; Building and installing Caffe; Installing Caffe prerequisites; Building Caffe; Caffe model file formats; Prototxt file; Caffemodel file | |
| 505 | 8 | |a Downloading Caffe model filesCaffe2 model file formats; predict_net file; init_net file; Converting a Caffe model to Caffe2; Converting a Caffe2 model to Caffe; Summary; Chapter 5: Working with Other Frameworks; Open Neural Network Exchange; Installing ONNX; ONNX format; ONNX IR; ONNX operators; ONNX in Caffe2; Exporting the Caffe2 model to ONNX; Using the ONNX model in Caffe2; Visualizing the ONNX model; Summary; Chapter 6: Deploying Models to Accelerators for Inference; Inference engines; NVIDIA TensorRT; Installing TensorRT; Using TensorRT | |
| 505 | 8 | |a Importing a pre-trained network or creating a networkBuilding an optimized engine from the network; Inference using execution context of an engine; TensorRT API and usage; Intel OpenVINO; Installing OpenVINO; Model conversion; Model inference; Summary; Chapter 7: Caffe2 at the Edge and in the cloud; Caffe2 at the edge on Raspberry Pi; Raspberry Pi; Installing Raspbian; Building Caffe2 on Raspbian; Caffe2 in the cloud using containers; Installing Docker; Installing nvidia-docker; Running Caffe2 containers; Caffe2 model visualization; Visualization using Caffe2 net_drawer | |
| 500 | |a Visualization using Netron | ||
| 520 | |a Caffe2 by Facebook is a popular and relatively lightweight deep learning framework. Caffe2 is known for speed, accuracy and high efficiency in training neural networks. Caffe2 is widely used in mobile apps. This book is a fast paced guide that will teach you how to train and deploy deep learning models with Caffe2 on resource constrained platforms. | ||
| 590 | |a ProQuest Ebook Central |b Ebook Central Academic Complete | ||
| 650 | 0 | |a Learning. | |
| 650 | 2 | |a Learning | |
| 650 | 6 | |a Apprentissage. | |
| 650 | 7 | |a Artificial intelligence. |2 bicssc | |
| 650 | 7 | |a Pattern recognition. |2 bicssc | |
| 650 | 7 | |a Computer vision. |2 bicssc | |
| 650 | 7 | |a Neural networks & fuzzy systems. |2 bicssc | |
| 650 | 7 | |a Computers |x Intelligence (AI) & Semantics. |2 bisacsh | |
| 650 | 7 | |a Computers |x Computer Vision & Pattern Recognition. |2 bisacsh | |
| 650 | 7 | |a Computers |x Neural Networks. |2 bisacsh | |
| 650 | 7 | |a Learning |2 fast | |
| 776 | 0 | 8 | |i Print version: |a Nanjappa, Ashwin. |t Caffe2 Quick Start Guide : Modular and Scalable Deep Learning Made Easy. |d Birmingham : Packt Publishing, Limited, ©2019 |z 9781789137750 |
| 856 | 4 | 0 | |u https://ebookcentral.uam.elogim.com/lib/uam-ebooks/detail.action?docID=5784231 |z Texto completo |
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