Dive into Deep Learning
Dive into Deep Learning은 PyTorch, NumPy/MXNet, JAX, TensorFlow로 구현된 오픈소스 대화형 딥러닝 교재로, 전 세계 500여 대학에서 채택되어 교육 및 연구에 활용되고 있습니다. 최신 강화학습, 가우시안 프로세스, 하이퍼파라미터 최적화 등 주제를 포함하며, Jupyter 노트북 기반으로 실습과 즉각적인 피드백이 가능합니다. Amazon, Google, CMU, NYU 등 다양한 기관의 연구자들이 참여해 지속적으로 업데이트되고 있으며, SageMaker Studio Lab, Google Colab 등 클라우드 환경에서 무료로 실행할 수 있습니다. 활발한 커뮤니티 지원과 다국어 번역도 제공되어 AI 개발자와 연구자에게 실용적인 학습 자원입니다.
📰 Architecture of Uncertainty: The New Landscape of Machine Learning
Explore the new landscape of machine learning and uncertainty architecture. Learn how MLOps and TensorFlow are transforming AI into an industrial powerhouse. Read the article!
#machinelearning #TensorFlow #MLOps #PyTorch #ReinforcementLearning
Business Latest | CUDA Proves Nvidia Is a Software Company by Sheon Han
AI generated summary, Read the full article for complete information.
The article argues that Nvidia’s true competitive advantage—its “moat”—lies not in its hardware but in its CUDA software platform, which enables efficient parallelization of GPU tasks and has become the foundation for modern AI frameworks. Originating from a Stanford‑spun idea to repurpose graphics GPUs for general‑purpose computing, CUDA evolved into a layered suite of highly optimized libraries that squeeze massive performance gains from Nvidia chips, creating a strong lock‑in effect that makes rival hardware (AMD, Intel, etc.) underperform despite comparable specifications. Because writing low‑level CUDA kernels is extremely specialized and most AI researchers lack the expertise, competitors’ alternatives such as OpenCL, AMD’s ROCm, and Intel’s oneAPI have failed to gain traction. Consequently, Nvidia’s dominance in AI is driven by its software ecosystem, much like Apple’s success stems from its integrated iOS environment, allowing the company to command premium pricing while keeping others at bay.
Read more: https://www.wired.com/story/cuda-proves-nvidia-is-a-software-company/
Luckfox Aura is a Linux SBC with RV1126B processor, 3 TOPS NPU, and dual CSI
PyTorch vs. TensorFlow: Choosing the Right Framework in 2026
#Python #Pycharm #Datascience #Pytorch #Tensorflow
https://blog.jetbrains.com/pycharm/2026/05/pytorch-vs-tensorflow-choosing-framework-2026/
Discover TorchTPU, Google’s new engineering stack designed to run PyTorch natively on TPU infrastructure with peak efficiency. Learn how its "Eager First" philosophy and XLA integration simplify model migration while unlocking massive scalability for the next generation of AI.
IP67-rated AI security camera feature Rockchip RV1126B or RK3576/J/M SoC for commercial, industrial, and automotive applications
Back in January 2024, Firefly released the CT36L AI smart security cameras, built around the Rockchip RV1106G2 SoC with a 0.5 TOPS NPU and Power over Ethernet (PoE) support. Now, Firefly has introduced two new AI cameras, the CQ38W-1126B and CQ38W-3576, which use a similar IP67-rated enclosure but come with much more powerful processors. Both new models no longer support PoE and instead use a 12V DC input, and they also add an RS485 interface. In terms of performance, the CQ38W-1126B is built around the Rockchip RV1126B with a 3 TOPS NPU and can run small multimodal AI models. The higher-end CQ38W-3576 features an octa-core Rockchip RK3576 with a 6 TOPS NPU, making it suitable for more demanding AI workloads, including YOLO and large language models. Both cameras are available with 3MP or 5MP sensors and come in Commercial or Industrial (J-suffix) variants. The 3576 series also adds an Automotive-grade
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