Recurrent-Neural-Nets

This post is sort-of a continuation to my last post, which was a Summary Note on the workings of basic Recurrent Neural Networks. As I mentioned in that post, I’ve been learning about the workings of RNNs for the past few days, and how they deal with sequential data, like text. An RNN can be built using either a basic RNN unit (described in the last post), a Gated Recurrent unit, or an LSTM unit. This post will describe how GRUs/LSTMs learn long term dependencies in the data, which is something basic RNN units are not so good at. ...

I’ve been learning about Recurrent Neural Nets this week, and this post is a “Summary Note” for the same. A “Summary Note” is just a blog post version of the notes I make for something, primarily for my own reference (if I need to come back to the material in the future). These summary notes won’t go into the very foundations of whatever they’re about, but rather serve as a quick and practical reference for that particular topic. ...

I’m currently learning about Convolutional Neural Nets from deeplearning.ai, and boy are they really powerful. Some of them even have cool names like Inception Network and make use of algorithms like You Only Look Once (YOLO). That is hilarious and awesome. This notebook/post is an exercise in trying to visualize the outputs of the various layers in a CNN. Let’s get to it. Setup import numpy as np import pandas as pd from math import ceil import matplotlib.pyplot as plt import matplotlib.image as mpimg from sklearn.model_selection import train_test_split from keras import layers from keras.layers import Input, Dense, Activation, ZeroPadding2D, Flatten, Conv2D from keras.layers import AveragePooling2D, MaxPooling2D from keras.models import Model from keras.datasets import fashion_mnist from keras.optimizers import Adam from keras.models import load_model To begin with, I’ll use the Fashion-MNIST dataset. ...