پاسخ کارشناس فلسفه به برخی از سوالات مشتریان و کاربران آنتروپیک :

👉 @ai_python ✍️

https://youtu.be/I9aGC6Ui3eE?si=ADZiJjeEm7GRJ-wy

سوپابیس هم MCP خودش رو چند وقتی هست که برای همه کد ادیتور ها ارائه داده. که تعامل باهاش راحت تر بشه.

👉 @ai_python ✍️

👉 @ai_python ✍️

انواع منابع پردازشی موجود در Azure Machine Learning :

👉 @ai_python ✍️

1️⃣ Compute instance: Behaves similarly to a virtual machine and is primarily used to run notebooks. It's ideal for experimentation.

2️⃣ Compute clusters: Multi-node clusters of virtual machines that automatically scale up or down to meet demand. A cost-effective way to run scripts that need to process large volumes of data. Clusters also allow you to use parallel processing to distribute the workload and reduce the time it takes to run a script.

3️⃣ Kubernetes clusters: Cluster based on Kubernetes technology, giving you more control over how the compute is configured and managed. You can attach your self-managed Azure Kubernetes (AKS) cluster for cloud compute, or an Arc Kubernetes cluster for on-premises workloads.

4️⃣ Attached compute: Allows you to attach existing compute like Azure virtual machines or Azure Databricks clusters to your workspace.

5️⃣ Serverless compute: A fully managed, on-demand compute you can use for training jobs.

The paper on Nested Learning argues that current deep learning models suffer from an “anterograde amnesia” problem—unable to gradually consolidate new experiences into long-term memory—and proposes a new framework where learning occurs across multiple nested optimization levels operating at different frequencies. Drawing inspiration from the brain’s multi‑scale processing,

👉 @ai_python ✍️

it reframes optimizers as memory systems and shows that architectures like Transformers and MLPs are fundamentally uniform when viewed through this lens.


The key promise is continual learning without catastrophic forgetting, shifting focus from stacking layers to designing nested optimization processes across time scales.

https://www.k-a.in/nl.html