#optimizer

Started optimizers. Vanilla gradient descent struggles with picking the right learning rate, predefined schedules that can’t adapt to the data, one learning rate for every parameter, and saddle points where the gradient vanishes. Momentum, RMSprop and Adam each fix a piece.

Only two types of fools are crushing with the ladies right now Hyper-optimizers on steroids and Peptides getting as rich as possible Or complete dirtbags living in Vans Once again the middle gets crushed

📊 FREE TNF PLAYER PROP TREND TOOL Flowers O47.5 Rec Yds — 6/7 Hunter U1.5 Receptions — 6/7 Lawrence U3.5 Rush Attempts — 6/7 Which trend gets your attention? 👀

Pick Optimizers • Betting Models • Slip Builder

I GOT SHADOWED PLAYING COMBAT TRAINING @CallofDutyCM PLEASE TELL ME THIS IS A JOKE 😭

Thank You @CallofDutyCM 🙏

@Rivvwz @CallofDutyCM

🚨WATCH OUT🚨 Startup chatgpt pc optimizers consistently popup in the community. If your looking to get your pc optimized, don't know anything about computers, and want the best performance for competitive games, you may go to someone like this and get scammed! Pop up tweaker @machatweaks who clearly lacks knowledge, and his only source of reputability is his "pro vouches" which are people in his friend group. He puts his clients on windows, 25h2 and doesn't even activate the windows for his clients😭+ doesn't put them on a local account. He then puts them on the latest gpu driver with terrible nvcp settings and calls it a day. As for his wonderful debloating of 100 processes on boot, which yes processes don't matter to much in this case but he left every app to run on startup, didn't touch autoruns, kept windows defender, left sysmain, and deliver optimization running😂and many other crucial to disable services + windows updates on (which will soon update the pc and get rid of all of the "tweaks"). Then doing no gpu oc, calling it a day going into bios and doing placebo dram timings, a few basic settings, and bad cpu oc's. This sums up his 100$ service, yes ik, 100$ 😿. I feel bad for ever poor soul that got scammed by this kid just watch out and stay safe guys, and remember that people like him are looking to take your money out there WATCH OUT PLEASE!

Good advice if you get your pc optimized not guna name names but they are active on Twitter. Go into nvidia profile inspector and make sure they are not locking your preset to m and forcing ultra performance which makes 0 semse because preset m is for performance. 3 people that came to me from this optimizer all were locked on ultra performance for quality so when he puts them on performace in game it looks like they are getting performance fps when realistically they are getting ultra with terrible quality #optimizers #MW4 #whatarewedoing

Another client kit off the bench 5800 MT/s | 2000 FCLK | 28-36-36-48 | Hynix M-Die No heatsink swap, no fan on the DIMMs. This client went through a few optimizers and none of them could get even plain EXPO 6000 stable, so he got left on JEDEC 4800 CL40. Turned out his IMC just doesn’t hold 6000, bad luck, but 5800 CL28 is way better than stock a JEDEC. Every IMC is different. I work with yours, not settle for JEDEC. Want a profile built for YOUR system? Book now ⬇️

Could turbulence be affecting your smokestack flow-meter readings? A Flow Optimizers grated spool conditions airflow upstream of the meter to improve measurement accuracy. Learn more: #FlowMeasurement

NEW ANIMATION FOR THE ONE PIECE X FORTNITE COLLAB THIS IS THE BIGGEST COLLAB IN FORTNITE HISTORY!

1/9 Current optimizers are static during training. What if the optimizer could think and react while the network trains? We let an agent watch the training dynamics and rewrite the optimizer's source code as training goes: a reactive optimizer. In every language and image setting we tried that has no tuned recipe, it matches or beats the best recipe one can one-shot, and it guards against loss spikes and overfitting by itself. In the near future, optimization decisions may belong to agents, not humans. 📝 💻 Try it on your task: 🧵

Need a good PC optimizer, who is the best for a reasonable price?

@SirChefLiam None. Pc optimizers are a scam. Turn on xmp in bios make sure ram is at correct speed Nvidia control panel set your gpu as priority Msi afterburner turn up power and temp limit to max nothing else. Game settings for comp fps = low.

HERE IT IS YALL!! ITS ALLLLL OF THE SHOP FR IM SO HAPPY 🥹😭

FOUR NBA UNDER MARKETS. +70.0u COMBINED. P+A: 187-129 | +28.0u PRA: 69-47 | +16.9u Assists: 130-78 | +16.1u Threes: 191-117 | +9.0u MSE Projections continue to CRUSH!

Pick Optimizers • Betting Models • Slip Builder

MACHINE LEARNING ENGINEER ROADMAP │ ├── 1. Programming Fundamentals │ ├── Python │ │ ├── Variables & Data Types │ │ ├── Loops & Conditionals │ │ ├── Functions & Modules │ │ ├── OOP & Exception Handling │ │ ├── List Comprehensions │ │ ├── File Handling │ │ └── Virtual Environments │ ├── Data Structures & Algorithms │ ├── Git & GitHub │ └── Linux & Command Line │ ├── 2. Mathematics for Machine Learning │ ├── Linear Algebra │ │ ├── Vectors & Matrices │ │ ├── Matrix Operations │ │ ├── Eigenvalues & Eigenvectors │ │ └── Matrix Decomposition │ ├── Calculus │ │ ├── Derivatives │ │ ├── Partial Derivatives │ │ ├── Gradients │ │ └── Chain Rule │ ├── Probability │ │ ├── Random Variables │ │ ├── Probability Distributions │ │ └── Bayes' Theorem │ └── Statistics │ ├── Mean, Median & Mode │ ├── Variance & Standard Deviation │ ├── Hypothesis Testing │ └── Correlation & Regression │ ├── 3. Data Analysis & Manipulation │ ├── NumPy │ ├── Pandas │ ├── Matplotlib │ ├── Seaborn │ ├── Data Cleaning │ ├── Data Transformation │ ├── Exploratory Data Analysis (EDA) │ ├── Missing Values & Outliers │ └── Feature Engineering │ ├── 4. SQL & Database Fundamentals │ ├── SQL Queries │ ├── Joins & Subqueries │ ├── Aggregations │ ├── Window Functions │ ├── PostgreSQL │ ├── Database Design │ └── Data Warehousing Basics │ ├── 5. Machine Learning Fundamentals │ ├── What Is Machine Learning? │ ├── Supervised Learning │ │ ├── Linear Regression │ │ ├── Logistic Regression │ │ ├── Decision Trees │ │ ├── Random Forest │ │ ├── Support Vector Machines │ │ └── K-Nearest Neighbors │ ├── Unsupervised Learning │ │ ├── K-Means Clustering │ │ ├── Hierarchical Clustering │ │ └── Principal Component Analysis │ ├── Reinforcement Learning Basics │ ├── Training & Testing Data │ ├── Cross-Validation │ ├── Overfitting & Underfitting │ ├── Regularization │ └── Hyperparameter Tuning │ ├── 6. Machine Learning Algorithms │ ├── Linear & Polynomial Regression │ ├── Naive Bayes │ ├── Gradient Boosting │ ├── XGBoost │ ├── LightGBM │ ├── CatBoost │ ├── Ensemble Learning │ └── Model Evaluation & Optimization │ ├── 7. Machine Learning Frameworks │ ├── Scikit-learn │ ├── XGBoost │ ├── LightGBM │ ├── CatBoost │ ├── Feature Pipelines │ ├── Model Serialization │ └── Custom Transformers │ ├── 8. Deep Learning │ ├── Neural Network Fundamentals │ ├── Perceptrons │ ├── Activation Functions │ ├── Forward & Backpropagation │ ├── Gradient Descent │ ├── Loss Functions │ ├── Optimizers │ ├── Convolutional Neural Networks (CNNs) │ ├── Recurrent Neural Networks (RNNs) │ ├── LSTMs & GRUs │ ├── Transformers │ └── Transfer Learning │ ├── 9. Deep Learning Frameworks │ ├── PyTorch │ │ ├── Tensors │ │ ├── Autograd │ │ ├── nn.Module │ │ ├── DataLoaders │ │ └── Training Loops │ ├── TensorFlow │ ├── Keras │ ├── GPU Acceleration │ └── Distributed Training Basics │ ├── 10. Natural Language Processing (NLP) │ ├── Text Preprocessing │ ├── Tokenization │ ├── Stemming & Lemmatization │ ├── Word Embeddings │ ├── Word2Vec & GloVe │ ├── Attention Mechanisms │ ├── Transformers │ ├── BERT & GPT Architectures │ ├── Hugging Face │ └── Fine-Tuning Language Models │ ├── 11. Computer Vision │ ├── Image Processing │ ├── Image Classification │ ├── Object Detection │ ├── Image Segmentation │ ├── OpenCV │ ├── YOLO │ ├── Vision Transformers │ └── Image Augmentation │ ├── 12. Generative AI & LLM Engineering │ ├── Large Language Models (LLMs) │ ├── Prompt Engineering │ ├── Embeddings │ ├── Vector Databases │ ├── Retrieval-Augmented Generation (RAG) │ ├── Semantic Search │ ├── LLM Fine-Tuning │ ├── LoRA & QLoRA │ ├── AI Agents │ ├── Model Evaluation │ └── LLM Inference Optimization │ ├── 13. MLOps & Model Deployment │ ├── ML Model Lifecycle │ ├── Experiment Tracking │ ├── MLflow │ ├── Weights & Biases │ ├── Model Versioning │ ├── Data Versioning (DVC) │ ├── Model Serving │ ├── FastAPI & Flask │ ├── REST APIs │ ├── Batch & Real-Time Inference │ ├── Model Monitoring │ └── Model Retraining │ ├── 14. Cloud & Infrastructure │ ├── AWS │ │ ├── Amazon S3 │ │ ├── EC2 │ │ ├── SageMaker │ │ └── Lambda │ ├── Google Cloud │ │ ├── Vertex AI │ │ └── BigQuery │ ├── Microsoft Azure ML │ ├── Docker │ ├── Kubernetes │ ├── CI/CD Pipelines │ ├── Terraform │ └── GPU & Cloud Computing │ ├── 15. Data Engineering for ML │ ├── ETL & ELT Pipelines │ ├── Apache Airflow │ ├── Apache Spark │ ├── Kafka │ ├── Data Lakes │ ├── Data Warehouses │ ├── Feature Stores │ └── Data Quality & Validation │ ├── 16. Model Optimization & Production │ ├── Model Quantization │ ├── Model Pruning │ ├── Knowledge Distillation │ ├── ONNX │ ├── TensorRT │ ├── Model Compression │ ├── Latency Optimization │ ├── Scalability │ └── Production Reliability │ ├── 17. Responsible AI & Security │ ├── AI Ethics │ ├── Bias & Fairness │ ├── Explainable AI (XAI) │ ├── SHAP & LIME │ ├── Data Privacy │ ├── Model Security │ ├── Adversarial Attacks │ └── AI Governance │ ├── 18. Build Real-World Projects │ ├── House Price Prediction System │ ├── Customer Churn Prediction │ ├── Fraud Detection System │ ├── Recommendation Engine │ ├── Spam Detection System │ ├── Stock Price Forecasting │ ├── Image Classification App │ ├── Object Detection System │ ├── Sentiment Analysis API │ ├── RAG-Based AI Assistant │ ├── AI Document Processing System │ └── End-to-End ML Production Pipeline │ └── 19. Career Preparation ├── Build a Strong GitHub Portfolio ├── Create an ML Engineering Resume ├── Master ML System Design ├── Practice Python & SQL Interviews ├── Solve ML Case Studies ├── Learn Model Deployment at Scale ├── Contribute to Open Source ├── Write Technical Articles └── Apply for Machine Learning Engineer Roles FINAL GOAL: Become a Production-Ready Machine Learning Engineer

Same building blocks, six different training games: next-token (GPT-3), masked (BERT), span corruption (T5), contrastive (CLIP), multi-token (DeepSeek-V3) and diffusion (Mercury), each shown on one sentence with when to use it. Which one do you use, and for what?

Next in the grid: the optimizers behind ResNet, the original Transformer, Llama, T5 and Kimi K2, and when to pick each one.

Adaptive Optimizers Can Get a Policy Ranking Wrong: Momentum, Dependent Data, and a Two-Run Remedy Sunyoung An, Xiaoming Huo [𝚜𝚝𝚊𝚝.𝙼𝙻 𝚌𝚜.𝙻𝙶 𝚖𝚊𝚝𝚑.𝙾𝙲 𝚖𝚊𝚝𝚑.𝚂𝚃]

Scaling Discovery requires Forgetting! 🌊 I am SUPER EXCITED to publish the 149th episode of the Weaviate Podcast featuring Shangyin Tan (@ShangyinT), a Ph.D. student @UCBerkeley! 🔥 Long-running optimizers stall when an agent remembers too much. Forgetting can get them exploring again! 🏔️ The story starts with GEPA and its successor, `optimize_anything`, which extends these ideas from prompt optimization to code, GPU kernels, and agent harnesses! 🚀 Shangyin explains why prompts and harnesses are really a generalization test, whereas a single kernel is pure optimization. I loved this nugget for understanding how optimization algorithms are extending from models to code. 💡 Running GEPA, Autoresearch, and Meta-Harness as backends behind `optimize_anything` resulted in every one of them plateauing a couple of hours in. As sessions grew, agents went from editing 100s of lines to 5 or 6 due to being stuck in a local optima. Handing only the best candidate to a fresh optimizer and forgetting the history got progress climbing again. 📈 We also discussed how to design Multi-Agent teams, the use of Git as a communication protocol for Multi-Agent AI, and how to handle expensive evals! 💸 This was a super conversation, I learned so much from Shangyin, and I really hope you find it interesting! YouTube: Spotify:

Adam dominates because it adapts learning rates per parameter, but it burns memory. SGD is lean. RMSprop splits the difference. Understanding these tradeoffs—not just picking a name—changes how you architect training. 12 optimizers broken down by what they actually do. #machinelearning #deeplearning #optimization

Who are the good Pc optimizers, preferably ones that can pass a background check..

remember learned optimizers? why isn’t anyone trying that again? neural networks as optimizers trained with RL

Would also like to see more research in that direction, have only seen a few recent things: 1. OPAL ("Reactive Optimizers") by @SimulatedAnneal et al — Agent monitors optimizer and edits its code while reasoning about training progress 2. OPRO ("LLMs as Optimizers") by @chengrun_yang et al — LLM sees previously-tested parameter & objective values and outputs new candidate parameters 3. NeuroGen by @jackqqwang, @YusenZhangNLP, Xi Li ("Neural Network Parameter Generation via LLMs") by — Fine-tunes LLM on neural-net checkpoints & task descriptions to one-shot generate the full parameters 4. OptoPrime ("Trace is the Next AutoDiff") by @chinganc_rl, @allenainie, @adith387 — LLM inspects computational graph & numerical feedback, then directly proposes updated parameter values These earlier works commented about struggling to scale their idea to big neural nets, plus frontier LLMs were much worse then