Top Tweets for #TensorFLow
【1戦目】めざましじゃんけん結果速報：
パー✋でした。チョキ✌が勝ちです。
今回の相手：反町隆史 さん 稲垣来泉 さん 森本陸斗 さん
#めざましじゃんけん #めざましテレビ #人工知能 #反町隆史 #稲垣来泉 #森本陸斗 #Raspi #Darknet #GenAI #TensorFlow #OpenCV #Raspberry #YOLO #機械学習 #RNN
Generative AI with LangChain! #BigData #Analytics #DataScience #AI #MachineLearning #NLProc #LLM #IoT #IIoT #PyTorch #Python #RStats #TensorFlow #Java #JavaScript #ReactJS #GoLang #CloudComputing #Serverless #DataScientist #Linux #Programming #Coding #Books #100DaysofCode
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The Mathematical Engine of Machine Learning & Optimization Stack! #BigData #Analytics #DataScience #IoT #IIoT #PyTorch #Python #RStats #TensorFlow #Java #JavaScript #ReactJS #GoLang #CloudComputing #Serverless #DataScientist #Linux #Mathematics #Programming #Books #Coding #100DaysofCode
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Building Agentic Workflows in Python with LangGraph! #BigData #Analytics #DataScience #AI #MachineLearning #NLProc #LLM #IoT #IIoT #PyTorch #Python #RStats #TensorFlow #Java #JavaScript #ReactJS #GoLang #CloudComputing #Serverless #DataScientist #Linux #Programming #Coding #100DaysofCode
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A Course to Get into Large Language Models with Roadmaps and Colab Notebooks! @maximelabonne #BigData #Analytics #DataScience #AI #MachineLearning #NLProc #LLM #IoT #IIoT #PyTorch #Python #RStats #TensorFlow #Java #JavaScript #ReactJS #GoLang #CloudComputing #Serverless #DataScientist #Linux #Programming #Coding #100DaysofCode
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Databricks Delta Sharing! #BigData #Analytics #DataScience #AI #MachineLearning #NLProc #LLM #IoT #IIoT #PyTorch #Python #RStats #TensorFlow #Java #JavaScript #ReactJS #GoLang #CloudComputing #Serverless #DataScientist #Linux #Programming #Coding #100DaysofCode
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GPT with LLM, and Generative AI! @AverConferences
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Get Your Book at Your Local Library 2020!
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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
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1847 Gaussian Machine Learning. #BigData #Analytics #DataScience #AI #MachineLearning #IoT #IIoT #PyTorch #Python #RStats #TensorFlow #ReactJS #GoLang #CloudComputing #Serverless #DataScientist #Linux #Programming #Coding #100DaysOfCode
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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
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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
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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/HOI4GvgW8AAVSB2.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/HOI4GpZWMAAOc64.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/HOI4GjqXAAALcD2.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/HOI4Gg1XoAAe4gy.jpg)
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