A deep dive into the foundational paper "Long Short-Term Memory" by S. Hochreiter and J. Schmidhuber, which introduced the LSTM architecture. We'll explore the motivation behind the model, its mathematical foundations and core intuitions, as well as its properties, achievements, and relevance today.
Building upon the recently implemented and trained VggNet, we will go through and implement 'A Neural Algorithm of Artistic Style' by Gatys, Ecker and Bethge which allowed to transfer the style of one image to the content of a different one. I will dive into some of the complexities of the implementation and give some beautiful examples of the results this technique can offer.
Let's code and train VGGNet from scratch! In this post, I will explain the process of implementing this iconic CNN from designing a general architecture and using dense evaluation to optimizing training speed and actually training the network to obtain a validation top-1 and top-5 error rates of 28.33% and 9.66% respectively. I will also compare the error rates and training performance against the original paper and AlexNet.
Continuing our exploration of foundational deep learning models in computer vision, we will dive into the 2014 paper Very Deep Convolutional Networks for Large-Scale Image Recognition by Karen Simonyan and Andrew Zisserman, which introduced VGGNet, a set of simple yet highly performant networks. We will examine its architecture, data processing, training, testing, and analysis of the results as a preliminary step toward implementing it.
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In this post, we will go through all the elements required to create and train AlexNet following the original paper. We will cover data processing, architecture definition, coding of training and validation loops, optimizations to speed up and training. Achieving comparable results with a top-1 error rate of 39.9% and top-5 error rate of 17.7%.
Let's dive into the paper 'ImageNet Classification with Deep Convolutional Neural Networks' by Alex Krizhevsky, Ilya Sutskever, and Geoffrey Hinton, which introduced the AlexNet to the world, and became a pivotal moment in the fields of computer vision and deep learning. The goal here is to explore in-depth the achievements, architecture, and details as a previous step toward its implementation.
Interactive calculator to compute the output shape of a convolutional layer based on the input, kernel and other settings. It supports non-square kernels, stride and padding.
An introduction to the basic concepts and building blocks of Convolution Neural Networks CNNs covering theory, best practices and practical widgets.
Let's understand Hopfield networks, introduced by John Hopefield in 'Neural networks and physical systems with emergent collective computational abilities' in 1982.
This is my first-ever post! I will explain my motivation for creating this space, the content I'd like to write about and the peculiarities that I'd like to create here.