Top Tweets for #DataSCience
Great marketing requires killing your darlings. πͺπ
Digital marketing isn't about blind loyalty to an idea; itβs about rapid data iteration. If your click-throughs are high but conversions are dead after a week, tweak the offer. If both are zero? Walk away. #DataScience #Growth
Admissions Open 2026 at #IIITKota in https://t.co/QKyLLTlUa9. and Ph.D. in CSE.Last Date for Online Application Submission: 22 June 2026
π: https://t.co/a0f7CK8zxY
π: https://t.co/NRmv0wF9p1
#IIITKota #MTechAdmissions #PhDAdmissions #CSE #ArtificialIntelligence #DataScience
TAPMIβs MBA AI & DS program is designed as the perfect bridge between business concepts and technical expertise, giving students an end-to-end understanding of product development and real-time industry work.
#TAPMI #AIandDS #DataScience #AI #Bschool #TAPMIManipal #MBAROI
A Data Scientist wears many hats. One day youβre cleaning data, the next youβre building pipelines, training models, debugging systems, analyzing trends, and communicating insights. Technical skills matter, but delivering business impact is the ultimate goal. #DataScience #MachineLearning #Python #AI #DataAnalytics #MLOps #Analytics #Tech
1847 Gaussian Machine Learning. #BigData #Analytics #DataScience #AI #MachineLearning #IoT #IIoT #PyTorch #Python #RStats #TensorFlow #ReactJS #GoLang #CloudComputing #Serverless #DataScientist #Linux #Programming #Coding #100DaysOfCode
https://t.co/2cLy2VxY8X
1847 Gaussian Machine Learning. #BigData #Analytics #DataScience #AI #MachineLearning #IoT #IIoT #PyTorch #Python #RStats #TensorFlow #ReactJS #GoLang #CloudComputing #Serverless #DataScientist #Linux #Programming #Coding #100DaysOfCode
https://t.co/2cLy2VxY8X
GPT with LLM, and Generative AI! @AverConferences
#BigData #Analytics #DataScience #AI #MachineLearning #NLProc #IoT #IIoT #Python #RStats #TensorFlow #JavaScript #CloudComputing #Serverless #DataScientist #Linux #Programming #Coding #100DaysOfCode
https://t.co/LL5phLRkMP
Charlotte! 143,000 #Books Reserved at North Carolina Library. #BigData #Analytics #DataScience #IoT #IIoT #Python #RStats #TensorFlow #Java #JavaScript #GoLang #CloudComputing #Serverless #DataScientist #Linux #Books #Programming #Coding #100DaysofCode
https://t.co/lntb7P9ss0
Get Your Book at Your Local Library 2020!
@marquiswhoswho
#BigData #Analytics #DataScience #AI #MachineLearning #IoT #IIoT #PyTorch #Python #RStats #TensorFlow #Java #JavaScript #ReactJS #GoLang #CloudComputing #Serverless #DataScientist #Linux #Programming #Coding #100DaysofCode https://t.co/emawVQsVjX
Gigatron for Linear Algebra in Machine Learning! @gp_pulipaka! #BigData #Analytics #DataScience #AI #MachineLearning #IoT #IIoT #PyTorch #Python #RStats #TensorFlow #Java #JavaScript #ReactJS #GoLang #CloudComputing #Serverless #DataScientist #Linux #Mathematics #Programming #Coding #100DaysofCode
https://t.co/speD9uDmwm
120 GUI Projects in Python-220 Engineering Principles in Python! #BigData #Analytics #DataScience #AI #MachineLearning #IoT #IIoT #PyTorch #Python #RStats #TensorFlow #Java #CloudComputing #Serverless #DataScientist #Linux #Books #Programming #Coding #100DaysOfCode
https://t.co/pewyR6Mvni
Gigatron for Linear Algebra in Machine Learning! @gp_pulipaka! #BigData #Analytics #DataScience #AI #MachineLearning #IoT #IIoT #PyTorch #Python #RStats #TensorFlow #Java #JavaScript #ReactJS #GoLang #CloudComputing #Serverless #DataScientist #Linux #Mathematics #Programming #Coding #100DaysofCode
https://t.co/speD9uDmwm
#MachineLearning Puts New Lens on #IoT Computing! by @gp_pulipaka! #BigData #Analytics #DataScience #AI #IIoT #PyTorch #Python #RStats #TensorFlow #Java #ReactJS #GoLang #CloudComputing #Serverless #DataScientist #Linux #Programming #Coding #100DaysofCode
https://t.co/Vp9xfgfBwk
RAG with LLM: Creating an AI-Powered File Reader!
#BigData #Analytics #DataScience #AI #MachineLearning #NLProc #IoT #IIoT #PyTorch #Python #RStats #TensorFlow #Java #JavaScript #ReactJS #GoLang #CloudComputing #Serverless #DataScientist #Linux #Programming #Coding #100DaysofCode
This is the type of application I've already built earlier at the beginning of this year for a conference demonstration!
GPT Paper-Reader, to show how you can leverage the summarization capabilities of large language models to retrieve research papers from databases, summarize their content, and engage in Q&A interactions with people. The future is here. In addition to the regular theoretical presentation, I'll be presenting a practical GPT. This application utilizes GPT, to effectively read and analyze complete academic papers: Basically the app chunks the PDF paper into manageable sections for detailed reading and generates concise summaries for each segment. By maintaining context from previous sections within the token limit, it enhances comprehension. Before diving into the paper, you can outline specific questions in the prompt. This approach allows GPT to extract the most important information during its reading and summarizing process, leading to superior outcomes. After summarizing all parts, you will receive comprehensive answers to your inquiries based on the consolidated summaries. By default, the initialized prompt will target essential points such as: These inquiries are tailored for research articles within the computer science domain. Upon completion of the paper review, feel free to engage with the question() interface to ask further questions.
References
Khan, A. A., Hasan, M. T., Kemell, K. K., Rasku, J., & Abrahamsson, P. (2025). Developing retrieval augmented generation (RAG) based LLM systems from PDFs: An experience report [Preprint]. arXiv. Retrieved April 3, 2025, from https://t.co/YCGfBCNY4v
Santos, G. (2025, March 3). LLM + RAG: Creating an AI-powered file reader assistant. Towards Data Science. Retrieved April 3, 2025, from https://t.co/YubIqDgyP8
![gp_pulipaka's tweet photo. RAG with LLM: Creating an AI-Powered File Reader!
#BigData #Analytics #DataScience #AI #MachineLearning #NLProc #IoT #IIoT #PyTorch #Python #RStats #TensorFlow #Java #JavaScript #ReactJS #GoLang #CloudComputing #Serverless #DataScientist #Linux #Programming #Coding #100DaysofCode
This is the type of application I've already built earlier at the beginning of this year for a conference demonstration!
GPT Paper-Reader, to show how you can leverage the summarization capabilities of large language models to retrieve research papers from databases, summarize their content, and engage in Q&A interactions with people. The future is here. In addition to the regular theoretical presentation, I'll be presenting a practical GPT. This application utilizes GPT, to effectively read and analyze complete academic papers: Basically the app chunks the PDF paper into manageable sections for detailed reading and generates concise summaries for each segment. By maintaining context from previous sections within the token limit, it enhances comprehension. Before diving into the paper, you can outline specific questions in the prompt. This approach allows GPT to extract the most important information during its reading and summarizing process, leading to superior outcomes. After summarizing all parts, you will receive comprehensive answers to your inquiries based on the consolidated summaries. By default, the initialized prompt will target essential points such as: These inquiries are tailored for research articles within the computer science domain. Upon completion of the paper review, feel free to engage with the question() interface to ask further questions.
References
Khan, A. A., Hasan, M. T., Kemell, K. K., Rasku, J., & Abrahamsson, P. (2025). Developing retrieval augmented generation (RAG) based LLM systems from PDFs: An experience report [Preprint]. arXiv. Retrieved April 3, 2025, from https://t.co/YCGfBCNY4v
Santos, G. (2025, March 3). LLM + RAG: Creating an AI-powered file reader assistant. Towards Data Science. Retrieved April 3, 2025, from https://t.co/YubIqDgyP8](https://pbs.twimg.com/media/HLEifapXcAAAh1T.jpg)
![gp_pulipaka's tweet photo. RAG with LLM: Creating an AI-Powered File Reader!
#BigData #Analytics #DataScience #AI #MachineLearning #NLProc #IoT #IIoT #PyTorch #Python #RStats #TensorFlow #Java #JavaScript #ReactJS #GoLang #CloudComputing #Serverless #DataScientist #Linux #Programming #Coding #100DaysofCode
This is the type of application I've already built earlier at the beginning of this year for a conference demonstration!
GPT Paper-Reader, to show how you can leverage the summarization capabilities of large language models to retrieve research papers from databases, summarize their content, and engage in Q&A interactions with people. The future is here. In addition to the regular theoretical presentation, I'll be presenting a practical GPT. This application utilizes GPT, to effectively read and analyze complete academic papers: Basically the app chunks the PDF paper into manageable sections for detailed reading and generates concise summaries for each segment. By maintaining context from previous sections within the token limit, it enhances comprehension. Before diving into the paper, you can outline specific questions in the prompt. This approach allows GPT to extract the most important information during its reading and summarizing process, leading to superior outcomes. After summarizing all parts, you will receive comprehensive answers to your inquiries based on the consolidated summaries. By default, the initialized prompt will target essential points such as: These inquiries are tailored for research articles within the computer science domain. Upon completion of the paper review, feel free to engage with the question() interface to ask further questions.
References
Khan, A. A., Hasan, M. T., Kemell, K. K., Rasku, J., & Abrahamsson, P. (2025). Developing retrieval augmented generation (RAG) based LLM systems from PDFs: An experience report [Preprint]. arXiv. Retrieved April 3, 2025, from https://t.co/YCGfBCNY4v
Santos, G. (2025, March 3). LLM + RAG: Creating an AI-powered file reader assistant. Towards Data Science. Retrieved April 3, 2025, from https://t.co/YubIqDgyP8](https://pbs.twimg.com/media/HLEifZ1XEAArqIW.jpg)
![gp_pulipaka's tweet photo. RAG with LLM: Creating an AI-Powered File Reader!
#BigData #Analytics #DataScience #AI #MachineLearning #NLProc #IoT #IIoT #PyTorch #Python #RStats #TensorFlow #Java #JavaScript #ReactJS #GoLang #CloudComputing #Serverless #DataScientist #Linux #Programming #Coding #100DaysofCode
This is the type of application I've already built earlier at the beginning of this year for a conference demonstration!
GPT Paper-Reader, to show how you can leverage the summarization capabilities of large language models to retrieve research papers from databases, summarize their content, and engage in Q&A interactions with people. The future is here. In addition to the regular theoretical presentation, I'll be presenting a practical GPT. This application utilizes GPT, to effectively read and analyze complete academic papers: Basically the app chunks the PDF paper into manageable sections for detailed reading and generates concise summaries for each segment. By maintaining context from previous sections within the token limit, it enhances comprehension. Before diving into the paper, you can outline specific questions in the prompt. This approach allows GPT to extract the most important information during its reading and summarizing process, leading to superior outcomes. After summarizing all parts, you will receive comprehensive answers to your inquiries based on the consolidated summaries. By default, the initialized prompt will target essential points such as: These inquiries are tailored for research articles within the computer science domain. Upon completion of the paper review, feel free to engage with the question() interface to ask further questions.
References
Khan, A. A., Hasan, M. T., Kemell, K. K., Rasku, J., & Abrahamsson, P. (2025). Developing retrieval augmented generation (RAG) based LLM systems from PDFs: An experience report [Preprint]. arXiv. Retrieved April 3, 2025, from https://t.co/YCGfBCNY4v
Santos, G. (2025, March 3). LLM + RAG: Creating an AI-powered file reader assistant. Towards Data Science. Retrieved April 3, 2025, from https://t.co/YubIqDgyP8](https://pbs.twimg.com/media/HLEifZDWgAAdVg7.jpg)
![gp_pulipaka's tweet photo. RAG with LLM: Creating an AI-Powered File Reader!
#BigData #Analytics #DataScience #AI #MachineLearning #NLProc #IoT #IIoT #PyTorch #Python #RStats #TensorFlow #Java #JavaScript #ReactJS #GoLang #CloudComputing #Serverless #DataScientist #Linux #Programming #Coding #100DaysofCode
This is the type of application I've already built earlier at the beginning of this year for a conference demonstration!
GPT Paper-Reader, to show how you can leverage the summarization capabilities of large language models to retrieve research papers from databases, summarize their content, and engage in Q&A interactions with people. The future is here. In addition to the regular theoretical presentation, I'll be presenting a practical GPT. This application utilizes GPT, to effectively read and analyze complete academic papers: Basically the app chunks the PDF paper into manageable sections for detailed reading and generates concise summaries for each segment. By maintaining context from previous sections within the token limit, it enhances comprehension. Before diving into the paper, you can outline specific questions in the prompt. This approach allows GPT to extract the most important information during its reading and summarizing process, leading to superior outcomes. After summarizing all parts, you will receive comprehensive answers to your inquiries based on the consolidated summaries. By default, the initialized prompt will target essential points such as: These inquiries are tailored for research articles within the computer science domain. Upon completion of the paper review, feel free to engage with the question() interface to ask further questions.
References
Khan, A. A., Hasan, M. T., Kemell, K. K., Rasku, J., & Abrahamsson, P. (2025). Developing retrieval augmented generation (RAG) based LLM systems from PDFs: An experience report [Preprint]. arXiv. Retrieved April 3, 2025, from https://t.co/YCGfBCNY4v
Santos, G. (2025, March 3). LLM + RAG: Creating an AI-powered file reader assistant. Towards Data Science. Retrieved April 3, 2025, from https://t.co/YubIqDgyP8](https://pbs.twimg.com/media/HLEifVGWsAAqA5N.jpg)
Top Expert in Machine Learning.@DataToBiz #BigData #Analytics #DataScience #AI #MachineLearning #IoT #IIoT #PyTorch #Python #RStats #TensorFlow #Java #ReactJS #CloudComputing #Serverless #DataScientist #Linux #Programming #Coding #100DaysofCode
https://t.co/aJIsLEUsyN
Great Learning! History of Machine Learning. #BigData #Analytics #DataScience #AI #MachineLearning #IoT #IIoT #Python #RStats #TensorFlow #Java #JavaScript #ReactJS #CloudComputing #Serverless #DataScientist #Linux #Programming #Coding #100DaysofCode
https://t.co/p58ypnU470
The Future of #DataScience and #ParallelComputing! #BigData #Analytics #AI #MachineLearning #IoT #IIoT #PyTorch #Python #RStats #TensorFlow #Java #ReactJS #CloudComputing #Serverless #DataScientist #Linux #Programming #Coding #100DaysofCode
https://t.co/HDuCHROHlX
Great Learning! History of Machine Learning. #BigData #Analytics #DataScience #AI #MachineLearning #IoT #IIoT #Python #RStats #TensorFlow #Java #JavaScript #ReactJS #CloudComputing #Serverless #DataScientist #Linux #Programming #Coding #100DaysofCode
https://t.co/p58ypnU470
History of AI! #BigData #Analytics #DataScience #AI #IoT #IIoT #Python #RStats #TensorFlow #JavaScript #ReactJS #CloudComputing #Serverless #DataScientist #Linux #Programming #Coding #100DaysofCode
https://t.co/2icYYoPIyX
Strategy Management! #BigData #Analytics #DataScience #AI #IoT #IIoT #Python #RStats #TensorFlow #JavaScript #ReactJS #CloudComputing #Serverless #DataScientist #Linux #Programming #Coding #100DaysofCode
https://t.co/YURmp1mafc
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