#machinelearningframework 搜索结果

Shraddha Bharuka

4 小时

🚀 𝗠𝗮𝗰𝗵𝗶𝗻𝗲 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 𝗡𝗼𝘁𝗲𝘀 (𝗛𝗮𝗻𝗱𝘄𝗿𝗶𝘁𝘁𝗲𝗻 𝗣𝗗𝗙) Master the core of 𝗠𝗮𝗰𝗵𝗶𝗻𝗲 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 —algorithms, models, training, evaluation & real-world examples. Perfect for interviews & AI enthusiasts! 🤖📊 📘FREE for the first 500 people! 1.…

BharukaShraddha's tweet image. 🚀 𝗠𝗮𝗰𝗵𝗶𝗻𝗲 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 𝗡𝗼𝘁𝗲𝘀 (𝗛𝗮𝗻𝗱𝘄𝗿𝗶𝘁𝘁𝗲𝗻 𝗣𝗗𝗙)

Master the core of 𝗠𝗮𝗰𝗵𝗶𝗻𝗲 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 —algorithms, models, training, evaluation &amp; real-world examples. Perfect for interviews &amp; AI enthusiasts! 🤖📊

📘FREE for the first 500 people!

1.…

Wenli Xiao

@_wenlixiao

54 分钟

What if robots could improve themselves by learning from their own failures in the real-world? Introducing 𝗣𝗟𝗗 (𝗣𝗿𝗼𝗯𝗲, 𝗟𝗲𝗮𝗿𝗻, 𝗗𝗶𝘀𝘁𝗶𝗹𝗹) — a recipe that enables Vision-Language-Action (VLA) models to self-improve for high-precision manipulation tasks. PLD…

Beginnersblog

@beginnersblog1

年10月29日

8 Machine Learning Algorithms ( A quick revision) 👇 1. Linear Regression: Predicts numbers (e.g., house prices). Straight line fit. Easy, but sensitive to outliers. 2. Logistic Regression: Predicts categories (e.g., spam/not spam). Uses probabilities. Great for classification.…

beginnersblog1's tweet image. 8 Machine Learning Algorithms ( A quick revision) 👇

1. Linear Regression: Predicts numbers (e.g., house prices). Straight line fit. Easy, but sensitive to outliers.

2. Logistic Regression: Predicts categories (e.g., spam/not spam). Uses probabilities. Great for classification.…

Karan🧋

@kmeanskaran

2 小时

Sunday 10 PM IST Publishing an article on tackling machine learning problems. I'm covering step-by-step strategies to follow in general for most production-level ML projects. Be an elite problem solver by applying a basic framework. This will be a go-to guide to start solving…

kmeanskaran's tweet image. Sunday 10 PM IST

Publishing an article on tackling machine learning problems. I'm covering step-by-step strategies to follow in general for most production-level ML projects.

Be an elite problem solver by applying a basic framework. This will be a go-to guide to start solving…

Applied Compute

@appliedcompute

年10月29日

Generalists are useful, but it’s not enough to be smart. Advances come from specialists, whether human or machine. To have an edge, agents need specific expertise, within specific companies, built on models trained on specific data. We call this Specific Intelligence. It's…

appliedcompute's tweet image. Generalists are useful, but it’s not enough to be smart.

Advances come from specialists, whether human or machine.

To have an edge, agents need specific expertise, within specific companies, built on models trained on specific data.

We call this Specific Intelligence.

It's…

pdawg

@prathamgrv

年10月29日

Introducing TensorTonic. A platform to actually learn ML by building it. > Practice ML algorithms in LC style > Replicate cutting-edge ML papers > Prepare with company-wise AI interview questions + blogs. v1 is out. accelerating hard. website link in comments.

Probability and Statistics

@probnstat

年10月25日

A machine learning system is the complete infrastructure, or MLOps pipeline, that brings a model to life in the real world. It’s not just the trained model itself. It includes automated data pipelines for ingestion and cleaning, the training and validation infrastructure, and an…

probnstat's tweet image. A machine learning system is the complete infrastructure, or MLOps pipeline, that brings a model to life in the real world. It’s not just the trained model itself. It includes automated data pipelines for ingestion and cleaning, the training and validation infrastructure, and an…

Tw93

@HiTw93

18 小时

Introduction to Machine Learning Systems，源自哈佛教授 Vijay Janapa Reddi 一个开源教材，教你从边缘设备到云端，动手搭建真实可用的 AI 系统。不再只停留在训练模型，而是学会怎么让模型真正跑起来、跑得稳、跑得快。 mlsysbook.ai

HiTw93's tweet image. Introduction to Machine Learning Systems，源自哈佛教授 Vijay Janapa Reddi 一个开源教材，教你从边缘设备到云端，动手搭建真实可用的 AI 系统。不再只停留在训练模型，而是学会怎么让模型真正跑起来、跑得稳、跑得快。
mlsysbook.ai

Daily Dose of Data Science

@DailyDoseOfDS_

年9月9日

4 machine learning training paradigms, explained visually: - Transfer Learning - Fine-tuning - Multi-task Learning - Federated Learning

Ambrosio

@__ambrosio

年10月18日

Se estiver iniciando em Machine Learning, salva esse repositório, pois ele contém uma lista com as principais frameworks, bibliotecas e softwares utilizados em Machine Learning. Ele é organizado por categorias, incluindo: • Deep Learning • Visão Computacional • Processamento…

__ambrosio's tweet image. Se estiver iniciando em Machine Learning, salva esse repositório, pois ele contém uma lista com as principais frameworks, bibliotecas e softwares utilizados em Machine Learning.

Ele é organizado por categorias, incluindo:

• Deep Learning
• Visão Computacional
• Processamento…

Kimi.ai

@Kimi_Moonshot

年10月30日

Kimi Linear Tech Report is dropped! 🚀 huggingface.co/moonshotai/Kim… Kimi Linear: A novel architecture that outperforms full attention with faster speeds and better performance—ready to serve as a drop-in replacement for full attention, featuring our open-sourced KDA kernels! Kimi…

moonshotai/Kimi-Linear-48B-A3B-Instruct · Hugging Face

来源: huggingface.co

Kirk Borne

@KirkDBorne

年10月25日

Practical #MachineLearning for #ComputerVision — End-to-End ML for Images: amzn.to/4ajfVSf ———— #ML #DataScience #AI #DeepLearning #NeuralNetworks #DataScientist

KirkDBorne's tweet image. Practical #MachineLearning for #ComputerVision — End-to-End ML for Images: amzn.to/4ajfVSf
————
#ML #DataScience #AI #DeepLearning #NeuralNetworks #DataScientist

Shashank | 𝔽rAI

@0xshai

年10月28日

After the trading benchmark comes the Poker benchmark. A strong example of reinforcement learning combined with LLMs using solvers. I firmly believe LLMs should leverage tools to solve problems rather than try to learn everything from scratch and Poker is a good space to test…

0xshai's tweet image. After the trading benchmark comes the Poker benchmark. A strong example of reinforcement learning combined with LLMs using solvers. I firmly believe LLMs should leverage tools to solve problems rather than try to learn everything from scratch and Poker is a good space to test…

Tanishq Mathew Abraham, Ph.D.

@iScienceLuvr

9 小时

Defeating the Training-Inference Mismatch via FP16 Quick summary: A big problem in RL LLM training is that typical policy gradient methods expect the model generating the rollouts and the model being trained are exactly the same... but when you have a separate inference server…

iScienceLuvr's tweet image. Defeating the Training-Inference Mismatch via FP16

Quick summary: A big problem in RL LLM training is that typical policy gradient methods expect the model generating the rollouts and the model being trained are exactly the same... but when you have a separate inference server…

Gradient

@Gradient_HQ

年10月29日

We’re excited to partner with @LMSYSOrg’s SGLang to bring LLM serving to everyone. 🔥 Parallax is a high-concurrency inference framework that lets anyone host LLMs of all sizes across heterogeneous GPUs and devices — local or remote, anywhere in the world. SGLang @LMSYSOrg…

Gradient_HQ's tweet image. We’re excited to partner with @LMSYSOrg’s SGLang to bring LLM serving to everyone. 🔥

Parallax is a high-concurrency inference framework that lets anyone host LLMs of all sizes across heterogeneous GPUs and devices — local or remote, anywhere in the world.

SGLang @LMSYSOrg…

marmik

@marmikch

年10月29日

What if bigger models are actually more interpretable? Mixture of Experts (MoE) have become central to scaling LLMs and are used in nearly every frontier model. Yet we still lack a mechanistic understanding of how MoEs represent features differently than dense models. How do…

marmikch's tweet image. What if bigger models are actually more interpretable?

Mixture of Experts (MoE) have become central to scaling LLMs and are used in nearly every frontier model.

Yet we still lack a mechanistic understanding of how MoEs represent features differently than dense models.

How do…

HackerNoon | Learn Any Technology

@hackernoon

年11月24日

In this work, we consider a three-stage training procedure: Alignment training, I-GPT training, and Supervised Fine-tuning. - hackernoon.com/the-model-trai… #machinelearningframework #dreamllm

hackernoon's tweet card. In this work, we consider a three-stage training procedure: Alignment training, I-GPT training, and Supervised Fine-tuning.

The Model Training DreamLLM Underwent: Its Origin Story | HackerNoon

来源: hackernoon.com

HackerNoon | Learn Any Technology

@hackernoon

年11月24日

During training, DREAMLLM is trained to predict this token that indicates where an image emerges, and the conditional image synthesis is performed afterward - hackernoon.com/how-dreamllm-g… #machinelearningframework #dreamllm

hackernoon.com

How DreamLLM Generates an Image On Its Own "Free Will" | HackerNoon

During training, DREAMLLM is trained to predict this token that indicates where an image emerges, and the conditional image synthesis is performed afterward

来源: hackernoon.com

HackerNoon | Learn Any Technology

@hackernoon

年11月23日

Multimodal signals typically exhibit modality-specific information that has distinct structure but complementary semantics (Dong et al., 2023). - hackernoon.com/using-mllms-fo… #machinelearningframework #dreamllm

hackernoon's tweet card. Multimodal signals typically exhibit modality-specific information that has distinct structure but complementary semantics (Dong et al., 2023).

Using MLLMs for Diffusion Synthesis That Synergizes Both Sides: How Is This Possible? | HackerNoon

来源: hackernoon.com

HackerNoon | Learn Any Technology

@hackernoon

年11月23日

Here's all the background on DreamLLM, a new learning framework. - hackernoon.com/get-to-know-mo… #machinelearningframework #dreamllm

hackernoon's tweet card. Here's all the background on DreamLLM, a new learning framework.

Get to Know More About DreamLLM: The Background on This Learning Framework | HackerNoon

来源: hackernoon.com

HackerNoon | Learn Any Technology

@hackernoon

年11月23日

This paper presents DREAMLLM, a learning framework that first achieves versatile Multimodal Large Language Models empowered with frequently overlooked synergy. - hackernoon.com/what-is-dreaml… #machinelearningframework #dreamllm

hackernoon's tweet card. This paper presents DREAMLLM, a learning framework that first achieves versatile Multimodal Large Language Models empowered with frequently overlooked synergy.

What Is DreamLLM? Everything You Need to Know About the Learning Framework | HackerNoon

来源: hackernoon.com

Top Software Companies

@topsoftwarefirm

2022年5月17日

Flask vs. Django: Which Machine Learning Framework is Better? #flaskvsdjango #machinelearningframework #machinelearningdevelopment #flask #django #machinelearning #artificialintelligence #softwaredevelopmentcompanies topsoftwarecompanies.co/web-developmen…

ThinkPalm

@ThinkPalm

2021年7月7日

Researchers from Germany and Chile have developed a new #machinelearningframework capable of evaluating whether a person is intoxicated, based on near infra-red images of their eyes. Read more: bit.ly/3yLjT2P via @UniteAi #machinelearning #healthcare

ThinkPalm's tweet image. Researchers from Germany and Chile have developed a new #machinelearningframework capable of evaluating whether a person is intoxicated, based on near infra-red images of their eyes. Read more: bit.ly/3yLjT2P via @UniteAi #machinelearning #healthcare

☁️⚡Cloud Native DEVS

@native_stack

2020年7月26日

Episode 1.1: Intro and building a machine learning framework #machinelearningframework youtube.com/watch?v=ArygUB…

Source Soft Solutions

@SourceSoftSol

2020年1月27日

#PyTorch is the most popular #machinelearningframework. The latest version allows developers to use fine-grain customization of build scripts, optimize library sizes and reduce the on-device footprint. Stay tuned for more tech news! bit.ly/37ulAEz #Facebook #sourcesoft