Explore a highly effective deep learning approach to sentiment analysis using TensorFlow and LSTM networks.

Dear Friends,

Let’s build a neural network that can do math. In Keras.

Tensorflow implementation of different GANs and their comparisions - sanghoon/tf-exercise-gan

A tensorflow implementation of EAST text detector. Contribute to argman/EAST development by creating an account on GitHub.

The Keras library provides a way to calculate and report on a suite of standard metrics when training deep learning models. In addition to offering standard metrics for classification and regression problems, Keras also allows you to define and report on…

Can you see the resemblance?

A few months ago I posted some results from experiments with highresolution GAN-generated faces. By popular request here is a little more on the approach tak...

Еще одна статья от лица участника школы о проекте, реализованном в рамках очередного выезда: «Я – Дмитрий Пасечнюк, и я хочу поделиться своим исследованием, с...

Introduction:

Tensorboard’s embeddings visualiser is great. You can use it to visualise and explore any set of high dimensional vectors (say, the activations of a hidden layer of a neural net) in a lower-dimensional space.

A python tool for evaluating the quality of sentence embeddings. - facebookresearch/SentEval

Deep recommender models using PyTorch. Contribute to maciejkula/spotlight development by creating an account on GitHub.

TensorFlow tutorials and best practices. Contribute to vahidk/EffectiveTensorflow development by creating an account on GitHub.

Deep learning is an incredibly powerful technology for understanding messy data from the real world—and the TensorFlow machine learning library is the ideal way to harness that power. In this practical report, author Pete Warden, tech lead on the Mobile/Embedded…

Modified implementation of DCGAN focused on generative art. Includes pre-trained models for landscapes, nude-portraits, and others. - robbiebarrat/art-DCGAN

Long Short-Term Memory (LSTM) Recurrent Neural Networks are a powerful type of deep learning suited for sequence prediction problems.

A possible concern when using LSTMs is if the added complexity of the model is improving the skill of your model or is…