种子简介
种子名称：
Java Machine Learning for Computer Vision [Video]
文件类型：
视频
文件数目：
38个文件
文件大小：
746.47 MB
收录时间：
2019-1-10 19:41
已经下载：
3次
资源热度：
217
最近下载：
2025-4-14 06:46
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种子包含的文件
Java Machine Learning for Computer Vision [Video].torrent
4.Real Time Object Detection/28.Build a Java Real Time Video Car and Pedestrians Detection Application.mp483.67MB
1.Introduction to Computer Vision and Training Neural Networks/02.Computer Vision State.mp49.9MB
1.Introduction to Computer Vision and Training Neural Networks/03.Exploring Neural Network.mp413.01MB
1.Introduction to Computer Vision and Training Neural Networks/04.How Is Neural Network Learning.mp426.61MB
1.Introduction to Computer Vision and Training Neural Networks/05.Organizing Your Data and Application.mp413.61MB
1.Introduction to Computer Vision and Training Neural Networks/06.Effective Training Techniques.mp416.94MB
1.Introduction to Computer Vision and Training Neural Networks/07.Optimization Algorithms.mp421.83MB
1.Introduction to Computer Vision and Training Neural Networks/08.Neural Network Training Parameters.mp415.37MB
1.Introduction to Computer Vision and Training Neural Networks/09.Images and Outputs Representations.mp419.97MB
1.Introduction to Computer Vision and Training Neural Networks/10.Build a Handwritten Digit Recognizer with 97% Accuracy.mp434.3MB
2.Convolution Neural Network Architectures/11.Understanding Edge Detection.mp418.36MB
2.Convolution Neural Network Architectures/12.Java Edge Detection Application.mp434.33MB
2.Convolution Neural Network Architectures/13.Convolution on Colored RGB Images.mp48.91MB
2.Convolution Neural Network Architectures/14.Working with Convolutional Layers Parameters.mp410.86MB
2.Convolution Neural Network Architectures/15.Pooling Layers.mp410.11MB
2.Convolution Neural Network Architectures/16.Building and Training Convolutional Neural Network.mp412.17MB
2.Convolution Neural Network Architectures/17.Improve Handwritten Digit Recognition Application (With 99.95% Accuracy).mp431.03MB
3.Transfer Learning and Deep CNN Architectures/18.Working with Classical Networks.mp419.11MB
3.Transfer Learning and Deep CNN Architectures/19.Using Residual Networks for Image Recognition.mp411.75MB
3.Transfer Learning and Deep CNN Architectures/20.The Power of 1x1 Convolution and Inception Network.mp414.57MB
3.Transfer Learning and Deep CNN Architectures/21.Applying Transfer Learning.mp414.84MB
3.Transfer Learning and Deep CNN Architectures/22.Building Animal Image Classification (Using Transfer Learning and VGG-16 Architecture).mp437.56MB
4.Real Time Object Detection/23.Resolving Object Localization Problem.mp416.31MB
4.Real Time Object Detection/24.Object Detection with Sliding Window Solution.mp415.19MB
4.Real Time Object Detection/25.Convolution Sliding Window.mp420.09MB
4.Real Time Object Detection/26.Detecting Objects with YOLO Algorithm.mp423.11MB
4.Real Time Object Detection/27.Max Suppression and Anchor Boxes.mp416.06MB
1.Introduction to Computer Vision and Training Neural Networks/01.The Course Overview.mp415.5MB
5.Creating Art with Neural Style Transfer/29.What Are Convolution Network Layers Learning.mp410.72MB
5.Creating Art with Neural Style Transfer/30.Neural Style Transfer.mp412.06MB
5.Creating Art with Neural Style Transfer/31.Applying Content Cost Function.mp49.81MB
5.Creating Art with Neural Style Transfer/32.Applying Style Cost Function.mp420.82MB
5.Creating Art with Neural Style Transfer/33.Build a Neural Network Which Produces Art.mp437.78MB
6.Face Recognition/34.Problems in Face Detection.mp412.04MB
6.Face Recognition/35.Differentiating Inputs with Siamese Networks.mp46.95MB
6.Face Recognition/36.Exploring Triplet Loss.mp415.15MB
6.Face Recognition/37.Binary Classification.mp46.68MB
6.Face Recognition/38.Build Face Recognition Java Application.mp429.41MB
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