با عرض سلام موضوعات پيشنهادي تز
برای دوستانی که نیاز دارن در ادامه اورده شده است.


master thesis

پيش بيني بار كوتاه مدت با استفاده از رويكردهاي يادگيري تركيبي

طبقه بندي رضايت مشتريان بانكي و موسسات اعتباري با استفاده از رويكردهاي بازگشتي

طبقه بندي اخبار جعل با استفاده از رويكرد تنسور سه بعدي و bert

پيشبيني قيمت سهام با استفاده از اطلاعات تويتر و ماركت

پيش بيني قيمت crypto با استفاده از اطلاعات hashrate

phd thesis

بهبود رویکردهای یادگیری عمیق بر روی اخبار جعل و شایعات

بهبود رویکرد های یادگیری عمیق ترکیبی جهت دستیابی به پورتوفولی بهینه

بهبود رویکردهای ترکیبی یادگیری عمیق برای طبقه بندی crypto با استفاده از اطلاعات hashrate

ارائه رویکردهای مبتنی بر وزن دهی غیر تصادفی در یادگیری عمیق

بهبود یادگیری انتقالی در سری زمانی

ارائه مدل های انتقالی برای طبقه بندی های سری زمانی

جهت مشاوره موضوعات می تونین با بنده در ارتباط باشین


@Raminmousa

imbalanced-DL: Deep Imbalanced Learning in Python

🖥 Github: https://github.com/ntucllab/imbalanced-dl

📕 Paper: https://arxiv.org/pdf/2308.15457v1.pdf

🔥 Dataset: https://paperswithcode.com/dataset/cifar-10

@Machine_learn

✅ LISA: Reasoning Segmentation via Large Language Model

New segmentation task -- reasoning segmentation. The task is designed to output a segmentation mask given a complex and implicit query text.


🖥 Github: https://github.com/dvlab-research/lisa

📕 Paper: https://arxiv.org/abs/2308.00692v2

☑️ Dataset: https://github.com/dvlab-research/lisa#dataset

@Machine_learn

🎲 Anti-Exploration by Random Network Distillation, Tinkoff Research, ICML 2023

We propose a new ensemble-free offline RL algorithm called SAC-RND. We evaluate our method on the D4RL (Fu et al., 2020) benchmark, and show that SAC-RND achieves performance comparable to ensemble-based methods while outperforming ensemble-free approaches.


🖥 Github: https://github.com/tinkoff-ai/sac-rnd

🤓 Paper: https://proceedings.mlr.press/v202/nikulin23a.html

@Machine_learn

Book: Machine Learning: The Basics
Authors: Alexander Jung
ISBN: -
year: 2023
pages: 287
Tags:#ML
@Machine_learn

🚀 AgentBench: Evaluating LLMs as Agents.

AgentBench, a multi-dimensional evolving benchmark that currently consists of 8 distinct environments to assess LLM-as-Agent's reasoning and decision-making abilities in a multi-turn open-ended generation setting.


🖥 Github: https://github.com/thudm/agentbench

📕 Paper: https://arxiv.org/abs/2308.03688v1

☑️ Dataset: https://paperswithcode.com/dataset/alfworld

@Machine_learn

✅ SSLRec: A Self-Supervised Learning Library for Recommendation

SSLRec, a novel benchmark platform that provides a standardized, flexible, and comprehensive framework for evaluating various SSL-enhanced recommenders.


🖥 Github: https://github.com/hkuds/sslrec

📕 Paper: https://arxiv.org/abs/2308.05697v1

⛓ Models: https://github.com/HKUDS/SSLRec/blob/main/docs/Models.md

☑️ Datasets: https://github.com/HKUDS/SSLRec/blob/main/docs/Models.md

ai_machinelearning_big_data

LightTBNet

🖥 Github: https://github.com/dani-capellan/LightTBNet

📕 Paper: https://arxiv.org/pdf/2309.02140v1.pdf

🔥 Dataset: https://paperswithcode.com/dataset/montgomery-county-x-ray-set

@Machine_learn

LLM_Fine_Tuning_Molecular_Properties

🖥 Github: https://github.com/SylwiaNowakowska/LLM_Fine_Tuning_Molecular_Properties

📕 Paper: https://chemrxiv.org/engage/api-gateway/chemrxiv/assets/orp/resource/item/65030b55b338ec988a780108/original/chem-ber-ta-2-fine-tuning-for-molecule-s-hiv-replication-inhibition-prediction.pdf

🔥 Dataset: https://paperswithcode.com/dataset/moleculenet

@Machine_learn

🚀 Gold-YOLO: Efficient Object Detector via Gather-and-Distribute Mechanism

Gold-YOLO, which boosts the multi-scale feature fusion capabilities and achieves an ideal balance between latency and accuracy across all model scales.


🖥 Github: https://github.com/huawei-noah/Efficient-Computing/tree/master/Detection/Gold-YOLO

📕 Paper: https://arxiv.org/abs/2309.11331v2

⏩ Dataset: https://paperswithcode.com/dataset/coco

@Machine_learn

InstructionERC

🖥 Github: https://github.com/LIN-SHANG/InstructERC

📕 Paper: https://arxiv.org/pdf/2309.11911v1.pdf

🔥 Dataset: https://paperswithcode.com/dataset/iemocap

@Machine_learn

This channels is for Programmers, Coders, Software Engineers.

0- Python
1- Data Science
2- Machine Learning
3- Data Visualization
4- Artificial Intelligence
5- Data Analysis
6- Statistics
7- Deep Learning
8- programming Languages

✅ Data Science Channels:
https://t.iss.one/addlist/8_rRW2scgfRhOTc0

✅ Main Channel:
https://t.iss.one/DataScienceM

🗣 Leveraging In-the-Wild Data for Effective Self-Supervised Pretraining in Speaker Recognition


pip3 install wespeakerruntime

🖥 Github: https://github.com/wenet-e2e/wespeaker

📕 Paper: https://arxiv.org/abs/2309.11730v1

⏩ Demo: https://huggingface.co/spaces/wenet/wespeaker_demo

⭐️ Dataset: https://paperswithcode.com/dataset/wenetspeech

@Machine_learn

🎓 BayesDLL: Bayesian Deep Learning Library

New Bayesian neural network library for PyTorch for large-scale deep network


🖥 Github: https://github.com/samsunglabs/bayesdll

📕 Paper: https://arxiv.org/abs/2309.12928v1

⭐️ Dataset: https://paperswithcode.com/dataset/oxford-102-flower

@Machine_learn

Book: ARTIFICIAL INTELLIGENCE (SUBJECT CODE 417) CLASS – 3
Authors: Orange Education Pvt Ltd
ISBN: Null
year: 2023
pages: 619
Tags:#AI
@Machine_learn

LongLoRA: Efficient Fine-tuning of Long-Context Large Language Models

🖥 Github: https://github.com/dvlab-research/longlora

📕 Paper: https://arxiv.org/pdf/2309.12307v1.pdf

🔥 Dataset: https://paperswithcode.com/dataset/pg-19

@Machine_learn

➕ fastMONAI: A low-code deep learning library for medical image analysis

Simplifying deep learning for medical imaging.


git clone https://github.com/MMIV-ML/fastMONAI

🖥 Github: https://github.com/MMIV-ML/fastMONAI

Project: https://fastmonai.no

📕 Paper: https://www.sciencedirect.com/science/article/pii/S2665963823001203

🖥 Colab: https://colab.research.google.com/github/MMIV-ML/fastMONAI/blob/master/nbs/10a_tutorial_classification.ipynb

@Machine_learn

Book: Quantum Mechanics and
Bayesian Machines
Authors: George Chapline
Lawrence Livermore National Laboratory, USA
ISBN: Null
year: 2023
pages: 194
Tags:#QM #BM
@Machine_learn

Privacy-preserving in-context learning with differentially private few-shot generation

🖥 Github: https://github.com/microsoft/dp-few-shot-generation

📕 Paper: https://arxiv.org/pdf/2309.11765v1.pdf

🔥 Dataset: https://paperswithcode.com/dataset/ag-news

@Machine_learn

Book: Developing Apps with GPT-4 and
ChatGPT
Authors: Build Intelligent Chatbots, Content Generators, and More
ISBN: 978-1-098-15248-2
year: 2023
pages: 117
Tags:#GPT
@Machine_learn