I have been struggling to implement parity of sequence since many weeks. Finally after going through Geron’s book, I am now able to successfully implement an algo that learns the parity of a sequence. This does not use the classification tweak that many apply to solve the parity problem Create Training and Validation Data 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 import numpy as np import re from sklearn.

The word “Bond” can depend on the context that you come across. If it is a community news letter, the bond could be used in the context of “Walk to Bond”, “Run to Bond”, “Meet to Bond”. Here the word “Bond”, means bonding. In the context of Financial news, “Bond” could in all probability mean a financial instrument. If you have a word2vec model, “Bond” is collapsed in to one dimension and hence loses all the context.

The following are the learnings from the podcast: Transfer learning entails reusing existing models. Use the model that comes from training on different tasks Value delivery through custom feature engineering is not required. Most of the recent successes are in the field of computer vision If you do not have a lot of training data, then you can use a model that is already trained on a large image dataset(ImageNet).

The following are the learnings from the podcast: Bruno Gonçalves who is now working in JP Morgan chase is a PhD from Emory university He has done some interesting work on looking at all twitter data and look for geographical based patterns. Can one draw a map based on language patterns? 10 TB of data - Twitter Create a huge matrix of latitude and longitude Words and Geolocation matrix pattern matching PCA + Kmeans based clustering based on the patterns in the high dimensional matrix that combines word embeddings and geo location Mobile phones have made marrying the two datasets possible Evolution of language across time can also be done Ton of people working on emoji’s in twitter feed Ton of stuff can be done based on Reuters News and NLP based work

The following are the learnings from the podcast The word “bank” has different meanings in different contexts. It could be a river bank or a financial institution Transformer is a encoder-decoder architecture that makes word embeddings more robust to the context It is a modern NLP technique Attention Is All You Need - A paper that has revolutionized this space The dominant sequence transduction models are based on complex recurrent or convolutional neural networks in an encoder-decoder configuration.