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Generalizable and Animatable Gaussian Head Avatar

🖥 Github: https://github.com/xg-chu/gagavatar

📕 Paper: https://arxiv.org/abs/2410.07971v1

@Machine_learn
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Artificial Intelligence A Modern Approach

📚 Book

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👍2🔥2
📃Fake news detection: A survey of graph neural network methods

📎 Study paper


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3👍2
UC Berkeley's "Machine Learning" lecture notes

📓 Book

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Probability and Statistics The Science of Uncertainty

📖 book

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با عرض سلام خيلي از دوستان در رابطه با طراحي صفر تا صد پروژه هاي ديپ از بنده سوال پرسيدن داخل پك زير ٣٦ پروژه رو با جزئيات شرح دادم:

1-Deep Learning Basic
-01_Introduction
--01_How_TensorFlow_Works
2-Classification apparel
-Classification apparel double capsule
-Classification apparel double cnn
3-ALZHEIMERS USING CNN(ResNet)
4-Fake News (Covid-19 dataset)
-Multi-channel
-3DCNN model
-Base line+ Char CNN
-Fake News Covid CapsuleNet
5-3DCNN Fake News
6-recommender systems
-GRU+LSTM MovieLens
7-Multi-Domain Sentiment Analysis
-Dranziera CapsuleNet
-Dranziera CNN Multi-channel
-Dranziera LSTM
8-Persian Multi-Domain SA
-Bi-GRU Capsule Net
-Multi-CNN
9-Recommendation system
-Factorization Recommender, Ranking Factorization Recommender, Item Similarity Recommender (turicreate)
-SVD, SVD++, NMF, Slope One, k-NN, Centered k-NN, k-NN Baseline, Co-Clustering(surprise)
10-NihX-Ray
-optimized CNN on FullDataset Nih-Xray
-MobileNet
-Transfer learning
-Capsule Network on FullDataset Nih-Xray
دوستاني كه نياز به اين پروژه ها دارن ميتونن با بنده در ارتباط باشن.
@Raminmousa
@Machine_learn
👍71
Efficiently Democratizing Medical LLMs for 50 Languages via a Mixture of Language Family Experts

💻 Github: https://github.com/freedomintelligence/apollomoe

🔖 Paper: https://arxiv.org/abs/2410.10626v1

🤗 Dataset: https://paperswithcode.com/dataset/mmlu

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Neural Networks and Deep Learning

📓 book

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👍2
Thesis2 2.pdf
5.5 MB
Thesis: Yolo object detection

این پروژه سال ۲۰۲۰ با یکی از دوستان انجام دادیم که هدف تشخیص وزن پل با استفاده از Yolo بود. جزئیات مدل یولو رو داخل این بررسی کردیم . برای دوستانی که می خوان بیشتر این مدل رو بررسی کنن می تونه مفید باشه.
@Machine_learn
🔥3
📃Network Modeling and Control of Dynamic Disease Pathways, Review and Perspectives


📎 Study the paper

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📑 Nine quick tips for open meta-analyses


📎 Study the paper

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Algebraic topology for physicists

📓 Book

@Machine_learn
👍2
✔️ LVD-2M: A Long-take Video Dataset with Temporally Dense Captions

New pipeline for selecting high-quality long-take videos and generating temporally dense captions.

Dataset with four key features essential for training long video generation models: (1) long videos covering at least 10 seconds, (2) long-take videos without cuts, (3) large motion and diverse contents, and (4) temporally dense captions.

🖥 Github: https://github.com/silentview/lvd-2m

📕 Paper: https://arxiv.org/abs/2410.10816v1

🖥 Dataset: https://paperswithcode.com/dataset/howto100m

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Forwarded from Github LLMs
🔥 NVIDIA silently release a Llama 3.1 70B fine-tune that outperforms
GPT-4o and Claude Sonnet 3.5


Llama 3.1 Nemotron 70B Instruct a further RLHFed model on
huggingface


https://huggingface.co/collections/nvidia/llama-31-nemotron-70b-670e93cd366feea16abc13d8
https://www.tg-me.com/deep_learning_proj
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Prompt Engineering Techniques: Comprehensive Repository for Development and Implementation 🖋️

📓 Github

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🌟 Zamba2-Instruct

🟢Zamba2-1.2B-instruct;
🟠Zamba2-2.7B-instruct.


# Clone repo
git clone https://github.com/Zyphra/transformers_zamba2.git
cd transformers_zamba2

# Install the repository & accelerate:
pip install -e .
pip install accelerate

# Inference:
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

tokenizer = AutoTokenizer.from_pretrained("Zyphra/Zamba2-2.7B-instruct")
model = AutoModelForCausalLM.from_pretrained("Zyphra/Zamba2-2.7B-instruct", device_map="cuda", torch_dtype=torch.bfloat16)

user_turn_1 = "user_prompt1."
assistant_turn_1 = "assistant_prompt."
user_turn_2 = "user_prompt2."
sample = [{'role': 'user', 'content': user_turn_1}, {'role': 'assistant', 'content': assistant_turn_1}, {'role': 'user', 'content': user_turn_2}]
chat_sample = tokenizer.apply_chat_template(sample, tokenize=False)

input_ids = tokenizer(chat_sample, return_tensors='pt', add_special_tokens=False).to("cuda")
outputs = model.generate(**input_ids, max_new_tokens=150, return_dict_in_generate=False, output_scores=False, use_cache=True, num_beams=1, do_sample=False)
print((tokenizer.decode(outputs[0])))


🖥GitHub


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📄 Advances of Artificial Intelligence in Anti-Cancer Drug Design: A Review of the Past Decade



📎 Study the paper

@Machine_learn
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Forwarded from Papers
يكي از بهترين موضوعات در طبقه بندي متن؛ تحليل احساس چند دامنه اي مي باشد. براي اين منظور مدلي تحت عنوان
Title: TRCAPS: The Transformer-based Capsule Approach for Persian Multi-
Domain Sentiment Analysis
طراحي كرديم كه نتايج خيلي بهتري نسبت به IndCaps داشته است.
دوستاني كه نياز به مقاله تو حوزه NLP دارن مي تونن تا اخر اين هفته داخل اين مقاله شركت كنند.

ژورنال هدف Array elsevier مي باشد.

شركت كنندگان داخل اين مقاله نياز به انجام تسك هايي نيز مي باشند.

@Raminmousa
@Machine_learn
@Paper4money
👍4
2025/07/10 02:49:21
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