deeplizard teaches us how to set up debugging for PyTorch source code in Visual Studio Code.

Content index:

  • 00:00 Welcome to DEEPLIZARD – Go to deeplizard.com for learning resources
  • 00:27 Visual Studio Code
  • 00:55 Python Debugging Extension
  • 01:30 Debugging a Python Program
  • 03:46 Manual Navigation and Control of a Program
  • 06:34 Configuring VS Code to Debug PyTorch
  • 08:44 Stepping into PyTorch Source Code
  • 10:36 Choosing the Python Environment00:00 Welcome to DEEPLIZARD – Go to deeplizard.com for learning resources
  • 00:27 Visual Studio Code
  • 00:55 Python Debugging Extension
  • 01:30 Debugging a Python Program
  • 03:46 Manual Navigation and Control of a Program
  • 06:34 Configuring VS Code to Debug PyTorch
  • 08:44 Stepping into PyTorch Source Code
  • 10:36 Choosing the Python Environment
  • 12:30 Collective Intelligence and the DEEPLIZARD HIVEMIND

Text-to-speech engines are usually multi-stage pipelines that transform the signal into many intermediate representations and require supervision at each step.

When trying to train TTS end-to-end, the alignment problem arises: Which text corresponds to which piece of sound?

This paper uses an alignment module to tackle this problem and produces astonishingly good sound.

Paper: https://arxiv.org/abs/2006.03575
Website: https://deepmind.com/research/publications/End-to-End-Adversarial-Text-to-Speech

Content index:

  • 0:00 – Intro & Overview
  • 1:55 – Problems with Text-to-Speech
  • 3:55 – Adversarial Training
  • 5:20 – End-to-End Training
  • 7:20 – Discriminator Architecture
  • 10:40 – Generator Architecture
  • 12:20 – The Alignment Problem
  • 14:40 – Aligner Architecture
  • 24:00 – Spectrogram Prediction Loss
  • 32:30 – Dynamic Time Warping
  • 38:30 – Conclusion

Cognizant’s Connected Factories is a next-generation offering that accelerates solution development and deployment for Industry 4.0 solutions helping manufacturers improve productivity, yield and safety.

It shortens time to value with pre-defined information meta models as well as configurable microservices for industry standard KPIs; fully leveraging the latest of Azure IoT services stack.

Learn more about Cognizant’s Connected Factories solution: https://aka.ms/iotshow/cognizant

Yannic Kilcher explores a recent innovation at Facebook

Code migration between languages is an expensive and laborious task. To translate from one language to the other, one needs to be an expert at both. Current automatic tools often produce illegible and complicated code. This paper applies unsupervised neural machine translation to source code of Python, C++, and Java and is able to translate between them, without ever being trained in a supervised fashion.

Paper: https://arxiv.org/abs/2006.03511

Content index:

  • 0:00 – Intro & Overview
  • 1:15 – The Transcompiling Problem
  • 5:55 – Neural Machine Translation
  • 8:45 – Unsupervised NMT
  • 12:55 – Shared Embeddings via Token Overlap
  • 20:45 – MLM Objective
  • 25:30 – Denoising Objective
  • 30:10 – Back-Translation Objective
  • 33:00 – Evaluation Dataset
  • 37:25 – Results
  • 41:45 – Tokenization
  • 42:40 – Shared Embeddings
  • 43:30 – Human-Aware Translation
  • 47:25 – Failure Cases
  • 48:05 – Conclusion