Hello everyone, back at the start of August, I announced that I was undertaking a research project, I am happy to now announce that my research has culminated in my paper entitled 'Optimizing Time Series Forecasting: A Comparative Study of Adam and Nesterov Accelerated Gradient on LSTM and GRU networks Using Stock Market data'; though it may not have been listed under a category I had hoped for, I am nonetheless pleased with the result of my work and I look forwards to taking part in many more research opportunity. I would like to extend heartfelt gratittude to Rishab for all of his videos which inspired and helped me in my endevaur. Please give it a read if you are interested !! https://arxiv.org/abs/2410.01843

yo guys is there a fast way i can find out if my research idea has alr been done b4. so i can make my idea more innovative

Hello everyone, a few weeks ago I made a post discussing my research project. I am pleased to announce that it is now completed in the form of a paper. However I need an endorser to upload to the LG category of arxiv and was wondering if anyone knew someone that could help me. My email adress in ngahmadmak@gmail.com or you could comment under this post if you can help me. Many thanks, Ahmad

Hello, I am making a research project based on the following question: Enhancing the Efficiency of Gradient Descent through Momentum-Based Optimization Techniques - What is the impact of momentum-based optimization techniques (e.g., Nesterov accelerated gradient, RMSprop, and Adam) on the convergence speed and stability of gradient descent in deep learning models? and here is my time plan: Step-by-Step Plan (4-5 Weeks) Week 1: Literature Review and Experimental Design 1. Objective: Gather comprehensive knowledge on momentum-based optimization techniques and design the experiments. 2. Tasks: Identify and review key research papers on momentum-based optimization techniques. Summarize the strengths and weaknesses of each technique. Select benchmark datasets (e.g., MNIST, CIFAR-10). Choose a standard neural network architecture for testing (e.g., Convolutional Neural Network). Define evaluation metrics (e.g., convergence speed, accuracy, stability). Outline the experimental setup, including hyperparameters and training procedures. 3. Deliverables: A detailed literature review document. An experimental design document outlining the methodology and setup. Week 2: Implementation and Initial Testing 1. Objective: Implement and run initial experiments to test the setup and gather preliminary data. 2. Tasks: Implement the selected momentum-based optimization techniques in the chosen neural network framework (e.g., TensorFlow, PyTorch). Train the neural network on a small subset of the benchmark datasets using each optimization technique. Record and analyze the initial performance metrics. Adjust experimental setup as needed based on initial results. 3. Deliverables: Source code and documentation of the implementation. Initial results and performance metrics. Week 3: Full Experimentation 1. Objective: Conduct full-scale experiments to gather comprehensive data on the performance of each optimization technique. 2. Tasks: Train the neural network on the complete benchmark datasets using each optimization technique. Record and analyze the performance metrics for each experiment. Ensure reproducibility by documenting the code and experimental setup. 3. Deliverables: Detailed results and performance metrics for each optimization technique.

Hello everyone, I am not sure if this is the place but I am seeking a mentor for a research project I would like to undertake, with the US election coming up and AI and machine learning being more powerful than ever, I wanted to see whether I could predict the outcome using a variety of sources from historic data to social media engagement. If you can help me in any way, please reach out on ngahmadmak@gmail.com https://www.linkedin.com/posts/ahmad-makinde-a9405b200_seeking-a-visionary-reasearch-mentor-for-activity-7206011976651550720-hLBg?utm_source=share&utm_medium=member_desktop