According to @AndrewYNg, DeepLearning.AI Pro is now generally available, offering full access to 150+ programs including the Agentic AI course and newly released Post-Training and PyTorch courses by Sharon Zhou and Laurence Moroney (source: Andrew Ng on X, Oct 30, 2025; https://twitter.com/AndrewYNg/status/1983946706564563171). All course videos remain free, while Pro adds hands-on labs, practice questions, and shareable certificates to accelerate building production-grade AI applications and career outcomes (source: Andrew Ng on X, Oct 30, 2025; https://twitter.com/AndrewYNg/status/1983946706564563171). New tools to help users create AI applications will roll out, with many available first to Pro members, and a free trial is available at https://learn.deeplearning.ai/membership (source: Andrew Ng on X, Oct 30, 2025; https://twitter.com/AndrewYNg/status/1983946706564563171). The announcement does not disclose any crypto tokens, equities, pricing, or partner integrations, implying limited immediate market-moving data for AI-related assets; traders should note this is primarily an upskilling catalyst around agentic AI and post-training workflows (source: Andrew Ng on X, Oct 30, 2025; https://twitter.com/AndrewYNg/status/1983946706564563171).

According to Soumith Chintala, Apple's actual engineering time on PyTorch support has not given him confidence that the PyTorch Mac experience will get close to NVIDIA's any time soon, if ever, source: Soumith Chintala on X, Oct 16, 2025. According to Soumith Chintala, Meta engineers are doing a large share of the heavy lifting to improve the MPS backend and feel responsible for the Mac experience, while Apple's priorities and engineering hours fluctuate, source: Soumith Chintala on X, Oct 16, 2025. According to Soumith Chintala, PyTorch has over 90% AI market share and Apple must prioritize full PyTorch software support if it wants Mac Studio to be an AI development box rather than mainly an inference machine, source: Soumith Chintala on X, Oct 16, 2025. According to Soumith Chintala, the NVIDIA stack remains the reference for PyTorch training quality versus Apple's current MPS pathway, which is a trading-relevant signal for relative AI development readiness between NVDA and AAPL ecosystems, source: Soumith Chintala on X, Oct 16, 2025. According to Soumith Chintala, he did not mention cryptocurrencies such as BTC or ETH, indicating no direct crypto market impact is stated in his post, source: Soumith Chintala on X, Oct 16, 2025.

According to Soumith Chintala, an efficient wrapper around FFmpeg for PyTorch has been developed, utilizing FFmpeg's fast seeking and read-ahead APIs correctly. This wrapper also optimizes memory buffer usage, avoiding unnecessary allocations and copies, which could significantly enhance video processing tasks in machine learning projects.

According to Soumith Chintala, a few months ago, a video decoding library named torchcodec was open-sourced for PyTorch. Described as small, nimble, and fast, it has received positive feedback from the LeRobotHF community. This development could potentially enhance video processing capabilities in AI and machine learning projects, impacting sectors reliant on video data analysis.

According to Soumith Chintala, the PyTorch team is making strides in democratizing fast kernel writing. This development could enhance computational efficiency and performance for AI applications, impacting trading algorithms reliant on machine learning models. Source: @soumithchintala

According to @DeepLearningAI, the newly launched course 'Attention in Transformers: Concepts and Code in PyTorch' by @joshuastarmer offers insights into how attention mechanisms in LLMs (Large Language Models) enhance base token embeddings into rich, context-aware embeddings, which is crucial for traders looking to understand the transformation of data in AI-driven trading algorithms.

According to Andrew Ng, a new course focusing on the attention mechanism within LLM transformers and its implementation in PyTorch has been released. This course aims to provide deeper technical insights crucial for developing advanced machine learning models, potentially impacting algorithmic trading strategies that leverage AI for market predictions.

According to DeepLearning.AI, a new course titled 'Attention in Transformers: Concepts and Code in PyTorch' has been introduced, focusing on the critical attention mechanism in transformer models. The course is taught by Joshua Starmer, founder of StatQuest, and aims to provide a deep understanding of attention mechanism implementation using PyTorch. This knowledge is essential for traders and developers looking to enhance algorithmic trading models with advanced machine learning techniques. Source: DeepLearning.AI Twitter