deeplearning.ai | Adrew Ng 's coursera course | Machine Learning library

My code is very basic, just following the first lab from DeepLearning.ai's GAN specialization. However, my code does not have the same output, what is the reason for this. Sorry if this is just a silly mistake, this is my first experience with GANs. I begin by creating the Generator and Discriminator classes, my random noise function, and creating my models. I then run the training loop, but after 3 epochs, all of the outputs from the GAN are black.

I'm following Andrew Ng's video1 video2 on edge detection and I'm trying to detect if a filter exists in an image.
So if I have 2 images and 2 filters (1 that detects horizontal lines and 1 that detects vertical lines):

I think there is a problem with my cudatoolkit version ie. 10.0.130. I don't understand this error message. I want to use Gradcam (heatmap generator) on the x-ray image. It is the same code from AI for Medicine by deeplearning.ai, I want to run it on my machine and I am trying to create a REST API for this model.

I am trying to generate YOLOv2 model yolo.h5 so that I can load this pre-trained model. I am trying to port Andrew Ng coursera Yolo assignment ( which runs in tensorflow 1.x) to tensorflow 2.3.

I was able to cleanly port it thanks to tensorflow uprade (https://www.tensorflow.org/guide/upgrade), But little did I realize that I cannot download the yolo.h5 file ( either its get corrupted or the download times out) and therefore I thought I should build one and I followed instructions from https://github.com/JudasDie/deeplearning.ai/issues/2. It looked pretty straight forward as I cloned YAD2k repo and downloaded both the yolo.weights and yolo.cfg. I ran the following the command as per the instructions:

python yad2k.py yolo.cfg yolo.weights model_data/yolo.h5

But I got the following error:-

The code below was taken from TensorFlow in Practice by deeplearning.ai course in Coursera (computer vision example - week 2).

I am trying to complete some homework in a DeepLearning assignment [deeplearning.ai by Dr. Andrew N.G.(Stanford University)].

When I try the assignment in Coursera platform everything works fine, however, when I try to do the same imports on my local machine it gives me an error,

ModuleNotFoundError: No module named 'lr_utils'

I have tried resolving the issue by installing lr_utils but to no avail.

There is no mention of this module online(surprised me a bit) and now I started to wonder if that's a proprietary to deeplearning.ai?

Or can we can resolve this issue in any other way!

this code converts a 2-dimensional matrix to a vector that contains all the values of the matrix, however, I don't understand what does this means :

I've searched in other forums such as the deeplearning.ai forum about what this line of code mean ... but it's as if nobody had the same doubt as me...

"""

def image2vector(image):

I am currently taking the Deep Learning specialization by Deeplearning.ai on Coursera and am on the first assignment that requires implementing Neural Network with Logistic Regression mindset. The problem is that the assignment is implementation of Neural Network as Logistic Regression function for UNSTRUCTURED DATA (IMAGES). I have successfully completed the assignment, getting all the expected outputs. However, I am now trying to use the coded Neural Network for STRUCTURE DATA but come across broadcast error. Part of the code is as below :

The dataset code

I'm learning regularization in Neural networks from deeplearning.ai course. Here in dropout regularization, the professor says that if dropout is applied, the calculated activation values will be smaller then when the dropout is not applied (while testing). So we need to scale the activations in order to keep the testing phase simpler.

I understood this fact, but I don't understand how scaling is done. Here is a code sample which is used to implement inverted dropout.