#learnalgorithms arama sonuçları

LoRA makes fine-tuning more accessible, but it's unclear how it compares to full fine-tuning. We find that the performance often matches closely---more often than you might expect. In our latest Connectionism post, we share our experimental results and recommendations for LoRA.…

Building an agent that can generate an entire interactive course from a PDF/textbook 🤯 The way it works: I have pre-built components (Q&A, drag & match, flowchart, info bubble, ect...) that can be invoked through tool calls. Here's the full workflow: 1. The agent will first…

Understanding Machine Learning Algorithms Guide f.mtr.cool/opyadfsljo

🧠 How can we equip LLMs with memory that allows them to continually learn new things? In our new paper with @AIatMeta, we show how sparsely finetuning memory layers enables targeted updates for continual learning, w/ minimal interference with existing knowledge. While full…

We just released a free online robotics course! Let's make everyone robotics AI builders thanks to open-source! 🦾🦾🦾 The course will take you on a journey, from classical robotics to modern learning-based approaches, in understanding, implementing, and applying machine…

If you're prepping for technical interviews, you likely have data structures and algorithms on the brain. And this comprehensive (49 hour!) course covers all the most popular ones you'll run into during interviews. You'll also learn about Big O Notation, sorting and searching,…

Using custom-trained LLMs and > 1k 4090s to visualize 100k scientific research papers in latent space 🌐 DM me for early access 🔜

RAG vs Fine-tuning: Which one should you use? When it comes to adapting Large Language Models (LLMs) to new tasks, two popular approaches stand out: Retrieval-Augmented Generation (RAG) and Fine-tuning. They solve the same problem, making models more useful, but in very…

La suite de mon introduction au Reinforcement Learning est en ligne ! 🚀 Après avoir posé les bases, on plonge dans le vif du sujet : le Deep Reinforcement Learning. On y explore les algorithmes qui ont tout changé et on découvre comment ils sont utilisés pour aligner les LLMs .

Learning via visualisation and then learning how algorithms learn will never be not fascinating for me. If you're an enthusiast or just starting with ML, check this out ml-visualized.com/index.html

Gsenti Fam If you've built or used open-source AI models, you know the excitement—and the worries. Sharing your work lets others learn and improve it. But what if someone tweaks it for harm, steals credit, or copies it without a trace? That's where Sentient's Lock-LLMs comes…

Builds an OS agent for long-horizon tasks. Uses step-wise RL and self-evolve training to enable the agent to carry out long-horizon interactions. Instead of one big model doing everything, it uses multiple cooperating agents. One for memory and context, one for breaking down…

The best books to learn Algorithms 1 - amzn.to/3VsmSu9 2 - amzn.to/47oxAcn 3 - amzn.to/3JETfTX 4 - amzn.to/461VEiX 5 - amzn.to/3I0Lpn2 #algorithms #maths #100DaysOfCode #CodeNewbie #code #Coding #LearnToCode #DataScience #DataScientist…

Recent advances such as self-consistency and test-time reinforcement learning improve the reliability of LLMs without additional supervision, but do we really know why they work? We dig into the maths behind it

Boost your ML model's performance with hyperparameter tuning! Explore techniques like Grid Search, Random Search, and Bayesian Optimization. Read more info : nomidl.com/machine-learni… #MachineLearning #HyperparameterTuning #AI #DataScience #MLTips

Last night I taught nanochat d32 how to count 'r' in strawberry (or similar variations). I thought this would be a good/fun example of how to add capabilities to nanochat and I wrote up a full guide here: github.com/karpathy/nanoc… This is done via a new synthetic task…

โมเดล AI เก่งเกินไป? ระวัง Overfitting! ✅ L1 → ลดน้ำหนักบางตัว = เลือกฟีเจอร์สำคัญ ✅ L2 → กระจายน้ำหนักให้เรียบ ✅ Dropout → ลดการพึ่งพานิวรอนบางตัว Regularization = โมเดลเรียนรู้ภาพรวมดีขึ้น #DeepLearning #NeuralNetworks #Keras #AI #MachineLearning #Overfitting

If you want to get into serious machine learning, beyond just “copy-paste an example from a ML library’s documentation and change the input data to your own,” then at a bare minimum, these are the math subjects you need to know for ML:

🚀 We used AI to discover a new algorithm for LLM inference, achieving a 5.0x speedup in MoE load balancing over expert-written code. ✍️ Read the details in our blog post: adrs-ucb.notion.site/moe-load-balan… 📄 Full paper: arxiv.org/abs/2510.06189 💻 Code: github.com/UCB-ADRS/ADRS