Convolutional Neural Networks

[N] Andrew Ng's Deep learning specialization's 4th course is up now on Coursera which is on CNNs

You may be able to find some blog somewhere that does it, but that's likely a topic for a proper class on deep learning as it is very math heavy.

You can find one introductory-level online class on this topic on Coursera's Deep Learning by Andrew Ng. Here, you don't do most of the formal derivations for convolutional backprop, but you do fully implement a convnet in Python only using Numpy in a notebook: https://www.coursera.org/learn/convolutional-neural-networks

I also like the videos here: https://www.coursera.org/learn/convolutional-neural-networks/home/week/4

Yes there has been significant work in this area, most of the face recognition application like Apple's face ID system uses one shot learning. If you think about it Apple is not training the Face recognition algorithm on your face but even when you buy an IPhoneX you are able to achieve amazing accuracy (actually they claim the error to 1/1,000,000 which means there is a possibility of failing once in every 1 million tests) how are they doing it? They use something called Siamese network, these networks once trained can take two images as input and output how far are they from each other (basically means if they are same or not). So once you train one such model your model will learn the weights to find the similarity between a given pair of images and with enough data and training this model can accurately differentiate if two images are same or not. Going back to my IPhoneX example, when you login into your phone for the first time it asks you to register your face in different orientation and after that anytime anyone tries to log in it uses the pertained Siamese network to see if you are the same person or not. So technically the algorithm was able to find out the similarity with just one shot of learning. For some reference on the same please look at this video : https://www.coursera.org/learn/convolutional-neural-networks/lecture/gjckG/one-shot-learning

Recommending Andrew Ng courses is par for the course on this sub, but here's a specific video

This one: https://www.coursera.org/learn/convolutional-neural-networks

Anything Andrew Ng produces. You could probably do this part of the deeplearning specialisation in a week. https://www.coursera.org/learn/convolutional-neural-networks?specialization=deep-learning Or you could just watch the videos to get a conceptual understanding.

After Andrew's ML course try a course on neural nets, or a specialised course on convolutional neural nets. There's such a course from Andrew as well.

For CNNs, Andrew Ng's course on Coursera is a good starting point.

Yolo and yolo9000 class predictions [xpost r/MachineLearning]

I am trying to understand yolo (https://arxiv.org/abs/1506.02640) and have some confusion over the final class predictions. The original paper and presentation by the author (https://www.youtube.com/watch?v=NM6lrxy0bxs) says "We only predict one set of class probabilities per grid cell, regardless of the number of boxes B." However Andrew Ng's CNN course (https://www.coursera.org/learn/convolutional-neural-networks/lecture/fF3O0/yolo-algorithm) and Siraj Raval's video (https://www.youtube.com/watch?v=4eIBisqx9_g) say we predict a set of class probabilities for each bounding box. Has anyone come across this? Why the disparity? Which approach is actually used/which is better?

>The only type of labeled training images that I've seen is when the entire image is labeled with an entity (ex: "dog", or "cat").

In the domain of machine learning, this is called "classification".

Detection could be seen as predicting bounding boxes + classification of the corresponding cropped areas. You can imagine an algorithm that takes every possible cropped areas of an image and feed them to a classifier. In theory, it would work, but practically would be way to long in processing time. Some of the first detection neural nets (like RCNN) had a module whose job was to propose areas/regions to the classifier, and thus limited the processing time.

YOLO is even faster by doing both tasks in a single convolution neural network. I think it is interesting for you to know how YOLO works, but the subject is too long to be described here. May I suggest to you to watch the videos from https://www.coursera.org/learn/convolutional-neural-networks (week 3) ? I think you have to subscribe to access the videos, but it is free. And it is very well explained, I think.

I've taken Andrew Ng's Coursera material on convolutional neural networks, which covers a lot more than just basic CNN theory. It's REALLY good: https://www.coursera.org/learn/convolutional-neural-networks/

Yeah! What you want is the YOLO algorithm. It's state of the art for multiclass object detection. https://www.coursera.org/learn/convolutional-neural-networks/

It is a bit hard to recommend a course of study without knowing your language of choice or your background but I would recommend the deep learning course on Coursera as a starting point: https://www.coursera.org/specializations/deep-learning

or specifically course 4 if you are just interested in CNNs and don't have much time: https://www.coursera.org/learn/convolutional-neural-networks

For a very high level overview of different deep learning architectures and approaches, this is a nice summary:

https://medium.com/comet-app/review-of-deep-learning-algorithms-for-object-detection-c1f3d437b852

It is a bit dated in that YOLOv3 (Darknet53) has been released and it doesn't mention anything on Retinanet.

A common trade-off you will hear is between accuracy and speed. I would urge you to consider the real implications of these results to your project as they tend to be competitive for mostly academic purposes.