NLP model selection guide to make it easier to select models. This is prescriptive in nature and has to be used with caution.

transfer-learning natural-language-processing neural-networks transformers

2020-05-11 · This class will start with a brief overview of neural networks, then spend the majority of the class demonstrating how to apply neural networks to ...

natural-language-processing course carnegie-mellon neural-networks

2020-06-25 · Expand the traditional EDA in a wider pipeline looking for the impact of each action into the behaviour of models. Exploratory Data & Models Analysis

exploratory-data-analysis classification machine-learning logistic-regression

2020-05-01 · On-line interactive book introducing the history, theory, and math of Neural Network Models with Python, from a Cog Science perspective.

neural-networks convolutional-neural-networks recurrent-neural-networks deep-learning

A Tensorflow-based framework to ease the training of generative models

computer-vision generative-adversarial-networks tensorflow deep-learning

2020-04-01 · Combine interpretability of a decision tree with accuracy of a neural network.

decision-trees neural-networks deep-learning interpretability

This repository provides tutorial code in C++ to learn PyTorch by building CNNs, RNNs, etc. Tutorials are divided into three sections based on complexity.

pytorch c++ torch torchscript

2020-05-24 · Development of neural networks over history.

neural-networks history perceptron multilayer-perceptrons

2020-04-04 · We all know about meta-learning and pseudo labeling but what if we combine the two techniques for semi-supervised learning? Can it be any beneficial?

semi-supervised-learning meta-learning machine-learning deep-learning

A book on data science and machine learning with a foundation in applied statistics leading into deep learning.

machine-learning neural-networks linear-regression linear-discriminant-analysis

projects **1 - 10** of **35**