uilding Skills in Object-Oriented Design,
Step-by-Step Construction of A Complete Application
This is release 4.2003, published Mar 04, 2020.
Link:https://slott56.github.io/building-skills-oo-design-book/build/html/
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Step-by-Step Construction of A Complete Application
This is release 4.2003, published Mar 04, 2020.
Link:https://slott56.github.io/building-skills-oo-design-book/build/html/
@Machine_learn
⚡️ DBRX, a groundbreaking open-source Large Language Model (LLM) with a staggering 132 billion parameters.
▪Github: https://github.com/databricks/dbrx
▪HF: https://huggingface.co/databricks/dbrx-base
▪Demo: https://huggingface.co/spaces/databricks/dbrx-instruct
▪Docs: https://docs.databricks.com/en/machine-learning/foundation-models/index.html
@Machine_learn
▪Github: https://github.com/databricks/dbrx
▪HF: https://huggingface.co/databricks/dbrx-base
▪Demo: https://huggingface.co/spaces/databricks/dbrx-instruct
▪Docs: https://docs.databricks.com/en/machine-learning/foundation-models/index.html
@Machine_learn
Forwarded from Papers
Title
A Comparative Analysis of Machine Learning Models on Cryptocurrency Encompassing Indicators of Gold, Dollar, And Technical Indicators
————————————
Short title
Time series forecasting, ML, Gradient Boost Machine, BTC, cryptocurrency.
————————————-
Abstract
In recent years, the application of machine learning models in financial forecasting has gained significant traction due to their ability to capture complex patterns in diverse datasets. This study presents a comprehensive comparison of several prominent machine learning algorithms, including XGBoost, AdaBoost, CatBoost, Random Forest, Decision Trees and LightGBM, across different datasets encompassing indicators of gold, dollar, and technical indicators. The evaluation is conducted on a range of performance metrics to ascertain the efficacy of each model in predicting financial trends and fluctuations. Through ML analysis, we examine the models' capabilities in handling the unique characteristics and dynamics inherent in each dataset, providing insights into their relative strengths and weaknesses. Furthermore, this research contributes to the existing literature by offering a comparative framework for assessing the suitability of machine learning algorithms in financial forecasting tasks. The findings of this study have implications for practitioners and researchers seeking to employ machine learning techniques in financial markets, aiding in informed decision-making and risk management strategies.
—————————————
Field
Business, Marketing, Industrial Engineering, Computer Engineering.
——————————————
journal
1. Annals of Operations Research (7.1 CiteScore, 4.8 Impact Factor)
2. Neural Computing and Applications ( 8.7 CiteScore, 6.0 Impact Factor)
3. IEEE Access (9.0 CiteScore, 3.9 Impact Factor)
با عرض سلام نفرات اول و دوم این مقاله رو خالی داریم . دوستانی که نیاز دارن با بنده هماهنگ کنند.
▶️ @Raminmousa
@Machine_learn
@Paper4money
A Comparative Analysis of Machine Learning Models on Cryptocurrency Encompassing Indicators of Gold, Dollar, And Technical Indicators
————————————
Short title
Time series forecasting, ML, Gradient Boost Machine, BTC, cryptocurrency.
————————————-
Abstract
In recent years, the application of machine learning models in financial forecasting has gained significant traction due to their ability to capture complex patterns in diverse datasets. This study presents a comprehensive comparison of several prominent machine learning algorithms, including XGBoost, AdaBoost, CatBoost, Random Forest, Decision Trees and LightGBM, across different datasets encompassing indicators of gold, dollar, and technical indicators. The evaluation is conducted on a range of performance metrics to ascertain the efficacy of each model in predicting financial trends and fluctuations. Through ML analysis, we examine the models' capabilities in handling the unique characteristics and dynamics inherent in each dataset, providing insights into their relative strengths and weaknesses. Furthermore, this research contributes to the existing literature by offering a comparative framework for assessing the suitability of machine learning algorithms in financial forecasting tasks. The findings of this study have implications for practitioners and researchers seeking to employ machine learning techniques in financial markets, aiding in informed decision-making and risk management strategies.
—————————————
Field
Business, Marketing, Industrial Engineering, Computer Engineering.
——————————————
journal
1. Annals of Operations Research (7.1 CiteScore, 4.8 Impact Factor)
2. Neural Computing and Applications ( 8.7 CiteScore, 6.0 Impact Factor)
3. IEEE Access (9.0 CiteScore, 3.9 Impact Factor)
با عرض سلام نفرات اول و دوم این مقاله رو خالی داریم . دوستانی که نیاز دارن با بنده هماهنگ کنند.
@Machine_learn
@Paper4money
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Pixart-Sigma, the first high-quality, transformer-based image generation training framework!
🖥 Github: https://github.com/PixArt-alpha/PixArt-sigma
🔥Demo: https://huggingface.co/spaces/PixArt-alpha/PixArt-Sigma
@Machine_learn
🔥Demo: https://huggingface.co/spaces/PixArt-alpha/PixArt-Sigma
@Machine_learn
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LongEmbed: Extending Embedding Models for Long Context Retrieval
🖥 Github: https://github.com/dwzhu-pku/longembed
📕 Paper: https://arxiv.org/abs/2404.12096v1
⚡️ Project: https://6img-to-3d.github.io/
@Machine_learn
⚡️ Project: https://6img-to-3d.github.io/
@Machine_learn
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⚡️ DesignEdit: Multi-Layered Latent Decomposition and Fusion for Unified & Accurate Image Editing
Microsoft представляет DesignEd it!
▪Github: https://github.com/design-edit/DesignEdit.git
▪Paper: https://arxiv.org/abs/2403.14487
▪Project: https://design-edit.github.io/
@Machine_learn
Microsoft представляет DesignEd it!
▪Github: https://github.com/design-edit/DesignEdit.git
▪Paper: https://arxiv.org/abs/2403.14487
▪Project: https://design-edit.github.io/
@Machine_learn
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⚡️Map-relative Pose Regression🔥(#CVPR2024 highlight)
▪Paper: https://arxiv.org/abs/2404.09884
▪Page: https://nianticlabs.github.io/marepo
@Machine_learn
▪Paper: https://arxiv.org/abs/2404.09884
▪Page: https://nianticlabs.github.io/marepo
@Machine_learn
📚 image InternVL Family: Closing the Gap to Commercial Multimodal Models with Open-Source Suites —— A Pioneering Open-Source Alternative to GPT-4V
🖥 Github: https://github.com/opengvlab/internvl
📕 Paper: https://arxiv.org/abs/2404.16821v1
🔥Dataset: https://paperswithcode.com/dataset/visual-genome
@Machine_learn
🔥Dataset: https://paperswithcode.com/dataset/visual-genome
@Machine_learn
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MER 2024: Semi-Supervised Learning, Noise Robustness, and Open-Vocabulary Multimodal Emotion Recognition
🖥 Github: https://github.com/zeroqiaoba/mertools
📕 Paper: https://arxiv.org/abs/2404.17113v1
🔥Dataset: https://paperswithcode.com/dataset/voxceleb2
@Machine_learn
🔥Dataset: https://paperswithcode.com/dataset/voxceleb2
@Machine_learn
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Forwarded from Papers
نفرات ۱ تا ۴ مقاله ی زیر خالی می باشد از دوستان اگر کسی خواست در خدمتیم
Title
Solar Energy Production Forecasting: A Comparative Study of LSTM, Bi-LSTM, and XGBoost Models with Activation Function Analysis
Abstract
This research focuses on the integration of Machine Learning (ML) methodologies and climatic parameters to predict solar panel energy generation, with a specific emphasis on addressing consumption-production imbalances. Leveraging a dataset sourced from the Kaggle platform, the study is conducted in the context of Estonia, aiming to optimize solar energy utilization in this geographic region. The dataset, obtained from Kaggle, encompasses comprehensive information on climatic variables, including sunlight intensity, temperature, and humidity, alongside corresponding solar panel energy output. Through the utilization of machine learning algorithms, such as XGBoost regression and neural networks, our predictive model endeavors to discern intricate patterns and correlations within these datasets. By tailoring the model to Estonia's climatic nuances, we seek to enhance the accuracy of energy production forecasts and, consequently, better manage the challenges associated with consumption-production imbalances. Furthermore, the research investigates the adaptability of the proposed model to diverse climatic conditions, ensuring its applicability for similar endeavors in other geographical locations. By utilizing Kaggle's rich dataset and employing advanced machine learning techniques, this study aims to contribute valuable insights that can inform sustainable energy policies and practices, ultimately promoting a more efficient and reliable renewable energy infrastructure.
Related Fields
Business, Marketing, Industrial Engineering, Computer Engineering.
Candidate Journals
1. Sustainability (5.8 CiteScore, 3.9 Impact Factor)
2. Archives of Computational Methods in Engineering (14.1 CiteScore, 9.7 Impact Factor)
3. Journal of Building Engineering (8.3 CiteScore, 6.4 Impact Factor)
@Raminmousa
@paper4money
@Machine_learn
Title
Solar Energy Production Forecasting: A Comparative Study of LSTM, Bi-LSTM, and XGBoost Models with Activation Function Analysis
Abstract
This research focuses on the integration of Machine Learning (ML) methodologies and climatic parameters to predict solar panel energy generation, with a specific emphasis on addressing consumption-production imbalances. Leveraging a dataset sourced from the Kaggle platform, the study is conducted in the context of Estonia, aiming to optimize solar energy utilization in this geographic region. The dataset, obtained from Kaggle, encompasses comprehensive information on climatic variables, including sunlight intensity, temperature, and humidity, alongside corresponding solar panel energy output. Through the utilization of machine learning algorithms, such as XGBoost regression and neural networks, our predictive model endeavors to discern intricate patterns and correlations within these datasets. By tailoring the model to Estonia's climatic nuances, we seek to enhance the accuracy of energy production forecasts and, consequently, better manage the challenges associated with consumption-production imbalances. Furthermore, the research investigates the adaptability of the proposed model to diverse climatic conditions, ensuring its applicability for similar endeavors in other geographical locations. By utilizing Kaggle's rich dataset and employing advanced machine learning techniques, this study aims to contribute valuable insights that can inform sustainable energy policies and practices, ultimately promoting a more efficient and reliable renewable energy infrastructure.
Related Fields
Business, Marketing, Industrial Engineering, Computer Engineering.
Candidate Journals
1. Sustainability (5.8 CiteScore, 3.9 Impact Factor)
2. Archives of Computational Methods in Engineering (14.1 CiteScore, 9.7 Impact Factor)
3. Journal of Building Engineering (8.3 CiteScore, 6.4 Impact Factor)
@Raminmousa
@paper4money
@Machine_learn
Dynamic Prompt Learning: Addressing Cross-Attention Leakage for Text-Based Image Editing
🖥 Github: https://github.com/wangkai930418/DPL
📕 Paper: https://arxiv.org/abs/2405.01496v1
🔥Dataset: https://neurips.cc/virtual/2023/poster/72801
@Machine_learn
🔥Dataset: https://neurips.cc/virtual/2023/poster/72801
@Machine_learn
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🧬 AlphaFold 3 predicts the structure and interactions of all of life’s molecules
▪Blog: https://blog.google/technology/ai/google-deepmind-isomorphic-alphafold-3-ai-model/
▪Nature: https://www.nature.com/articles/s41586-024-07487-w
▪Two Minute Papers: https://www.youtube.com/watch?v=Mz7Qp73lj9o
@Machine_learn
▪Blog: https://blog.google/technology/ai/google-deepmind-isomorphic-alphafold-3-ai-model/
▪Nature: https://www.nature.com/articles/s41586-024-07487-w
▪Two Minute Papers: https://www.youtube.com/watch?v=Mz7Qp73lj9o
@Machine_learn
Forwarded from Papers
Automatic Image Annotation (AIA) of AlmondNet-20 Method for Almond Detection by Improved CNN-based Model
Machine Learning, Convolutional Neural Networks (CNNs), Image Annotation, Food Industry, Almond, Nuts Detection
Abstract:
In response to the global demand for high-quality agricultural products, especially in the competitive nut market, we present an innovative approach to enhance the grading of almonds and their shells. Leveraging Deep Convolutional Neural Networks (AlmondNet-20), we achieved over 99% accuracy through 20 layers of CNN, employing data augmentation for robust almond-shell differentiation. Our model, trained over 1000 epochs, demonstrated a remarkable accuracy of 99%, with a low loss function of 0.0567. Test evaluations revealed perfect precision, recall, and F1-score for almond detection. This advanced classification system not only boosts grading accuracy but also ensures reliability in distinguishing almonds from shells globally, benefiting both experts and non-experts. The application of deep learning algorithms opens avenues for product patents, contributing to the economic value of our country.
Field
Food Industry, Agricultural Engineering, Industrial Engineering, Computer Engineering.
1. Agronomy (3.7 CiteScore, 5.2 Impact Factor)
2. Biosystems Engineering (10.1 CiteScore, 5.1 Impact Factor)
3. Precision Agriculture (9.9 CiteScore, 6.2 Impact Factor)
@Raminmousa
@Machine_learn
@Paper4money
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🔥 Say Goodbye to LoRA, Hello to DoRA 🤩🤩
DoRA consistently outperforms LoRA with various tasks (LLM, LVLM, etc.) and backbones (LLaMA, LLaVA, etc.)
[Paper] https://arxiv.org/abs/2402.09353
[Code] https://github.com/NVlabs/DoRA
😄 @Machine_learn
DoRA consistently outperforms LoRA with various tasks (LLM, LVLM, etc.) and backbones (LLaMA, LLaVA, etc.)
[Paper] https://arxiv.org/abs/2402.09353
[Code] https://github.com/NVlabs/DoRA
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Enhancing Semantics in Multimodal Chain of Thought via Soft Negative Sampling
🖥 Github: https://github.com/zgmin/snse-cot
📕 Paper: https://paperswithcode.com/dataset/scienceqa
@Machine_learn
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GitHub
GitHub - zgMin/SNSE-CoT: Official implementation for "Enhancing Semantics in Multimodal Chain of Thought via Soft Negative Sampling"
Official implementation for "Enhancing Semantics in Multimodal Chain of Thought via Soft Negative Sampling" - zgMin/SNSE-CoT
💡 Lumina-T2X: Transforming Text into Any Modality, Resolution, and Duration via Flow-based Large Diffusion Transformers
▪Github: https://github.com/alpha-vllm/lumina-t2x
▪Paper: https://arxiv.org/abs/2405.05945
▪Demo: https://lumina.sylin.host/
@Machine_learn
▪Github: https://github.com/alpha-vllm/lumina-t2x
▪Paper: https://arxiv.org/abs/2405.05945
▪Demo: https://lumina.sylin.host/
@Machine_learn
Awesome-Text-to-Video-Generation Awesome
🖥 Github: https://github.com/soraw-ai/awesome-text-to-video-generation
📕 Paper: https://arxiv.org/abs/2405.10674v1
@Machine_learn
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⚡️ Deblur-GS: 3D Gaussian Splatting from Camera Motion Blurred Images
▪Code: https://github.com/Chaphlagical/Deblur-GS
▪Paper: https://chaphlagical.icu/Deblur-GS/static/paper/Deblur_GS_author_version.pdf
▪Project: https://chaphlagical.icu/Deblur-GS/
@Machine_learn
▪Code: https://github.com/Chaphlagical/Deblur-GS
▪Paper: https://chaphlagical.icu/Deblur-GS/static/paper/Deblur_GS_author_version.pdf
▪Project: https://chaphlagical.icu/Deblur-GS/
@Machine_learn