#learningoptimization 搜尋結果
RT HRTechConf "RT DBoyleInfor: .Infor_HCM will showcase #LearningOptimization during HRTechConf … https://t.co/RoY37K60LL"
PrensarioTILA: #Infor: impulso de talentos con #LearningOptimization goo.gl/F2SllJ - Infor InforLatam
PrensarioTILA: #Infor: impulso de talentos con #LearningOptimization goo.gl/F2SllJ - Infor InforLatam
PrensarioTILA: #Infor: impulso de talentos con #LearningOptimization goo.gl/F2SllJ - Infor InforLatam
When you don't know which direction to go, or which of several options to choose, a simple heuristic is to optimize for learning -- which choice will yield the most interesting information?
One of my favorite lessons I’ve learnt from working with smart people: Action produces information. If you’re unsure of what to do, just do anything, even if it’s the wrong thing. This will give you information about what you should actually be doing. Sounds simple on the…
We tuned the plan. Now let's check the learning side. * Look back (what did you learn/wins) * Spot gaps (industry, business, workflow) * Prioritize (up to 3 for Jan–Jun) Write a one-liner for each: [Skill] + [Source] + [Frequency] + [Completion Date] Build your learning mix
Most people optimize for output. The smart ones optimize for speed of iteration. Productivity gets you results today. Adaptive learning keeps you relevant tomorrow and that's the real edge.
We need to optimize for learning per second, which requires a system that understands the students knowledge and deliver personalized education.
1. This paper optimizes for LLM inference by considering SLO (Service Level Objective). For LLM inference, we want to optimize for TTFT (time to first token) and TPOT (time per output token). We can optimize them to improve the user experience.
🤖🔍 This study explores autonomous metaheuristic algorithms that self-adjust parameters to handle complex, high-dimensional optimization. It showed improved performance on CEC LSGO benchmarks. 🔗 mdpi.com/2313-7673/9/1/7 #Optimization #Metaheuristics #MachineLearning
Augmenting humanity’s ability to optimize human experience requires understanding and planning every element of the experience. As labor becomes more predictable, we can use LLMs and other constructs to iteratively improve experiences that drive progress towards cohesive…
Unlocking Personalized eLearning 🎓🔓 Personalized eLearning tailors training to individual needs. Here's how: 🔹 Understand learner profiles 🔹 Offer flexible learning paths 🔹 Curate relevant content 🔹 Continuously assess and adjust 👉 Learn more: zurl.co/Z1aHN
Did training really go as planned? Plans set the target, but readiness, conditions, & execution can shift the stimulus. With Performance Optimization, you can see planned vs achieved loads instantly, spot misalignments, & prevent over/under-training. bit.ly/4reZOxQ
Bandwidth optimization minimizing data transfer costs during training
Day 32/40 of Python-ML. Training neural networks configuration. Wrapped up yesterday’s chapter on avoiding overfitting — learned how techniques like L1/L2 regularization, Dropout, and Max-Norm Regularization which help models generalize better... #pythonMl
Accelerating Training Speed of Tiny Recursive Models via Curriculum Guided Adaptive Recursion. arxiv.org/abs/2511.08653
💥A new approach enables LLMs to update themselves in a way that permanently internalizes new information The model generates multiple self-edits to learn from one input, then applies each one to see which improves its performance the most. This trial-and-error process teaches…
🧩 We learn action spaces via submodular optimization, balancing utility & diversity of candidate actions. A greedy linear-time selection builds dynamic spaces that steer MCTS toward stronger reasoning trajectories. #AIresearch #Submodular #LLM
LLM optimization (LLMO) is a marketing tactic that aims to improve a brand’s visibility and portrayal in LLM-generated responses - like those found in ChatGPT, Google’s AI Overviews, and Google’s AI Mode. Key LLM optimization techniques include: 1. Getting positive mentions of…
[ml grind] 📖 read RL handbook towardsdatascience.com/the-handbook-o… 🤯Tried to research more about nested learning more and seems like there is a two loops of learning: outer optimization and inner, which learns abt task itself
![ominousEureka's tweet image. [ml grind]
📖 read RL handbook towardsdatascience.com/the-handbook-o…
🤯Tried to research more about nested learning more and seems like there is a two loops of learning: outer optimization and inner, which learns abt task itself](https://pbs.twimg.com/media/G5fYfBLXoAEUlw2.jpg)
Check out the new optimization lessons right here: courses.tomlooman.com/courses/unreal…
AVOID this mistake when training your LLM - don't forget to do optimizer hyperparameter search 1. leaerning rate 2. momentum 3. weight decay 4. lr schedule 5. and more... 中文字幕请查看评论区的链接 (For chinese subtitles check the link in the comments)
RT HRTechConf "RT DBoyleInfor: .Infor_HCM will showcase #LearningOptimization during HRTechConf … https://t.co/RoY37K60LL"
PrensarioTILA: #Infor: impulso de talentos con #LearningOptimization goo.gl/F2SllJ - Infor InforLatam
PrensarioTILA: #Infor: impulso de talentos con #LearningOptimization goo.gl/F2SllJ - Infor InforLatam
PrensarioTILA: #Infor: impulso de talentos con #LearningOptimization goo.gl/F2SllJ - Infor InforLatam
Something went wrong.
Something went wrong.
United States Trends
- 1. Raiders 82.1K posts
- 2. #WWERaw 169K posts
- 3. Cowboys 51.4K posts
- 4. Pickens 21.1K posts
- 5. Gunther 21.7K posts
- 6. Geno 15.8K posts
- 7. Jeanty 7,013 posts
- 8. Chip Kelly 2,372 posts
- 9. #WickedForGood 8,422 posts
- 10. Pete Carroll 3,456 posts
- 11. Roman 75.2K posts
- 12. #Dragula N/A
- 13. Jlexis 8,149 posts
- 14. Dolph 42.8K posts
- 15. Mark Davis 1,494 posts
- 16. Brock 21.5K posts
- 17. Maxxine 19.7K posts
- 18. Becky 55.3K posts
- 19. AJ Lee 18.7K posts
- 20. Sigourney N/A