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    How to Chain Prompts for Better LLM Flow

    Watch: Let The LLM Write The Prompt 2025 | Design Perfect Prompts for AI Agent | Prompt Mistakes (PART 1/7) by Amine DALY Prompt chaining enhances large language model (LLM) workflows by linking prompts sequentially or in parallel to solve complex tasks. This section breaks down techniques,…
    Thumbnail Image of Tutorial How to Chain Prompts for Better LLM Flow
    Image of Author Dr. Dipen
    Dr. Dipen
    I am an AI/ML researcher with 150+ citations and 16 published research papers. I have three tier-1 publications, including Internet of Things (Elsevier), Biomedical Signal Processing and Control (Elsevier), and IEEE Access. In my research journey, I have collaborated with NASA Glenn Research Center, Cleveland Clinic, and the U.S. Department of Energy for various research projects. I am also an official reviewer and have reviewed over 100 research papers for Elsevier, IEEE Transactions, ICRA, MDPI, and other top journals and conferences. I hold a PhD from Cleveland State University with a focus on large language models (LLMs) in cybersecurity, and I also earned a master’s degree in informatics from Northeastern University.
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      AI Bootcamp Success Checklist: Fine-Tuning Instructions for Real-World Application Development

      Watch: Prompt Engineering by Thinking Neuron The LSU Online AI Bootcamp spans 26 weeks with 200+ hours of live classes and 15+ projects, focusing on Python, TensorFlow, and OpenAI. The Virginia Tech Bootcamp emphasizes machine learning and neural networks but lacks real-time project demos. In…
      Thumbnail Image of Tutorial AI Bootcamp Success Checklist: Fine-Tuning Instructions for Real-World Application Development
      Image of Author Dr. Dipen
      Dr. Dipen
      I am an AI/ML researcher with 150+ citations and 16 published research papers. I have three tier-1 publications, including Internet of Things (Elsevier), Biomedical Signal Processing and Control (Elsevier), and IEEE Access. In my research journey, I have collaborated with NASA Glenn Research Center, Cleveland Clinic, and the U.S. Department of Energy for various research projects. I am also an official reviewer and have reviewed over 100 research papers for Elsevier, IEEE Transactions, ICRA, MDPI, and other top journals and conferences. I hold a PhD from Cleveland State University with a focus on large language models (LLMs) in cybersecurity, and I also earned a master’s degree in informatics from Northeastern University.
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        Pipeline Parallelism vs Data Parallelism: Which Improves Throughput?

        Watch: I explain Fully Sharded Data Parallel (FSDP) and pipeline parallelism in 3D with Vision Pro by william falcon Pipeline parallelism and data parallelism are two strategies for optimizing computational workloads, particularly in deep learning and large-scale model training. The choice between…
        Thumbnail Image of Tutorial Pipeline Parallelism vs Data Parallelism: Which Improves Throughput?
        Image of Author Dr. Dipen
        Dr. Dipen
        I am an AI/ML researcher with 150+ citations and 16 published research papers. I have three tier-1 publications, including Internet of Things (Elsevier), Biomedical Signal Processing and Control (Elsevier), and IEEE Access. In my research journey, I have collaborated with NASA Glenn Research Center, Cleveland Clinic, and the U.S. Department of Energy for various research projects. I am also an official reviewer and have reviewed over 100 research papers for Elsevier, IEEE Transactions, ICRA, MDPI, and other top journals and conferences. I hold a PhD from Cleveland State University with a focus on large language models (LLMs) in cybersecurity, and I also earned a master’s degree in informatics from Northeastern University.
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          Pipeline Parallelism in Practice: Step‑by‑Step Guide

          Pipeline parallelism splits large deep learning models across multiple devices to optimize memory and compute efficiency. This technique partitions models into stages, enabling parallel execution of layers while managing data flow between devices. Below is a structured overview of key…
          Thumbnail Image of Tutorial Pipeline Parallelism in Practice: Step‑by‑Step Guide
          Image of Author Dr. Dipen
          Dr. Dipen
          I am an AI/ML researcher with 150+ citations and 16 published research papers. I have three tier-1 publications, including Internet of Things (Elsevier), Biomedical Signal Processing and Control (Elsevier), and IEEE Access. In my research journey, I have collaborated with NASA Glenn Research Center, Cleveland Clinic, and the U.S. Department of Energy for various research projects. I am also an official reviewer and have reviewed over 100 research papers for Elsevier, IEEE Transactions, ICRA, MDPI, and other top journals and conferences. I hold a PhD from Cleveland State University with a focus on large language models (LLMs) in cybersecurity, and I also earned a master’s degree in informatics from Northeastern University.
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            Optimizing Pipeline Parallelism for Large‑Scale Models

            Watch: Efficient Large-Scale Language Model Training on GPU Clusters by Databricks Optimizing pipeline parallelism involves selecting the right technique for your use case and balancing trade-offs between complexity, latency, and throughput. Below is a structured breakdown of key considerations:…
            Thumbnail Image of Tutorial Optimizing Pipeline Parallelism for Large‑Scale Models
            Image of Author Dr. Dipen
            Dr. Dipen
            I am an AI/ML researcher with 150+ citations and 16 published research papers. I have three tier-1 publications, including Internet of Things (Elsevier), Biomedical Signal Processing and Control (Elsevier), and IEEE Access. In my research journey, I have collaborated with NASA Glenn Research Center, Cleveland Clinic, and the U.S. Department of Energy for various research projects. I am also an official reviewer and have reviewed over 100 research papers for Elsevier, IEEE Transactions, ICRA, MDPI, and other top journals and conferences. I hold a PhD from Cleveland State University with a focus on large language models (LLMs) in cybersecurity, and I also earned a master’s degree in informatics from Northeastern University.
            •Last Updated:
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