Serving Word Vectors for Distributed Computations

Word vectors are amazing! Vectors for everything! And that is why we use them for various deep learning tasks at Talentpair. What makes them amazing? Each word in the model is represented by vector. Each vector captures the semantic meaning of the word. Therefore, related terms will be close to each other, unrelated terms faraway. … Continue reading Serving Word Vectors for Distributed Computations

Visualizing Convolutional Nets for NLU Tasks

With deep learning becoming the foundation for Talentpair’s matching algorithm, we wanted to get a better understanding how our convolutional neural networks make the decisions they do. Google’s TensorBoard has been a tremendous help to achieve our goal (read more about Talentpair’s deep learning stack here). Unfortunately, due to our model setup, we couldn’t take … Continue reading Visualizing Convolutional Nets for NLU Tasks

Talentpair’s Deep Learning Stack

Over the past few months, we have upgraded our deep learning stack to take advantage of the latest developments in the world of deep learning. We transitioned from Theano to Tensorflow, upgraded our Keras version, introduced GPUs into the Talentpair stack and rolled out helpful machine learning tools to accelerate our model iterations. Transitioning from … Continue reading Talentpair’s Deep Learning Stack

Running AngularJS Tests with Jest

Earlier this year, we began the process of using React in our AngularJS app. Luckily, rendering React components within Angular isn’t too hard. We use a component “bridge” based on the approach shared by the Small Improvements team, which is working well. A side-effect of bringing React into the codebase was seeing how fast Jest … Continue reading Running AngularJS Tests with Jest