This workshop from NVDIA that learning fundamentals of deep learning step by step. You can find link of workshop here: https://www.nvidia.com/en-us/training/instructor-led-workshops/fundamentals-of-deep-learning/
In this exercise, you will train a model to recognize fresh and rotten fruits. The dataset comes from Kaggle, a great place to go if you're interested in starting a project after this class. The dataset structure is in the data/fruits folder. There are 6 categories of fruits: fresh apples, fresh oranges, fresh bananas, rotten apples, rotten oranges, and rotten bananas. This will mean that your model will require an output layer of 6 neurons to do the categorization successfully. You'll also need to compile the model with categorical_crossentropy, as we have more than two categories.
1.Load ImageNet Base Model
2.Freeze Base Model
3.Add Layers to Model
4.Compile Model
5.Augment the Data
6.Load Dataset
7.Train the Model
8.Unfreeze Model for Fine Tuning
9.Evaluate the Model
10.Run the Assessment
dataset is from kaggle https://www.kaggle.com/datasets/sriramr/fruits-fresh-and-rotten-for-classification
This link of certificate
https://courses.nvidia.com/certificates/60c0a296d8024d41b604a2513cb60cd0/