Build Your Own Image Classifier- Train a CNN on CIFAR-10 with Google Colab (Beginner-Friendly)
Download the source code from here:
https://onepagecode.substack.com/
All the explanations dataset are provided on my substack. It takes a lot of time to type and make this video, and I don't generate any income from this channel, so if you can subscribe to my substack it will be very helpful.
In the ever-evolving field of computer vision, convolutional networks have emerged as a cornerstone technology, driving advancements with their remarkable ability to process and interpret visual data. This article delves into the practical aspects of implementing and training convolutional networks, leveraging the widely-used CIFAR-10 dataset as a case study. We begin by setting up our environment in Google Colab, ensuring seamless access to necessary resources and datasets. As we journey through the intricacies of convolutional networks, we'll explore the implementation of various layer types and their integration to form an efficient learning model. This hands-on guide not only provides foundational knowledge but also equips readers with the practical skills needed to experiment with and optimize convolutional networks for real-world computer vision tasks.
Build Your Own Image Classifier- Train a CNN on CIFAR-10 with Google Colab (Beginner-Friendly)
