2020 fast.ai study 활동기록 저장소

• 이 저장소는 2020 fast.ai study의 활동기록을 저장하는 저장소입니다.

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List

	Part	Page
1	Deep Learning Is for Everyone	3
1	How to Learn Deep Learning	12
2	The Software: PyTorch, fastai, and Jupyter (And Why It Doesn’t Matter)	12
2	Running Your First Notebook	20
3	What Is Machine Learning?	20
3	Limitations Inherent to ML	26
4	How Our Image Recognizer Works	26
4	What Our Image Recognizer Learned	36
5	Image Recognizers Can Tackle Non-Image Tasks	36
5	Deep Learning Is Not Just for Image Classification	48
6	Validation Sets and Test Sets	48
6	A Choose Your Own Adventure Moment	54
7	The Preactice of Deep Learning	57
7	The Practice of Deep Learning : The Drivetrain Approach	65
8	Gathering Data	65
8	Gathering Data	70
9	From Data to DataLoaders	70
9	Training Your Model, and Using It to Clean Your Data	78
10	Tuning Your Model into an Online Application	78
10	Tuning Your Model into an Online Application:Deploying Your App	86
11	How to Avoid Disaster	86
11	Questionaire : Further Research	92
12	Key Example for Data Ethnics	93
12	Key Example for Data Ethnics	99
13	Integrating Machine Learning with Product Design	99
13	Topics in Data Ethnics : Feedback Loops	105
14	Topics in Data Ethnics : Bias	105
14	Topics in Data Ethnics : Bias	116
15	Topics in Data Ethnics : Disinformation	116
15	Identifying and Addressing Ethical Issues:Fairness, Accountability, and Transparency	123
16	Role of Policy	123
16	Deep Learning in Practice : That's a Wrap!	128
17	Pixels: The Foundations of Computer Vision	133
17	First Try: Pixel Similarity:NumPy Arrays and PyTorch Tensors	145
18	Computing Metrics Using Broadcastingn	145
18	Stochastic Gradient Descent:Calculating Gradients	156
19	Stochastic Gradient Descent:Stepping with a Learning Rate	157
19	Stochastic Gradient Descent:Summarizing Gradient Descent	163
20	The MNIST Loss Function:Sigmoid	163
20	The MNIST Loss Function:SGD and Mini-Batches	171
21	Putting It All Together:Creating an Optimizer	171
21	Questionnaire:Further Research	184
22	From Dogs and Cats to Pet Breeds	185
22	Presizing:Checking and Debugging a DataBlocks	194
23	Cross-Entropy Loss	194
23	Cross-Entropy Loss:Taking the log	203
24	Model Interpretation	203
24	Improving Our Model:Unfreezing and Transfer Learning	210
25	Improving Our Model:Discriminative Learning Rates	210
25	Questionnaire:Further Research	217
26	Other Computer Vision Problems: Multi-Label Classification	219
26	Other Computer Vision Problems: Binary Cross Entropy	231
27	Other Computer Vision Problems: Regression	231
27	Other Computer Vision Problems: Questionnaire	239
28	Training a State-of-the-Art Model: Imagenette	239
28	Training a State-of-the-Art Model: Questionnaire	252
29	Collaborative Filtering: A First Look at the Data	253
29	Collaborative Filtering: Creating Our Own Embedding Module	266
30	Collaborative Filtering: Interpreting Embeddings and Biases	267
30	Collaborative Filtering: Questionnaire	276
31	Categorical Embeddings	277
31	The Dataset: Look at the Data	286
32	Decision Trees	287
32	Decision Trees: Categorical Variables	297
33	Random Forests	298
33	Model Interpretation: Removing Redundant Features	306
34	Model Interpretation: Partial Dependence	308
34	Extrapolation and Neural Networks: Finding Out-of-Domain Data	318
35	Extrapolation and Neural Networks: Using a Neural Network	318
35	Conclusion	327
36	Text Preprocessing	329
36	Putting Our Texts into Batches for a Language Model	342
37	Training a Text Classifier	342
37	Fine-Tuning the Classifier	350
38	Going Deeper into fastai's Layered API	350
38	Pipeline	359
39	TfmdLists and Datasets: Transformed Collections	359
39	Understanding fastai's Applications: Wrap Up	373
40	The Data, Our First Language Model from Scratch	373
40	Our First Recurrent Neural Network	381
41	Improving the RNN	381
41	Creating More Signal	386
42	Multilayer RNNs	386
42	Exploding or Disappearing Activations	390
43	LSTM	390
43	Training a Language Model Using LSTMs	394
44	Regularizing an LSTM	394
44	Further Research	402

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