DataBloom

I recently started neural networks and wanted to use my pc for making some small models. I thought that being a recent GPU it must be having cuda cores so I installed all the required softwares like cuda toolkit and cuda. After installation I tried to see if it detected my GPU and failed. Then I searched on YouTube and came to the CUDA GPUs list by Nvidia. It has all the GPU around mine like 1650ti and 2060 but no 1660ti. Is there a specific reason for it or does it actually have cuda cores.

Hi Guys, which Cloud platform should I pay for/use in order to perform deep learning experiments? Google Colab Pro doesn’t meet my requirements since it doesn’t let you use the service for more than 24 hours in one go. My experiments might run for approximately 4 days, give or take.

Thanks!

Okay, I feel like this is such a dumb question but please please help me. I’m a high school student and I really really want to do this my Maths written assessment on human pose estimation because I want to analyze/accurately compare my dance covers to the original dancer’s performance. I got my inspiration from this video: https://www.youtube.com/watch?v=bPDoMdn71h0&t=0s

I’m really keen to do this and explain the mathematics behind this because I have such a deep interest in it but I honestly don’t know where to start!

Like what should I do? Vectors? Matrices? Gradients? Geometry? Trig? Please help!!!!

Hi everyone, I have a question posted on stackoverflow but wanted to share it here too in case someone can help:

My question is related with how keras implements the weights constraints, in particular in dense layers. I am using mainly R and its keras implementation, so I will use here their notation, but I will be also happy if someone has a solution in Python. As far as I know, keras constraints like constraint_maxnorm() can be applied to a layer_dense() with the arguments kernel_constraint or bias_constraint, where the kernel refers to the weigth matrix without the bias terms. However, in order to test new approaches I think it would be useful to also be able to apply constraints to the full weight matrix, where the biases vector forms an extra row in the weigth matrix. An example of this kind of constraint would be to restrict to 1 the L1 norm of the weight vectors incident to a neuron (considering the bias at that neuron also as an element of said vector).

In this sense, custom constraints can be created but the problem is that they receive the kernel weights as input, not both the kernel weights and bias, and the output is also only the kernel weights, not the full weight matrix.

Is it possible to implement this kind of constraints in keras or tensorflow that affect both the kernel and the bias at the same time? In this sense, it would be enough to be able to create custom constraints that accept both the kernel weights and the bias as input, and then apply the desired constraint twice, at the kernel and at the bias.

Will the tensorflow developer certificate help me get an internship?

I need to detect the Big Dipper constelation on photos of night sky. Is Tensorflow good choice for this kind of project? How can I start with it?