Computer Vision Using Deep Learning

Neural Network Architectures with Python and Keras

de

Éditeur :

Apress


Paru le : 2021-02-15



eBook Téléchargement , DRM LCP 🛈 DRM Adobe 🛈
Lecture en ligne (streaming)
56,19

Téléchargement immédiat
Dès validation de votre commande
Ajouter à ma liste d'envies
Image Louise Reader présentation

Louise Reader

Lisez ce titre sur l'application Louise Reader.

Description

Organizations spend huge resources in developing software that can perform the way a human does. Image classification, object detection and tracking, pose estimation, facial recognition, and sentiment estimation all play a major role in solving computer vision problems. 
This book will bring into focus these and other deep learning architectures and techniques to help you create solutions using Keras and the TensorFlow library. You'll also review mutliple neural network architectures, including LeNet, AlexNet, VGG, Inception, R-CNN, Fast R-CNN, Faster R-CNN, Mask R-CNN, YOLO, and SqueezeNet and see how they work alongside Python code via best practices, tips, tricks, shortcuts, and pitfalls. All code snippets will be broken down and discussed thoroughly so you can implement the same principles in your respective environments.

Computer Vision Using Deep Learning offers a comprehensive yet succinct guide that stitches DL and CV together to automate operations, reduce human intervention, increase capability, and cut the costs. 

What You'll Learn

Examine deep learning code and concepts to apply guiding principals to your own projectsClassify and evaluate various architectures to better understand your options in various use casesGo behind the scenes of basic deep learning functions to find out how they work

Who This Book Is For
Professional practitioners working in the fields of software engineering and data science. A working knowledge of Python is strongly recommended. Students and innovators working on advanced degrees in areas related to computer vision and Deep Learning.
Pages
308 pages
Collection
n.c
Parution
2021-02-15
Marque
Apress
EAN papier
9781484266151
EAN PDF
9781484266168

Informations sur l'ebook
Nombre pages copiables
3
Nombre pages imprimables
30
Taille du fichier
10138 Ko
Prix
56,19 €
EAN EPUB
9781484266168

Informations sur l'ebook
Nombre pages copiables
3
Nombre pages imprimables
30
Taille du fichier
16118 Ko
Prix
56,19 €

Suggestions personnalisées