Fastbook notebook_5

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Notebook_5 deep dive to Image classification problem and explain the mathematical concepts in an easy understandable code way.

I don't want to explain the concepts in the notebook because Jeremy and Sylvian explained in an amazing way. What I am going to discuss is that I face difficulties in some areas you might come across the same issue. That's what we are going to discuss.

Once the dataset is downloaded you may come across why we are using the Path.BASE_PATH = path ,if we don't do this means we are going to write the entire path of the directory. Path.BASE_PATH = path performs the same as pathlib module.

Data block is like a blueprint for the model we are going to build. While building the data block you may come across an interesting library called regex, don't get intimidated and learn everything about regex, I am sure you may get that in the deep learning journey. Go with the flow learn what you want to learn like just know what it's like $ took the last digits , /d taking the decimal and + just added the string operation it's enough as of now.

you can use the summary class to check the pipeline of the data block, data loaders and so on. We can also use the show batch to know whether we prepared the data set correctly or not.

Cross Entropy loss it's the part lit bit of maths heavy dont worry too much if you don't understand something go through the Jeremy lecture and read the notebook and repeat until you understand definitely you will understand within 3-4 th go through.

Learning rate finder, confusion matrix you will understand by going through it what i would suggest is experiment with different learning rate and see how our is doing.

I was so much interested in transfer learning techniques while doing projects like twitter sentiment analysis and disaster tweet classification and so on. While reading that fastai i was learning concepts like discriminative learning rate and so on which excites me. 

For this notebook_4 you can experiment with a clean folder of the fastbook repository.

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