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<aside> 💡 I write my own comments this way. The notes should follow what Jeremy is saying and I shouldn't add own comments between without noticing the reader. Please let me know if that's happening somewhere because I have watched this course previously and might add something from old courses without noticing.
High school math is enough to understand deep learning.
Lots of data is not needed but some record-breaking results have been made with <50 items.
You don't need an expensive computer but start of the art results can be made for free.
Deep learning is the best known approach in these areas
Deep learning is the same as deep neural networks.
The idea of this course is to approach deep learning from top to bottom. It means that first we get to see and test what DL can be used and then we start learning more details.
The biggest regret a lot of people are saying is that they wish to have spent more time coding and building models instead of learning the theory.
fastai is built top of PyTorch and these are written in Python and it's the language we will use during this course. Many people think that fastai is just for beginners and teachers but it's actually using layered API which makes it infinite customizable and practical for every purpose.
GPU (NVIDIA) machine is needed to support the libraries. It's not recommended buy this kind of device but there are free and paid cloud machines available.
Forums are helpful to find information about setting up these tools.
Jupyter Notebook is coding environment often used by DL people. It's easier to experiment things using Jupyter Notebooks than running Python code in terminal. Linux highly recommended.
It's possible to write markdown and code into the same Jupyter Notebook and code can be run one cell at a time
When using variables in the notebook it's taking the value from notebook's state. This means that it doesn't matter what the variable was defined above cell if it's not run.
shift + enter = run cell and go to the next