๐ Learning ML one bug at a time
๐ (AI/ML) @ GEU + BS DS @ IIT MADRAS
๐ฑ Building, breaking, figuring out
โ๏ธ Chess | ๐๏ธ F1 | always debugging
Completed my PyTorch learning journey and pushed everything to GitHub.
There's still plenty to explore, but consistency beats intensity
Looking forward to building more deep learning projects.
GitHub: https://t.co/Wr1hlvsgGl
Day 37/โ โ Learning AI until I get hired
1.Creating training and testing loop functions
2. Plotting model 0 loss curves
3. Overfitting and underfitting
4. Plotting model 1 loss curves
Day 36/โ โ Learning AI until I get hired
1. Writing a custom dataset class from scratch
2. Turning custom datasets into DataLoaders
3. Data augmentation
4. Building a baseline model
5. Getting a summary of our model with torchinfo
Day 34/โ โ Learning AI until I get hired
1.Functions of a continuous random variable
2.Expectations of continuous random variable
1.Turing our data into tensors
2.Creating image DataLoaders
3.Creating a custom dataset class (overview)
I'm in a regular college, and it's hard to stay consistent since my classes run from 8 AM to 6 PM. I'm doing my best to make the most of Saturdays and Sundays for studying.
Day 33/โ โ Learning AI until I get hired
There was a Quiz 1 for Statstic
->Pytorch
1.Introduction to custom datasets
2.Downloading a custom dataset of pizza, steak and sushi images
3.Becoming one with the data
Day 32/โ โ Learning AI until I get hired
Practicing Statistics for my exam next week.
->Pytorch
1. Making predictions on random test samples
2. Plotting our best model predictions
3. Evaluating model predictions with a confusion matrix