PyTorch gets you pretty far, but when performance becomes the problem, understanding what’s happening at the GPU level matters. In the first chapter of CUDA for Deep Learning, @elliotarledge explains why GPUs excel at workloads like matrix multiplication and convolutions. He also gets into when writing custom CUDA is worth it instead of relying entirely on high-level libraries.

First Chapter Summary: https://hubs.la/Q04h1-z40

@ManningBooks: PyTorch gets you pretty far, but when performance becomes the problem, understanding what’s happening at the GPU level …

Channel: @ManningBooks Source: https://www.youtube.com/watch?v=qRLyoP8zOyQ&utm_campaign=36463000-book_arledge&utm_content=378180001&utm_medium=social&utm_source=twitter&hss_channel=tw-24914741

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A sneak peek at the first chapter of a book by Elliot Arledge 📖 CUDA for Deep Learning | https://hubs.la/Q04gYKr_0 📖 To save 40% off this book ⭐ DISCOUNT CODE: watcharledge40 ⭐

In this chapter recap from CUDA for Deep Learning by Elliot Arledge, we step beneath PyTorch and look at the CUDA programming model that powers modern deep learning on NVIDIA GPUs. You’ll learn why GPUs are so effective for workloads like matrix multiplication, convolutions, activations, and attention, and when it’s worth writing custom CUDA instead of relying on PyTorch, cuBLAS, or cuDNN.

This video covers the big ideas from Chapter 1:

• What CUDA is and how it fits under frameworks like PyTorch
• The difference between host code on the CPU and device code on the GPU
• Why CUDA kernels run across thousands of lightweight GPU threads
• How to recognize “same operation, different data” patterns in deep learning
• Why GPU memory hierarchy often matters more than raw compute
• When custom CUDA kernels make sense, and when PyTorch is still the right tool
• The optimization path from naive kernels to tensor cores, Flash Attention, quantization, and distributed training

If you’re an AI engineer, C/C++ developer, or deep learning practitioner who wants to understand what the GPU is actually doing, this chapter gives you the mental model you’ll need before writing your first kernel.

CUDA for Deep Learning teaches CUDA from first principles, then builds toward practical deep learning kernels, transformer inference, tensor cores, Flash Attention, and PyTorch C++ extensions.

👉 Get the book here: https://hubs.la/Q04gYKr_0 ⭐ Save 40% with code: watcharledge40

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