2. Machine Learning with Python Topics: • ML problem... principles • Implement & analyze models • Choosing suitable models for different apps • ML project implementation: Training, validation, tuning, and feature engineering 🔗show more

1. Machine Learning Specialization. Break Into AI with this... 3-course program by Andrew Ng. What You’ll learn: → Build ML models → Train neural networks → Deep reinforcement learning → Unsupervised learning techniques 🔗show more

Epic Games released the Machine Learning Deformer Sample for... Unreal Engine 5.2, which demonstrates how the engine's ML technology can help create a character with deformations driven by full muscle, flesh, and cloth simulation. #ml #UnrealEngine5show more

1. Machine Learning Specialization Break into AI with this... 3-course program by Andrew Ng. What you’ll learn: → Build machine learning models → Train neural networks → Deep reinforcement learning → Unsupervised learning techniques 🔗show more

The ML Intern can now ping you on Slack... when it's finished training models, generating datasets, or compiling an analysis of the 1000 ablations you launched 💸show more

make your python endpoints reactive + auto generate ts... types for models & api endpoints, releasing soon. FastAPI #pythonshow more

there is a rich and under-tapped paradigm where LLMs... help you learn fundamentals topics in computer science which I think the folks at deep-ml have integrated beautifully for ML in a very engaging way this learning corner of the internet has became one of my favoriteshow more

This Quant bot turned $1.4K → $203K in 3... months using a self-trained ML model i traced his 55K predictions → uploaded into Codex 5.5 → connected Hermes agent installed it on a VPS + connected Binance + Synth ML models API 3 days → 343% ROI run trading agent in 5 steps: • rent a VPS on Hetzner - $5.99 • install Hermes CLI using one-liner code - free • connect Codex 5.5 + TG bot + Polymarket API • provide Synth Data API's for crypto predictions • sent Hermes step-by-step prompts from article start small 1-2$ give Hermes least {50-100} trades to build self-learning skills based on Synth ML models self-learning agent + crypto predictions models = best combination for building algo-trading setup bot profile: s tart copy-trading it with even with $5 using Ares: read full article below to build your first trading agent ↓show more

Decentralized Diffusion Models power stronger models trained on more... accessible infrastructure. DDMs mitigate the networking bottleneck that locks training into expensive and power-hungry centralized clusters. They scale gracefully to billions of parameters and generate photorealistic images with just a week of training on eight independent GPU nodes. They’re easy to implement, adopt DiT hyperparameters directly and outperform standard models FLOP-for-FLOP.show more

𝑩𝑼𝑻 𝑾𝑯𝑨𝑻 𝑰𝑭 𝑻𝑯𝑬 𝑯𝑬𝑨𝑻𝑬𝑹 𝑵𝑬𝑽𝑬𝑹 𝑺𝑻𝑶𝑷𝑺 ?!? —... SoBet ✅- Phillies ML F7 (-125) | 2U ✅- Phillies ML F7 (-127) ✅- Phillies ML F7 (-119) | 2U ✅- Phillies ML F7 (-115) ✅- Phillies ML F7 (-114) ✅- Phillies ML F7 (-115) ✅- Phillies ML F7 (-114) ✅- Phillies ML F7 (-112) ✅- Phillies ML F7 (-113) ✅- Phillies ML F7 (-112) ✅- Phillies ML F7 (-114) ✅- Phillies ML F7 (-112) ✅- Phillies ML F7 (-111) ✅- Phillies ML F7 (-112) ✅- Phillies ML (-131) | 3U ✅- Phillies ML (-130) ✅- Phillies ML (-128) ✅- Phillies ML (-120) | 2U ✅- Phillies ML (-118) | 2U ✅- Phillies ML (-117) | 2U ✅- Phillies ML (-111) | 2U ✅- Phillies ML (-111) ✅- Phillies ML (-110) ✅- Phillies ML (-111) ✅- Phillies ML (-110) ✅- Phillies ML (-111) ✅- Phillies ML (-112) ✅- Phillies ML (-113) ✅- Phillies ML (-112) ✅- Phillies ML (-113) ✅- Phillies ML (-112) ✅- Phillies ML (-113) ✅- Phillies ML (-112) ✅- Phillies ML (-113) ✅- Phillies ML (-112) I’m doing it for NASTY NATION !!! Nasty 🇺🇸 Coop GutsyPicksCashshow more

make a mech surface texture with COPs ✌️ -... use the texture with different UV scales for the same 2 models - adjust the displacement map heights to overlap 2 models #houdini #copernicus #apexrigshow more

😍⚠️And it’s out in J Cell Science!! Our review... on the opportunities and challenges of using Machine Learning in Microscopy research 🤖🔬! From using ML to curate data to using ML to give us more KNOWLEDGE about our data (i.e. Interpretation 😉) DOI: (1/n) ⬇️show more

This is the coolest thing you'll see today! Drawdata... is a python library that allows you to draw a 2-D dataset of any shape in a Jupyter Notebook. Very handy for learning & understanding the behaviour of ML algorithms! Check this out👇show more

been learning more ML fundamentals as what i had... learned during uni left us around RNNs and LSTMs– was pleasantly surprised to find the Ilya 30~ papers for modern ML essentially walks you through to attention mechanisms and transformers from there!show more

many parts of Hugging Face Hub is actually powered... by open machine learning models 🥹 translation feature is one of them, it uses a very tiny (600M) multilingual translation model by AI at Meta 💗show more

The nice part about Google AI that no one... is really talking about: Model garden It has so many foundational models anyone can tune/use with 1 click Now a small company with a single dev can utilize cutting edge ML models right inside of gcp, with 0 data privacy issues.show more

Short, cuter ML with 2 potential love interests, with... one of them being taller and grumpy? Ok.. #FanServiceshow more

Meta just announced that Code Llama was now free... for both research and commercial. This might the strongest competitor to ChatGPT: ▸ Can generate, explain, and debug your code ▸ Handles input 100,000 tokens ▸ Free for research + commercial use ▸ Outperforms most open models ▸ Comes in 7B, 13B, and 34B ▸ Supports Python, C++, Java, PHP, Typescript (Javascript), C#, and Bash Available in: ▸ Foundation base models (Code Llama) ▸ Python specializations (Code Llama - Python), ▸ Instruction-following models (Code Llama - Instruct)show more

First fully ML-framework-free 3D Gaussian Splatting implementation in LichtFeld... Studio. I’ve completed the migration of the full training pipeline to a custom CUDA-based tensor library. No PyTorch, no LibTorch, no autograd. Every gradient is implemented by hand, either through CUDA kernels or minimal abstractions on top. This makes it the first full training setup for 3D Gaussian Splatting with zero dependencies on existing ML frameworks. It’s not just about independence, it's about control! We now manage every byte of GPU memory, which opens the door to tighter optimization and finer performance tuning. The framework footprint is minimal, without pulling in gigabytes of ML runtime code that was never designed for real-time or graphics-driven applications. A few modules, such as the metrics and 3DGUT interfaces, are still being ported, and some operations are temporarily naïve, so performance is not yet on par with master. But this refactor lays the groundwork for: - A fully self-contained binary - Fine-grained memory optimization - Easier experimentation without the weight of an ML stack We’re getting close.show more