Lesson 2 - Deep Learning for Coders (2020)

NB: We recommend watching these videos through https://course.fast.ai rather than directly on YouTube, to get access to the searchable transcript, interactive notebooks, setup guides, questionnaires, and so forth. 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