ResNet, a simple introduction
Residual networks or ResNets for short have been key elements in the computer vision oriented deep learning community. In this...
Tag: Machine Learning
Greedy layer-wise training of deep networks, a TensorFlow/Keras example
In the early days of deep learning, people training neural networks continuously ran into issues – the vanishing gradients problem...
Quick and easy GPU & TPU acceleration for PyTorch with HuggingFace Accelerate
Deep learning benefits from Graphical Processing Units (GPUs) and Tensor Processing Units (TPUs) because of the way they handle the...
How to use Upsample for upsampling with PyTorch
Within computer vision, upsampling is a relatively common practice these days. Whereas Convolutional layers and Pooling layers make inputs smaller,...
Cropping layers with PyTorch
Sometimes, you may wish to perform cropping on the input images that you are feeding to your neural network. While...
How to use TensorBoard with PyTorch
When your Deep Learning model is training, you are interested in how well it performs. Often, it’s possible to output...
GANs: an Introduction to Fréchet Inception Distance (FID)
The Frechet Inception Distance or FID is a method for comparing the statistics of two distributions by computing the distance between them....
Simple Multi-options A/B/n test with Multi-Armed Bandit in Python
The hardened Machine Learning professional knows that there are three key branches of ML: supervised learning, unsupervised learning and reinforcement...
How to use L1, L2 and Elastic Net regularization with PyTorch?
Training a neural network means that you will need to strike a balance between optimization and over-optimization. Over-optimized models work...
Building a simple vanilla GAN with PyTorch
Using a Generative Adversarial Model, or a GAN, it is possible to perform generative Machine Learning. In other words, you...
