Fastai Notes

Concise Lecture Notes - Lesson 7 | Fastai v3 (2019)

CNNs, U-nets, GANs, Feature losses and RNNs.

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Concise Lecture Notes - Lesson 6 | Fastai v3 (2019)

Dropout, Data Augmentation, Batch Normalizations, Convolutions and most importantly data ethics.

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Concise Lecture Notes - Lesson 5 | Fastai v3 (2019)

Embedding matrices, Collaborative Filtering, Regularization, weight decay and Adam Optimizer.

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Concise Lecture Notes - Lesson 4 | Fastai v3 (2019)

Creating language models and text classifiers, fastai for tabular data, collaborative filtering, deeper dive into structure of Deep Neural Networks.

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Concise Lecture Notes - Lesson 3 | Fastai v3 (2019)

Datablock API to prepare datasets, Image Segmentation, Image Regression and NLP.

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Concise Lecture Notes - Lesson 2 | Fastai v3 (2019)

Creating Deep Learning datasets, learning rates, making model production ready, debugging general issues and SGD.

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#Machine Learning #fastai notes

Concise Lecture Notes - Lesson 1 | Fastai v3 (2019)

how to use the fastai library to quickly get a model up and running with state of the art results.

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#Machine Learning #fastai notes