Refactor and clean up notesHEADmaster

4 files changed, 86 insertions, 1 deletions

diff --git a/day1/second-talk.txt b/day1/diving-deep-into-blockchain.txt
index 46df43f..8fe9218 100644
--- a/day1/second-talk.txt
+++ b/day1/diving-deep-into-blockchain.txt
@@ -1,5 +1,8 @@
 Block chain
 
+Tomasz Kowalczyk
+https://joind.in/event/php-uk-conference-2019/dive-deep-into-blockchain
+
 - Block chain is a database
 - Bespoke data structure
 
diff --git a/day1/first-talk.txt b/day1/massively-scaled-microservices.txt
index da8689e..3b6dbe0 100644
--- a/day1/first-talk.txt
+++ b/day1/massively-scaled-microservices.txt
@@ -1,5 +1,8 @@
 # micro services - perf
 
+https://joind.in/event/php-uk-conference-2019/massively-scaled-high-performance-web-services-with-php
+https://www.slideshare.net/DeminYin/massively-scaled-high-performance-web-services-with-php-132696547
+
 ## Overivew
 
 glu mobile - microservices
diff --git a/day1/no-sql.txt b/day1/non-scalar-data.txt
index ccb6a4a..908cf84 100644
--- a/day1/no-sql.txt
+++ b/day1/non-scalar-data.txt
@@ -1,4 +1,6 @@
 # Non-Scalar Data
+https://joind.in/event/php-uk-conference-2019/storing-non-scalar-data
+Derick Rethans
 
 ## Redis sets SSAD
 
@@ -46,7 +48,7 @@ DONT: RETRIVE MANIPULATE STORE
 
  redis bad at scaling
 
- joned.in/25887 
+ joned.in/25887
 
 
 # Useful links
diff --git a/day1/supervised-learning.txt b/day1/supervised-learning.txt
new file mode 100644
index 0000000..1dce7da
--- /dev/null
+++ b/day1/supervised-learning.txt
@@ -0,0 +1,77 @@
+# Learning: the hows and whys of machine learning
+
+Liam Wiltshire
+https://liam-wiltshire.github.io/talks/?talk=machinelearning&conference=phpuk
+https://joind.in/event/php-uk-conference-2019/learning-the-hows-and-whys-of-machine-learning
+
+## Overivew
+
+Charge backs
+
+## Supervised learning
+Training data
+Learning functions
+Categorisation / Classification
+Regression - Where do we sit on a line
+
+## Naive Bayes classifier
+Standardise words
+- Un pluralise
+- Un gender
+- Un tense
+- etc
+
+More data == better
+
+## Tokenisation
+https://en.wikipedia.org/wiki/Benford%27s_law
+https://php-ml.readthedocs.io
+
+Unique tokens for each unique context
+
+## Imbalanced data
+One category has more database
+99% data not charge back
+Just being accurate, not very helpful
+    - Started by flagging 100% as fine.
+    - Need to collect more data, change methods, resample data
+
+## Understand data
+- context
+- Common data vs specific data
+- Continuous vs discrete data
+
+## KNN
+K Nearest Number
+https://en.wikipedia.org/wiki/K-nearest_neighbors_algorithm
+    - Distances
+    - less sensitive to imbalance
+    - Keep K odd (no draws)
+
+## Handling nominal data
+
+Binary
+- Increase amounts of dimensions
+- normalisation required
+- equal scales
+
+## Contextless data is meaningless
+Is it normal?
+
+## Next to try
+Weighting
+Different dimensions
+Change K value (was 3NN)
+Remove outliers
+Diff distance function
+weighted distance
+
+
+
+
+# Useful links
+https://en.wikipedia.org/wiki/Benford%27s_law
+https://php-ml.readthedocs.io
+https://liam-wiltshire.github.io/talks/?talk=machinelearning&conference=phpuk
+https://joind.in/event/php-uk-conference-2019/learning-the-hows-and-whys-of-machine-learning
+https://en.wikipedia.org/wiki/K-nearest_neighbors_algorithm