Here, you can find information about Computer Vision, Deep Learning, Machine Learning and Artificial Intelligence. Rest assured that your time will not be wasted.
Mathematics from scratch was something which I couldn't find anywhere else. Thank you for making this awesome video ❤. But I didn't understand the following . 1. reparameterisation part 2. How the sum of normals were calculated
Nice explanation. you helped me to solve the calibration problem in a grid detection problem. In opencv, there are methods called ".getPerspectiveTransform" and "warpPerspective". If you know the math behind, two lines of codes solve the problem.
very good explanation, Could you please make a video on vmamba or Vision mamba to understand it in depth , like how selective scan 2d works etc , looking forward
Thanks! I was experimenting with IFS fractals 30+ years ago. Did not remember much and google was no help. Everyone is just listing the basic known and nobody else explains the math to make your own.
Yorumunuz için teşekkür ederim. Kanalım için yaptığım çalışmalar özelinde değil de daha geniş manasıyla bakacak olursak, hayatın bana öğrettiği şeylerden biri de her çabanın her fiilin bir karşılığı olduğu. Bazen hemen olur, bazen zaman alır. Bazen direkt olur, bazen dolaylı yollardan.
Thank You. I think YOLOv1, YOLOv2 and YOLOv3 are important to understand how to address object detection in single pass formulating it as a regression problem.
It is really a very simple and understandable series. The series is easy to understand and follow. It would be great if you could include courses on OpenCV, advanced computer vision, and Kaggle project solutions. Thank you for all your hard work.
Those are two different examples. In the first one, at 09:22 of the video, an RGB image is convolved with a 3×3 filter. Since RGB image has 3 channels, convolution filter should also have 3 channels. This is a typical filtering operation in an image processing application. The second example, at 12:08 of the video, is more generic, a convolution operation at a convolutional layer is illustrated. That's why, in the video, it's written "Let our input image depth be 32".
Very Informative Video. I see that you have covered various topics like mathematics of transformation, supervised learning etc. in your various videos. If you create playlists, it would be easier for the viewers.
I congratulate your Digital Image Processing videos. When commercial products are everywhere , then detailed and explanatory videos are easily accessible datum. Thank you so much.
This is amazing, please do more of these, camera calibration also with example and from there what and can be achieved using the calibration like solving parallax problem, estimating object distance etc. With you kind of slow and steady explanation everyone will be able to understand.
I do not fully understand how jt works honestly. Given a batch, the output of that hidden layer should be dimension_batch* dimension _output? It follows that mean / variance shouldn't be vectors?
Hi, batch normalization can be confusing at first glance. Never mind. Let's say we have a fully connected layer with n neurons. If batch size is m, then each neuron outputs m values for 1 batch of inputs. Mean and variance for that neuron for that batch are computed using those m outputs as described in 09:01 of the video. So mean and variance are scalars and are computed for each batch during training. And one important thing to note is that while computing mean and variance for 1 neuron, only outputs of that neuron are used.
This is a good video. I am currently searching on implementation of deconvolution using tensorflow. Did you use tensorflow for your implementation? If so, can you share the code?
When I first started my channel, I prepared some videos without voiceover. But, I can assure you, those videos, too, include all necessary information and detail as text, diagrams and images to understand the related concepts.
Thanks a lot! Was facing difficulty in understanding how mini-batch standard deviation helps prevent mode collapse until I saw this video! Really appreciate it! Great work!
Reduction ratio r is used to create a bottleneck. This way, network is forced to learn which channels are important. Then unimportant channels are suppressed scaling them with modulation weights.
