In this tutorial, we'll learn Predicting my cat’s species using Tensorflow and Python.
I decided to try my hand at a classic image classification problem this week. I wondered how well I could train a neural network model to tell if an image contains a cat or not. I have come across many examples of CNN projects identifying cats and dogs in images, so I decided to try to solve the problem on my own using a few tutorials as a reference. I will share the sources I used to develop this neural network model and fine-tune it for the best results.
I have a cat at home, and she has been an excellent source of entertainment and comfort over this past year of repeated lockdowns and restrictions. My real motivation for this little project is to see if my cat Fussel (the German word for fluff or lint), who is known for her rather ridiculous poses, would be correctly classified as a ‘cat’ by such an AI model. I have some pretty unique images of her to test out, so I guess I will need a rather good model to correctly classify her!
In this post, we'll learn top 30 Python Tips and Tricks for Beginners
Here, we will discuss how deep learning in python works for various applications, including neural networks and computer vision. A new and useful knowledge. It's a pity if you ignore it.
In this video we will do small image classification using CIFAR10 dataset in tensorflow. We will use convolutional neural network for this image classification problem. First we will train a model using simple artificial neural network and then check how the performance looks like and then we will train a CNN and see how the model accuracy improves. This tutorial will help you understand why CNN is preferred over ANN for image classification.
This video shows performance comparison of using a CPU vs NVIDIA TITAN RTX GPU for deep learning. We will use simple artificial neural network (we are not using CNN, usually CNN is preferred for image classification but since we have not covered that in our deep learning playlist so far we will be happy with simple ANN that still gives pretty high accuracy).
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