TWiML Fastai "Deep Learning From the Foundations" Study Group

This repo is to share annotated Jupyter notebooks, lecture slide decks and other curriculum materials that I'm creating for the TWiML Fastai "Deep Learning From the Foundations" Study Group aka "Practical Deep Learning for Coders Part 2"

Annotated or otherwise modified notebooks bear the suffix '-jcat.ipynb'.

The study group meets remotely via Zoom on Saturdays at 8:45 AM Pacific time, from July 6, 2019 through November 30, 2019. We'll cover Lessons 8 - 12 of the course, which are associated with the Python / PyTorch Fastai library. We will not cover lessons 13 and 14, which provide an introduction to the new Swift / TensorFlow implementation of the Fastai library. We communicate on the on the #fastai_dl channel of the TWiML Slack Group. Our use of the Slack and Zoom platforms is by courtesy of Sam Charrington, of TWiML (This Week in Machine Learning).

An up-to-date schedule is maintained at https://docs.google.com/spreadsheets/d/139XMd3moRb1C2No1XF5zN11Ivqm1XXXhEWNu2wcfTZc/edit#gid=339739437

Join the TWiML Fastai study group here: https://twimlai.com/twiml-x-fast-ai/

Here is the key to optimizing your Fastai course experience: BE MORE THAN A SPECTATOR! Even if you intently watch and study all the videos, unless you're also learning from the course notebooks by spending time with them, running them and playing with them, you are a spectator, and you will miss out on getting the full benefit of the course.

I've found that the primary barrier to participation in the Fastai courses is the task of setting up infrastructure to run the course notebooks. There are many possible ways to accomplish the task. If you have a computer with a GPU, you can run the notebooks locally. Otherwise you can run the notebooks on Kaggle, on Google Colab, or on other cloud platforms such as paperspace, AWS, and crestle.

Currently the best cloud alternative for most people is Google Colab, which provides free access to CPUs, GPUs and even TPUs (Tensor Processing Units)! I have created a Jupyter notebook to guide you through the process of setting up to run the course notebooks on Google Colab; you can run it directly from this repo via the URL

https://colab.research.google.com/github/jcatanza/Fastai-Deep-Learning-From-the-Foundations-TWiML-Study-Group/blob/master/initialize_fastai_dl2_colab_jcat.ipynb

If you plan to participate in the course, please set up infrastruture to run the notebooks as a first priority!

Hope to see you at the next meetup!

Name		Name	Last commit message	Last commit date
Latest commit History 70 Commits
.ipynb_checkpoints		.ipynb_checkpoints
exp		exp
images		images
study_group		study_group
00_exports.ipynb		00_exports.ipynb
01_matmul.ipynb		01_matmul.ipynb
02_fully_connected.ipynb		02_fully_connected.ipynb
02a_why_sqrt5.ipynb		02a_why_sqrt5.ipynb
02a_why_sqrt5_jcat.ipynb		02a_why_sqrt5_jcat.ipynb
02b_initializing.ipynb		02b_initializing.ipynb
02b_initializing_jcat.ipynb		02b_initializing_jcat.ipynb
03_minibatch_training.ipynb		03_minibatch_training.ipynb
03_minibatch_training_jcat.ipynb		03_minibatch_training_jcat.ipynb
04_callbacks.ipynb		04_callbacks.ipynb
04_callbacks_jcat.ipynb		04_callbacks_jcat.ipynb
05_anneal.ipynb		05_anneal.ipynb
05_anneal_jcat.ipynb		05_anneal_jcat.ipynb
05a_foundations.ipynb		05a_foundations.ipynb
05a_foundations_jcat.ipynb		05a_foundations_jcat.ipynb
05b_early_stopping.ipynb		05b_early_stopping.ipynb
05b_early_stopping_jcat.ipynb		05b_early_stopping_jcat.ipynb
06_cuda_cnn_hooks_init.ipynb		06_cuda_cnn_hooks_init.ipynb
06_cuda_cnn_hooks_init_jcat.ipynb		06_cuda_cnn_hooks_init_jcat.ipynb
07_batchnorm.ipynb		07_batchnorm.ipynb
07_batchnorm_jcat.ipynb		07_batchnorm_jcat.ipynb
07a_lsuv.ipynb		07a_lsuv.ipynb
07a_lsuv_jcat.ipynb		07a_lsuv_jcat.ipynb
08_data_block.ipynb		08_data_block.ipynb
08_data_block_jcat.ipynb		08_data_block_jcat.ipynb
09_optimizers.ipynb		09_optimizers.ipynb
09_optimizers_jcat.ipynb		09_optimizers_jcat.ipynb
09b_learner.ipynb		09b_learner.ipynb
09b_learner_jcat.ipynb		09b_learner_jcat.ipynb
09c_add_progress_bar.ipynb		09c_add_progress_bar.ipynb
09c_add_progress_bar_jcat.ipynb		09c_add_progress_bar_jcat.ipynb
10_augmentation.ipynb		10_augmentation.ipynb
10_augmentation_jcat.ipynb		10_augmentation_jcat.ipynb
10b_mixup_label_smoothing.ipynb		10b_mixup_label_smoothing.ipynb
10b_mixup_label_smoothing_jcat.ipynb		10b_mixup_label_smoothing_jcat.ipynb
10c_fp16.ipynb		10c_fp16.ipynb
10c_fp16_jcat.ipynb		10c_fp16_jcat.ipynb
11_train_imagenette.ipynb		11_train_imagenette.ipynb
11_train_imagenette_jcat.ipynb		11_train_imagenette_jcat.ipynb
11a_transfer_learning.ipynb		11a_transfer_learning.ipynb
11a_transfer_learning_jcat.ipynb		11a_transfer_learning_jcat.ipynb
12_text.ipynb		12_text.ipynb
12_text_jcat.ipynb		12_text_jcat.ipynb
12a_awd_lstm.ipynb		12a_awd_lstm.ipynb
12a_awd_lstm_jcat.ipynb		12a_awd_lstm_jcat.ipynb
12b_lm_pretrain.ipynb		12b_lm_pretrain.ipynb
12b_lm_pretrain_jcat.ipynb		12b_lm_pretrain_jcat.ipynb
12c_error.JPG		12c_error.JPG
12c_ulmfit.ipynb		12c_ulmfit.ipynb
12c_ulmfit_jcat.ipynb		12c_ulmfit_jcat.ipynb
README.md		README.md
Untitled.ipynb		Untitled.ipynb
_config.yml		_config.yml
audio.ipynb		audio.ipynb
bleu_metric.ipynb		bleu_metric.ipynb
bn		bn
bn.png		bn.png
cyclegan.ipynb		cyclegan.ipynb
cyclegan_ws.ipynb		cyclegan_ws.ipynb
devise.ipynb		devise.ipynb
dice_metric.ipynb		dice_metric.ipynb
euler_rotations.ipynb		euler_rotations.ipynb
initialize_fastai_dl2_colab_jcat.ipynb		initialize_fastai_dl2_colab_jcat.ipynb
notebook-6-pets-more-rev1.odt		notebook-6-pets-more-rev1.odt
notebook2script.py		notebook2script.py
pascal.ipynb		pascal.ipynb
run_notebook.py		run_notebook.py
timing.ipynb		timing.ipynb
train_imagenette.py		train_imagenette.py
transforms-dali.ipynb		transforms-dali.ipynb
translation.ipynb		translation.ipynb
translation_transformer.ipynb		translation_transformer.ipynb

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