- Build a character RNN from scratch with PyTorch
- Understand the
Learner
class in PyTorch - concatenate inputs to hidden layers instead of adding inputs together
- abstract the need to manually write for loops of FC layers with Pytorch's
rnn
class - overwrite the randomly initialised hidden-hidden weight matrix with an identity matrix
- spend time getting bounding box coordinates into useable format
- first step - write functions to display data and annotations to allow fast exploration
- use dictionary comprehensions
- use
imageclassifierdata.from_csv
- to create bounding box model 1. create model with 4 continuous outputs 2. create loss functions that is lower is those 4 outputs are near to 4 other numbers
- PathLib JSON
- Defaultdict
- Lambda function
TODO - learn how to navigate through fastai library source code - learn how to use Python debugger to set traces and debug code with hotkeys
- process text by downloading corpus,
- train a language model on data that learns structure of a language by taking in a token and predicting the token that comes next.
- unfreezing layers of a pre-trained language model
- multi-batch RNN
- output of (English) encoder layer h, is passed into (French) decoder along with previous French word predicted by the decoder