2019

09-18 Using Transfer Learning and Pre-trained Language Models to Classify Spam
09-18 Embeddings in NLP and beyond
09-18 BERT
09-13 LSTM Models for Simulated Time Series data
09-13 Heuristics
09-12 Early Stopping in Keras
09-11 Generative LSTM
09-10 Stacked LSTM
09-10 Sequence to Sequence LSTM
09-10 Plain Vanilla LSTM
09-10 LSTM output
09-10 Bidirectional LSTM
09-08 Ultra Learning - Book Review
09-07 Spacy Deliberate Practice
09-07 NLP - Natural Language Processing with Python - Jose Portilla
09-06 Sentiment Analysis via LSTM
09-06 Generating text via SimpleRNN
09-03 Train an XOR using Simple RNN
09-03 Train a Simple RNN to track cumulative sums
09-03 Train a Simple RNN to track a simple shift
09-03 Train a Simple RNN to track a shift sampled from a normal distribution
08-26 Example of why word embeddings matter
08-23 The Transformer
08-14 The Transformer
08-14 Mapping Dialects with Twitter Data
08-07 The Death of a Language
08-07 Named Entity Recognition
08-06 Simultaneous Translation
08-06 Sequence to Sequence Models
08-02 Human vs Machine Transcription
07-31 Data Skeptic - Word Embeddings Lower Bound

2018

07-01 Index Funds and ETFs
06-28 Machine Learning With Boosting - Summary
06-21 Make Your Own Neural Network
06-21 A Brief Introduction to Cloud Computing
06-19 Neural Networks - A Visual Introduction For Beginners
06-18 Machine Learning with Random Forests and Decision Trees
06-17 Decision Trees and Random Forests : A Visual Introduction For Beginners

2017

12-30 The Power of Habit : Summary
12-30 The Idiot Brain: Summary
09-11 Incorporating Implied volatility in Portfolio Risk Estimation
09-07 Graph Databases : Book Summary
08-10 Learning Cypher : Summary

2016

05-02 Learn Git in a Month of Lunches : Review
05-01 The Cartoon Guide to Genetics : Book Review
05-01 Machine Learning for cracking DNA
04-26 Becoming Silent
02-01 Quote for the day
01-13 The Master Algorithm : Book Summary
01-13 Fail, Fail Again, Fail Better