Note: the complete collection of Part 2 video timelines is available in a single thread for keyword search.
Part 2: complete collection of video timelines
Lesson 14 video timeline:
00:01:25 Time-Series and Structured Data
& “Patient Mortality Risk Predictions in Pediatric Intensive Care, using RNN’s” (research)
00:07:30 Time-Series with Rossmann Store Sales (Kaggle)
& 3rd place solution with "a very uncool NN ^!^ ".
00:18:00 Implementing the Rossman solution with Keras + TensorFlow + Pandas + Sklearn
Building Tables & Exploratory Data Analysis (EDA)
00:27:15 Digress: categorical variable encodings and “Vtreat for R”
00:30:15 Back to Rossmann solution
& “Python for Data Analysis” (book)
00:36:30 What Jeremy does everytime he sees a ‘date’ in a structured ML model
& other tips
00:43:00 Dealing with duration of special events (holidays, promotions) in Time-Series
00:52:00 Using ‘inplace=True’ in .drop(), & a look at our final ‘feature engineering’ results
00:53:40 Starting to feed our NN
& using pickle.dump() for storage encodings
01:00:45 “Their big mistake” and how they could have won #1
01:05:30 Splitting into Training and Test, but not randomly
01:08:20 Why they modified their Sales Target with np.log()/max_log_y
01:11:20 A look at our basic model
01:14:45 Training our model and questions
01:16:45 Running the same model with XGBoost
01:20:10 “The really, really, really weird things here !”
& end of the Rossmann coverage 
01:26:30 Taxi Trajectory Prediction (Kaggle) with “another uncool NN” Time-Series winner
01:38:00 “Start with a Conv layer and pass it to an RNN” question and research
01:42:40 The 100-layers Tiramisu: Fully Convolutional DenseNets, for Image Segmentation (Lesson 13 cont.)
01:58:00 Building and training the Tiramisu model
02:02:50 ENet and LINKNet models: better than the Tiramisu ?
02:04:00 Part 2: conclusion and next steps