Automatic Differentiation with Python and C++ for Deep Learning
Author(s): Luiz doleron Originally published on Towards AI. This story explores automatic differentiation, a feature of modern Deep Learning frameworks that automatically calculates the parameter gradients during the training loop. The story introduces this technology in conjunction with practical examples using Python …
Deep Learning from Scratch in Modern C++: Cost Functions
Author(s): Luiz doleron Originally published on Towards AI. Let’s have fun by implementing Cost Functions in pure C++ and Eigen. In machine learning, we usually model problems as functions. Therefore, most of our work consists of finding ways to approximate functions using …
Deep Learning from Scratch in Modern C++: Gradient Descent
Author(s): Luiz doleron Originally published on Towards AI. Let’s have fun by implementing Gradient Descent in pure C++ and Eigen. In this story, we will cover the fitting of 2D convolution kernels from data by introducing the Gradient Descent algorithm. We will …
Deep Learning from Scratch in Modern C++: Convolutions
Author(s): Luiz doleron Originally published on Towards AI. Let’s have fun by implementing 2D Convolution in C++. In the previous story, we covered some of the most relevant coding aspects of machine learning such as functional programming, vectorization, and linear algebra programming. …
Deep Learning from Scratch in Modern C++
Author(s): Luiz doleron Originally published on Towards AI. Let’s have fun by implementing Deep Learning models in C++. It is needless to say how relevant machine learning frameworks are for research and industry. Due to their extensibility and flexibility, it is rare …
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