cs231n | Exercises from Stanford 's CS231n class

Hi I am going through the book https://nnfs.io/ but using JuliaLang (it's a self-challenge to get to know the language better and use it more often.. rather than doing the same old same in Python..)

I have come across a part of the book in which they have custom wrote some function and I need to recreate it in JuliaLang...

source: https://cs231n.github.io/neural-networks-case-study/

python

I am attempting to translate some Python code into Julia.. but having trouble looking for an example where I have a range that I can then apply to a Matrix or vector to index upon... here's my code

source: https://cs231n.github.io/neural-networks-case-study/

python

I'm trying to convert a trained sign language classification solution in python to a C language headers so that I can deploy on a M4-cortex CPU board. In Python, I'm able to build model and train it and I can see it predicting with 90% accuracy. But I see an issue with number of weights used/generated in convolution layers

I am trying to code a ResNet CNN architecture based on the paper by using Python3, TensorFlow2 and CIFAR-10 dataset. You can access the Jupyter notebook here.

During training the model using "model.fit()", after just one epoch of training, I get the following error:

ValueError: Input 0 is incompatible with layer model: expected shape=(None, 32, 32, 3), found shape=(32, 32, 3)

The training images are batched using batch_size = 128, hence the training loop gives the following 4-d tensor which TF Conv2D expects- (128, 32, 32, 3).

What's the source of this error?

This might come across as a seriously newbie question, but I have not many options as I am not sure which direction I should be heading.

Now I am studying Deep Learning frequently and I want to toy around with Stanford's CS231N's Convolutional Neural Network Demo as I find it extremely user friendly. The visuals are embedded in this website. I really want to toy around with this but I do not know how and where to start.

I have knowledge of Python and VS-Code if that helps.

I'm trying to do it by myself the assignments from Stanford CS231n 2017 CNN course.

I'm trying to compute L2 distance using only matrix multiplication and sum broadcasting with Numpy. L2 distance is:

And I think I can do it if I use this formula:

The following code shows three methods to compute L2 distance. If I compare the output from the method compute_distances_two_loops with the output from method compute_distances_one_loop, both are equals. But I compare the output from the method compute_distances_two_loops with the output from the method compute_distances_no_loops, where I have implemented the L2 distance using only matrix multiplication and sum broadcasting, they are different.

I have decided to complete cs231 course and do its assignment. I happily watched the first 2 videos of the course and now I had to solve the first assignments. I followed the guidelines step by step which was shown in the video in this link: https://cs231n.github.io/setup-instructions/ Then, when I run the first cell, which is not the cell shown in the video but nonetheless it's in the assignments1 file which I downloaded from their site, I get a nasty error which has paralyzed me four a couple of hours. I'd be happy if anyone could respond. IF you take a look at my picture, you'll see that files are added in the google drive, but surprisingly, it gives an error out of nowhere. Thanks.

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Update: Here is the snapshot of the video provided to guide students how to setup their google colab (in that link).

As you can see, in their vide the first chunk of code specifies their working directory but in the file that they have uploaded as their assignment1, they have not done so!

In the following code written by Karpathy, why do we have this line(Why do we need to compare with the uniform distribution to select an action while the policy function did that)

I'm going through CS231n to understand the basics of neural networks.

Attached is the slide in which Justin (the tutor) gives the reasoning for why data preprocessing is required and I don't completely understand. The explanation given is similar to the one given on the slide and I don't get it. The slide is below.

The second question I have is: is it actually normalisation or standardisation? This link implies that it is standardisation, whereas the course material says it is normalisation.

Any help will be appreciated.