draft

subjects/ai/backtesting-sp500/README.md

@@ -135,7 +135,7 @@ A data point (x-axis: date, y-axis: cumulated_return) is: the **cumulated return
 
 ![alt text][performance]
 
-[performance]: images/w1_weekend_plot_pnl.png 'Cumulative Performance'
+[performance]: images/w1_weekend_plot_pnl.png "Cumulative Performance"
 
 ## 5. Main
 

subjects/ai/backtesting-sp500/audit/README.md

@@ -107,7 +107,7 @@ Best practice:
 
 ![alt text][performance]
 
-[performance]: ../images/w1_weekend_plot_pnl.png 'Cumulative Performance'
+[performance]: ../images/w1_weekend_plot_pnl.png "Cumulative Performance"
 
 ##### 5. main.py
 

subjects/ai/credit-scoring/readme_data.md

@@ -4,7 +4,7 @@ This file describes the available data for the project.
 
 ![alt data description](data_description.png "Credit scoring data description")
 
-## application_{train|test}.csv
+## application\_{train|test}.csv
 
 This is the main table, broken into two files for Train (with TARGET) and Test (without TARGET).
 Static data for all applications. One row represents one loan in our data sample.
@@ -17,24 +17,23 @@ For every loan in our sample, there are as many rows as number of credits the cl
 ## bureau_balance.csv
 
 Monthly balances of previous credits in Credit Bureau.
-This table has one row for each month of history of every previous credit reported to Credit Bureau – i.e the table has (#loans in sample * # of relative previous credits * # of months where we have some history observable for the previous credits) rows.
+This table has one row for each month of history of every previous credit reported to Credit Bureau – i.e the table has (#loans in sample _ # of relative previous credits _ # of months where we have some history observable for the previous credits) rows.
 
 ## POS_CASH_balance.csv
 
 Monthly balance snapshots of previous POS (point of sales) and cash loans that the applicant had with Home Credit.
-This table has one row for each month of history of every previous credit in Home Credit (consumer credit and cash loans) related to loans in our sample – i.e. the table has (#loans in sample * # of relative previous credits * # of months in which we have some history observable for the previous credits) rows.
+This table has one row for each month of history of every previous credit in Home Credit (consumer credit and cash loans) related to loans in our sample – i.e. the table has (#loans in sample _ # of relative previous credits _ # of months in which we have some history observable for the previous credits) rows.
 
 ## credit_card_balance.csv
 
 Monthly balance snapshots of previous credit cards that the applicant has with Home Credit.
-This table has one row for each month of history of every previous credit in Home Credit (consumer credit and cash loans) related to loans in our sample – i.e. the table has (#loans in sample * # of relative previous credit cards * # of months where we have some history observable for the previous credit card) rows.
+This table has one row for each month of history of every previous credit in Home Credit (consumer credit and cash loans) related to loans in our sample – i.e. the table has (#loans in sample _ # of relative previous credit cards _ # of months where we have some history observable for the previous credit card) rows.
 
 ## previous_application.csv
 
 All previous applications for Home Credit loans of clients who have loans in our sample.
 There is one row for each previous application related to loans in our data sample.
 
-
 ## installments_payments.csv
 
 Repayment history for the previously disbursed credits in Home Credit related to the loans in our sample.

subjects/ai/emotions-detector/README.md

@@ -150,12 +150,10 @@ Preprocessing ...
 
 ### Useful resources:
 
-- https://machinelearningmastery.com/what-is-computer-vision/
+- [Computer vision](https://machinelearningmastery.com/what-is-computer-vision/)
 
-- Use a pre-trained CNN: https://arxiv.org/pdf/1812.06387.pdf
+- [Use a pre-trained CNN](https://arxiv.org/pdf/1812.06387.pdf)
 
-- Hack the CNN https://medium.com/@ageitgey/machine-learning-is-fun-part-8-how-to-intentionally-trick-neural-networks-b55da32b7196
+-[Hack the CNN](https://medium.com/@ageitgey/machine-learning-is-fun-part-8-how-to-intentionally-trick-neural-networks-b55da32b7196)
 
-- http://ice.dlut.edu.cn/valse2018/ppt/WeihongDeng_VALSE2018.pdf
-
-- https://arxiv.org/pdf/1812.06387.pdf
+- [Pre-Trained Convolutional Neural Network Features for Facial Expression Recognition](https://arxiv.org/pdf/1812.06387.pdf)