Showing posts from 2019

Recurrent Neural Network and Long-Short Term Memory

Recurrent Neural Network In traditional neural networks, all the inputs and outputs are independent of each other, but in cases like when it is required to predict the next word of a sentence, the previous words are required and hence there is a need to remember the previous words.  Thus Recurrent Neural Network (RNN) came into existence. RNN are networks with loops in them, allowing information to persist.
In the above diagram, the network takes $ x_t$ as input and outputs $ y_t$. The hidden layer applies a formula to the current input as well as the previous state to get current state $ h_t$.
The formula for the current state can be written like this: $h_t = tanh(l1(x_t) + r1(h_{t-1}))$ The output can be calculated: $y_t = l2(h_t)$ This chain-like nature reveals that recurrent neural networks are intimately related to sequences and lists. They’re the natural architecture of neural network to use for such data.
Recurrent Neural Networks suffer from short-term memory. If a sequence i…

Correct A Math Test with Deep Learning - Part I

100-Masu 100-Masu (or 100-Math) is a popular math test for primary school student in Japan. It's just only a combination of one digit addition, multiplication, and division in 100 squares table.

As you can see, the math test has a table that include 100 cells where students write their answers. Any cell that is left empty or with a wrong answer is to be counted towards the total number of mistakes and the tally is marked on the test sheet. The important in practicing 100-math calculations test is also in Prof. Hideo Kageyama's book: To do everydayRecording everyday With about 30 students in class, the teacher must spent so much time everyday to manually correct these tests. So it's worth to implement a automatically correction.
The Approach Our target is building a mobile app that can capture test sheet image, identify the multipliers in each table, identify the numbers written in the answer cells, compare against the expected answers, and mark the total number of mistakes …