Hi @DigitalSreeni, saw all the videos on genetic algorithms, I absolutely like the way you explain. Wanted to know if there is a upcoming video on Reduced Order Modelling (ROMs) which essentially combines physics or first principle with Machine Learning - would like to see your explanation and take on it. Its a topic I quite find interesting but conceptually not yet able to grasp it. Just a request :).
Thanks🥰 Hope there could be a theoretical explanation of evolutionary history from YOLOv1 to v9 and their applications on microscope image multiclasses object segmentation!
thanks for the video. I am just rying to understand the true power of GA. I am wondering why can't we simply find the minimum yeild strength from CSV opened in excel using MIN commmand. The elements corresponding to that MIN strength would be similar to what we found using GA. The reason is GA uses training data and that training data must cover a wide parameter space otherwise the objective function from random forest won't be accurate? So what's really the point of GA when the parameter space has been already evaluated? Could you please explain? Thanks
Thanks for this. looking forward to the next video 🔥
Hi @DigitalSreeni, saw all the videos on genetic algorithms, I absolutely like the way you explain. Wanted to know if there is a upcoming video on Reduced Order Modelling (ROMs) which essentially combines physics or first principle with Machine Learning - would like to see your explanation and take on it. Its a topic I quite find interesting but conceptually not yet able to grasp it. Just a request :).
Thanks🥰 Hope there could be a theoretical explanation of evolutionary history from YOLOv1 to v9 and their applications on microscope image multiclasses object segmentation!
Please refer to my YOLO video. th-cam.com/video/JQ_RRcHLKFc/w-d-xo.html
thanks for the video. I am just rying to understand the true power of GA. I am wondering why can't we simply find the minimum yeild strength from CSV opened in excel using MIN commmand. The elements corresponding to that MIN strength would be similar to what we found using GA. The reason is GA uses training data and that training data must cover a wide parameter space otherwise the objective function from random forest won't be accurate? So what's really the point of GA when the parameter space has been already evaluated? Could you please explain? Thanks
how do we adjust for constraints ?
How can we perform this code with ANN model?
Great!
can we you Q-learnig or PPO to optimize the trength?
please provide colab note book also
Please look at the video description for the link to code.