1- Deep Learning for Computer Vision
We will discuss the following: some application in computer vision
SlideshareAdvanced Topics in Computer Science 2
Welcome my students, I hope to enjoy learning our course.
This course is a computer vision and deep dive into details of the deep learning architectures with a focus on learning end-to-end models for these tasks, particularly image classification and detection.
Lectures
2- Artificial Neural Network
We will discuss the following: Artificial Neural Network, Perceptron Learning Example, Artificial Neural Network Training Process, Forward propagation, Backpropagation, Classification of Handwritten Digits, Neural Network Zoo.
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3- Convolutional Neural Networks
We will discuss the following: Deep vs Machine Learning, Superstar Researchers, Superstar Companies, Deep Learning, Deep Learning Requirements, Deep Learning Architectures, Convolution Neural Network, Case studies, LeNet,AlexNet, ZFNet, GoogLeNet, VGGNet, ResNet, ILSVRC, MNIST, CIFAR-10, CNN Optimization , NVIDIA TITAN X.
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4- How it Works: Convolutional Neural Networks
We will discuss the following: Filtering, Convolution, Convolution layer, Normalization, Rectified Linear Units, Pooling, Pooling layer, ReLU layer, Deep stacking, Fully connected layer.
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5- Deep Learning Frameworks
We will discuss the following: Deep Learning Frameworks, TensorFlow, Keras, TFlearn, Katacoda.
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6- Convolutional Neural Network Models
We will discuss the following: Convolutional Neural Network, AlexNet, ZFNet, VGGNet, GoogleNet, ResNet, DenseNet.
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7- Deep Learning Applications
We will discuss the following: Deep Learning Applications, Image Processing, Video Processing, Medical, Computer Network, Big Data, Bioinformatics.
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Contact Me
Any Problem send me e-mail to mloey@live.com
Benha University: http://bu.edu.eg/staff/mloey