Article
Neural Radiance Fields, colloquially known as NeRFs have struck the world by storm in 2020, released alongside the paper "NeRF: Representing Scenes as Neural Radiance Fields for View Synthesis", and are still the cornerstone of high quality synthesis of novel views, given sparse images and camera positions. Since...
David Landup
Suppose you want your Keras model to have some specific behavior during training, evaluation or prediction. For instance, you might want to save your model at every training epoch. One way of doing this is using Callbacks. In general, Callbacks are functions that are called when some event happens, and...
Felipe Antunes
Object detection has been gaining steam, and improvements are being made to several approaches to solving it. In the past couple of years, YOLO-based methods have been outperforming others in terms of accuracy and speed, with recent advancements such as YOLOv7 and YOLOv6 (which was released independently, after YOLOv7). However...
The learning rate is an important hyperparameter in deep learning networks - and it directly dictates the degree to which updates to weights are performed, which are estimated to minimize some given loss function. In SGD: $$ weight_{t+1} = weight_t - lr * \frac{derror}{dweight_t} $$ With a learning...
Data augmentation has, for a long while, been serving as a means of replacing a "static" dataset with transformed variants, bolstering the invariance of Convolutional Neural Networks (CNNs), and usually leading to robustness to input. Note: Invariance boils down to making models blind to certain perturbations, when making...
Most practitioners, while first learning about Convolutional Neural Network (CNN) architectures - learn that it's comprised of three basic segments: Convolutional Layers Pooling Layers Fully-Connected Layers Most resources have some variation on this segmentation, including my own book. Especially online - fully-connected layers refer to a flattening layer and (usually)...
Deep Learning frameworks like Keras lower the barrier to entry for the masses and democratize the development of DL models to unexperienced folk, who can rely on reasonable defaults and simplified APIs to bear the brunt of heavy lifting, and produce decent results. A common confusion arises between newer deep...
There are plenty of guides explaining how transformers work, and for building an intuition on a key element of them - token and position embedding. Positionally embedding tokens allowed transformers to represent non-rigid relationships between tokens (usually, words), which is much better at modeling our context-driven speech in language modeling....
Byte
Computer Vision models have come a long way - and you can leverage existing models, pre-trained on a large corpora of data, and just plug them into your prediction pipeline. While fine-tuning a network is the best way to go - importing an existing model and running predictions from the...
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