Edge computing can solve specific business problems that demand some combination of in-house computing, high speed, and low latency that cloud-based AI can’t deliver, explained Deepu Talla, NVIDIA VP and GM of Embedded and Edge Computing.

The hardware and architecture that can support edge computing has improved significantly over the past year, including GPUs with Tensor Cores for dedicated AI processing, plus secure, high-performance networking gear. And edge server software is growing more sophisticated as well, such as NVIDIA’s EGX cloud-native software stack, which brings traditional cloud capabilities to the edge of the network. He also pointed to the company’s industry-specific application frameworks such as Metropolis for smart cities, Clara for health care, Jarvis for conversational AI, Isaac for robotics, and Aerial for telecommunications — each supporting forms of AI on NVIDIA GPUs.

deeplizard demonstrates how to create a confusion matrix, which will aid us in being able to visually observe how well a neural network is predicting during inference.

VIDEO SECTIONS

  • 00:00 Welcome to DEEPLIZARD – Go to deeplizard.com for learning resources
  • 00:34 Plotting a Confusion Matrix
  • 02:48 Reading a Confusion Matrix
  • 04:56 Collective Intelligence and the DEEPLIZARD HIVEMIND

deeplizard shows us how to add batch normalization to a convolutional neural network.

Content index:

  • 00:00 Welcome to DEEPLIZARD – Go to deeplizard.com for learning resources
  • 00:30 What is Batch Norm?
  • 04:04 Creating Two CNNs Using nn.Sequential
  • 09:42 Preparing the Training Set
  • 10:45 Injecting Networks Into Our Testing Framework
  • 14:55 Running the Tests – BatchNorm vs. NoBatchNorm
  • 16:30 Dealing with Error Caused by TensorBoard
  • 19:49 Collective Intelligence and the DEEPLIZARD HIVEMIND

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

deeplizard debugs the PyTorch DataLoader to see how data is pulled from a PyTorch data set and is normalized.

We see the impact of several of the constructor parameters and see how the batch is built.

Content index:

  • 0:00 Welcome to DEEPLIZARD – Go to deeplizard.com
  • 0:45 Overview of Program Code
  • 3:12 How to Use Zen Mode
  • 3:56 Start the Debugging Process
  • 4:38 Initializing the Sampler Based on the Shuffle Parameter
  • 5:35 Debugging next(iter(dataloader))
  • 7:57 Building the Batch Using the Batch Size
  • 10:37 Get the Elements from Dataset
  • 18:43 Tensor to PIL Image
  • 20:41 Thanks for Contributing to Collective Intelligence