Techniques for training large neural networks
Large neural networks are at the core of many recent advances in AI, but training them is a difficult engineering and research challenge which requires or...
Similar Articles (10 found)
π 71.4% similar
Why DeepSeek is cheap at scale but expensive to run locally
Why is DeepSeek-V3 supposedly fast and cheap to serve at scale, but too slow and expensive...
π 68.8% similar
For some reason they focus on the inference, which is the computationally cheap part. If you're working on ML (as opposed to deploying someone else's ...
π 67.3% similar
First, thanks to the publisher and authors for making this freely available!
I retired recently after using neural networks since the 1980s. I still s...
π 65.3% similar
Your Laptop Isnβt Ready for LLMs. Thatβs About to Change
Local AI is driving the biggest change in laptops in decades
Odds are the PC in your office t...
π 64.8% similar
The three types of LLM workloads and how to serve them
We hold this truth to be self-evident: not all workloads are created equal.
But for large langu...
π 64.5% similar
A Recipe for Training Neural Networks
Some few weeks ago I posted a tweet on βthe most common neural net mistakesβ, listing a few common gotchas relat...
π 64.2% similar
> the generation of 281,128 augmented examples, from which 1,000 were
held out as a benchmark test set.
This model is trained on a custom dataset of 2...
π 64.1% similar
If you are reading this, you probably have strong opinions about AGI, superintelligence, and the future of AI. Maybe you believe we are on the cusp of...
π 63.8% similar
Understanding LLM Inference Engines: Inside Nano-vLLM (Part 1)
Architecture, Scheduling, and the Path from Prompt to Token
When deploying large langua...
π 63.4% similar
Writing an LLM from scratch, part 22 -- finally training our LLM!
This post wraps up my notes on chapter 5 of Sebastian Raschka's book "Build a Large ...