Lesson 2 - Deep Learning for Coders (2020)

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In today's lesson we finish covering chapter 1 of the book, looking more at test/validation sets, avoiding machine learning project failures, and the foundations of transfer learning.

Then we move on to looking at the critical machine learning topic of evidence, including discussing confidence intervals, priors, and the use of visualization to better understand evidence.

Finally, we begin our look into productionization of models (chapter 2 of the book), including discussing the overall project plan for model development, and how to create your own datasets.

0:00 - Lesson 1 recap
2:10 - Classification vs Regression
4:50 - Validation data set
6:42 - Epoch, metrics, error rate and accuracy
9:07 - Overfitting, training, validation and testing data set
12:10 - How to choose your training set
15:55 - Transfer learning
21:50 - Fine tuning
22:23 - Why transfer learning works so well
28:26 - Vision techniques used for sound
29:30 - Using pictures to create fraud detection at Splunk
30:38 - Detecting viruses using CNN
31:20 - List of most important terms used in this course
31:50 - Arthur Samuel’s overall approach to neural networks
32:35 - End of Chapter 1 of the Book
40:04 - Where to find pretrained models
41:20 - The state of deep learning
44:30 - Recommendation vs Prediction
45:50 - Interpreting Models - P value
57:20 - Null Hypothesis Significance Testing
1:02:48 - Turn predictive model into something useful in production
1:14:06 - Practical exercise with Bing Image Search
1:16:25 - Bing Image Sign up
1:21:38 - Data Block API
1:28:48 - Lesson Summary

Видео Lesson 2 - Deep Learning for Coders (2020) канала Jeremy Howard