Pinned Repositories
CC6204
Material del curso de Deep Learning de la Universidad de Chile
awd-lstm-lm
biopython-notebook
Notebooks to introduce biopython
black-action
A GitHub action that runs black code formatter for Python
BRENDA-Parser
Classes to parse the BRENDA database.
EasyPatents
Buscador/Comparador de textos enfocado en Análisis de Patentes de Invención
yapf-action
A YAPF formatter GitHub action
Hilbert-AE
Using Hilbert curve to compress sequence information with AutoEncoder
syllable-aware
Challenge-analitic-IBM
Desafio analitico Unicard - IBM
diegovalenzuelaiturra's Repositories
diegovalenzuelaiturra/EasyPatents
Buscador/Comparador de textos enfocado en Análisis de Patentes de Invención
diegovalenzuelaiturra/yapf-action
A YAPF formatter GitHub action
diegovalenzuelaiturra/awd-lstm-lm
diegovalenzuelaiturra/biopython-notebook
Notebooks to introduce biopython
diegovalenzuelaiturra/black-action
A GitHub action that runs black code formatter for Python
diegovalenzuelaiturra/BRENDA-Parser
Classes to parse the BRENDA database.
diegovalenzuelaiturra/diegovalenzuelaiturra.github.io
diegovalenzuelaiturra/keras
Deep Learning for humans
diegovalenzuelaiturra/NeMo
NeMo: a toolkit for conversational AI
diegovalenzuelaiturra/neural-image-assessment
Implementation of NIMA: Neural Image Assessment in Keras
diegovalenzuelaiturra/nmt
TensorFlow Neural Machine Translation Tutorial
diegovalenzuelaiturra/PyTorch-VAE
A Collection of Variational Autoencoders (VAE) in PyTorch.
diegovalenzuelaiturra/SIF_mini_demo
minimal example for sentence embedding by Smooth Inverse Frequency weighting scheme
diegovalenzuelaiturra/spanish-word-embeddings
Spanish word embeddings computed with different methods and from different corpora
diegovalenzuelaiturra/tape
Tasks Assessing Protein Embeddings (TAPE), a set of five biologically relevant semi-supervised learning tasks spread across different domains of protein biology.
diegovalenzuelaiturra/temperature_scaling
A simple way to calibrate your neural network.
diegovalenzuelaiturra/Theano
Theano is a Python library that allows you to define, optimize, and evaluate mathematical expressions involving multi-dimensional arrays efficiently. It can use GPUs and perform efficient symbolic differentiation.