Chapter 5: Memory-Augmented Neural Networks

1. NTM is an interesting algorithm that has the ability to store and retrieve information from memory. The idea of NTM is to augment the neural network with external memory—that is, instead of using hidden states as memory, it uses external memory to store and retrieve information. 
2. The controller is basically a feed-forward neural network or recurrent neural network. It reads from and writes to memory.
3. The read head and write head are the pointers containing addresses of the memory that it has to read from and write to.
4. The memory matrix or memory bank, or simply the memory, is where we will store the information. Memory is basically a two-dimensional matrix composed of memory cells. The memory matrix contains N rows and M columns. Using the controller, we access the content from the memory. So, the controller receives input from the external environment and emits the response by interacting with the memory matrix. 
5. Location-based addressing and content-based addressing are the different types of addressing mechanisms used in NTM.

6. An interpolation gate is used to decide whether we should use the weights we obtained at the previous time step,
   
   , or use the weights obtained through content-based addressing, 
   
   . 

7. Computing the least-used weight vector, 
   
   , from the usage weight vector, 
   
   , is very simple. We simply set the index of the lowest value usage weight vector to 1 and the rest of the values to 0, as the lowest value in the usage weight vector means that it is least recently used.