Neural Machine Translation with TensorFlow
Learn how to build build a recurrent neural network to do French to English translation using Google's open-source machine learning library, TensorFlow.
6 years ago
Deep Learning
Unpaired Image to Image Translation with CycleGAN
This article gives insights into the working mechanism of a Generative Adversarial Network and one of its popular variants, the Cycle Consistent Adversarial Network.
6 years ago
TensorFlow 2.0 in Action
To demonstrate what we can do with TensorFlow 2.0, we will be implementing a GAN mode using the Keras API and generative models.
By
Alvin Koontz
Move Quickly, Think Deeply: How Research Is Done @ Paperspace ATG
In this post, we'll be giving a broad overview of the tools and practices the Advanced Technologies Group, or ATG, uses to explore various research, in the form of a high level research workflow.
By
Harsh Sikka
6 Interesting Deep Learning Applications for NLP
Read on to discover deep learning methods are being applied in the field of natural language processing, achieving state-of-the-art results for most language problems.
6 years ago
Building a State of the Art Bacterial Classifier with Paperspace Gradient and Fast.ai
In this post, we’re going to be demonstrating how to to build a state of the art Bacterial Classification model on Gradient using the Fast.ai machine learning library.
By
Harsh Sikka
PyTorch 101, Part 5: Understanding Hooks
In this post, we cover debugging and Visualisation in PyTorch. We go over PyTorch hooks and how to use them to debug our backpass, visualise activations and modify gradients.
6 years ago
PyTorch 101, Part 4: Memory Management and Using Multiple GPUs
This article covers PyTorch's advanced GPU management features, including how to multiple GPU's for your network, whether be it data or model parallelism. We conclude with best practises for debugging memory error.
6 years ago
PyTorch 101, Part 3: Going Deep with PyTorch
In this tutorial, we dig deep into PyTorch's functionality and cover advanced tasks such as using different learning rates, learning rate policies and different weight initialisations etc
6 years ago
