XuShuai

1 - object localization In order to build up the object detection, we first learn about object localization. image classification: the algorithm look at the picture and responsible for saying ...

1 - Computer vision Computer vision problem: images recognition object detection style transfer One of the challenges of the computer vision problem is that input can get really big. Th...

Course1 Week2-foundation of neural network Week3-one hidden layer neural network Week4-deep neural network Course2 Week1-setting up your ML application ...

1 - carrying out error analysis If the learning algorithm is not yet at the performance of a human, then manually examiming mistakes that the algorithm is making can give us a insight into what to do...

introduction to ML strategy 1 - why ML strategy? How to structure machine learning project, that is on the machine learning strategy. What is machine learning strategy, let’s say we are working ...

hyperparameter tuning 1 - tuning process How to systematically organize hyperparameters tuning process? hyperparameters learning rate αα\alpha ββ\beta in momentum, or set the default 0.9 mini-b...

optimization algorithms 1 - mini-batch gradient descent vectorization allows you to efficiently compute on m examples.But if m is large then it can be very slow. With the implement of graident des...

setting up your ML application 1 - train/dev/test set This week we’ll learn the partical aspects of how to make your neural network work well, ranging from things like hyperparameters tuning to ho...

4.1 - deep L-layer neural network We have seen forward propagation and backward propagation in the context of a neural network with a single hidden layer as well we the logistic regression, and we le...

3.1 - neural networks overview Some new notation have been introduce, we’ll use superscript square bracket 1 to refer to the layer of neural network, for instance, w[1]w[1]w^{[1]} representing the pa...

Week 2 Basics of Neural Network Programming 2.1 binary classification mmm training example: (x(1),y(1)),⋯,(x(m),y(m))(x(1),y(1)),⋯,(x(m),y(m))(x^{(1)}, y^{(1)}), \cdots, (x^{(m)}, y^{(m)}) X=⎡...

1 - what is face recognition? This week will show you a couple important special applications of CONVnet, we will start with face recognition and then go on to neural style transfer. Verification:...

case studies 1 - why look at cases studies? how the together the basic building block, such as CONV layer, POOL layer, FC layer, to form effective convolutinal neural network? outline: classic ...