In this video, deeplizard will see how we can experiment with large numbers of hyperparameter values easily while still keeping the training loop and results organized.
Here’s a great article on R-CNN, object detection, and the ins and outs of computer vision.
After exploring CNN for a while, I decided to try another crucial area in Computer Vision, object detection. There are several methods popular in this area, including Faster R-CNN, RetinaNet, YOLOv3, SSD and etc. I tried Faster R-CNN in this article. Here, I want to summarise what I have learned and maybe give you a little inspiration if you are interested in this topic.
In this video from deeplizard, learn how to build, plot, and interpret a confusion matrix using PyTorch. They also cover about locally disabling PyTorch gradient tracking or computational graph generation.
In this episode from deeplizard, learn how to build the training loop for a convolutional neural network using Python and PyTorch.
Here’s an interesting tutorial on prepping image data for CNNs.
It is challenging to know how to best prepare image data when training a convolutional neural network. This involves both scaling the pixel values and use of augmentation techniques during both the training and evaluation of the model. Instead of testing a wide range of options, a useful shortcut […]
In this video, deeplizard shows how to use a convolutional neural network (CNN) to generate an output prediction tensor from a sample image of a dataset. The associated blog post has a more in-depth tutorial.
Here’s a great video on how to implement the forward method for a convolutional neural network (CNN) in PyTorch.
Here’s an interesting article in Nature about the use of AI in evaluating embryos with AI — another use of computer vision in the medical field. Could this bring down healthcare costs? What if the algorithm mislabels an embryo? Are there ethical implications?
Deep learning algorithms, in particular convolutional neural networks (CNNs), have recently been used to address a number of medical-imaging problems, such as detection of diabetic retinopathy,18 skin lesions,19 and diagnosing disease.20 They have become the technique of choice in computer vision and they are the most successful type of models for image analysis. Unlike regular neural networks, CNNs contain neurons arranged in three dimensions (i.e., width, height, depth). Recently, deep architectures of CNNs such as Inception21 and ResNet22 have dramatically increased the progress rate of deep learning methods in image classification.23 In this paper, we sought to use deep learning to accurately predict the quality of human blastocysts and help select the best single embryo for transfer (Fig. 1).
In this video, Two Minute Papers examines the paper “DeepFocus: Learned Image Synthesis for Computational Displays” from Facebook Research. Having a neural network synthesize depth and focus information from a two dimensional input can make more content usable for virtual reality or mixed reality uses.
Here’s a great video explaining Convolutional Neural Networks (CNNs), a type of neural network used in computer vision scenarios.
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