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...
Tag: Machine Learning
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...
Creating DCGAN with TensorFlow 2 and Keras
Generative Machine Learning is a really interesting area of research that investigates how Machine Learning (and by consequence, Deep Learning)...
Easy grammar error detection & correction with Machine Learning
Machine learning in general and deep learning in particular has boosted Natural Language Processing. A variety of models has allowed...
Convolutional Neural Networks with PyTorch
Deep neural networks are widely used to solve computer vision problems. Frequently, their performance is much better compared to Multilayer...
Using Dropout with PyTorch
The Dropout technique can be used for avoiding overfitting in your neural network. It has been around for some time...
An introduction to DCGANs
The class of Generative Adversarial Network models, or GANs, belongs to the toolbox of any advanced Deep Learning engineer these...
Easy Chatbot with DialoGPT, Machine Learning and HuggingFace Transformers
These past few years, machine learning has boosted the field of Natural Language Processing via Transformers. Whether it’s Natural Language...
DialoGPT: Transformers for Dialogues
DialoGPT is “a tunable gigaword-scale neural network model for generation of conversational responses, trained on Reddit data”. It uses a...
Transformers for Long Text: Code Examples with Longformer
Transformer models have been boosting NLP for a few years now. Every now and then, new additions make them even...
