Artificial intelligence (AI, also machine intelligence, MI) is intelligence demonstrated by machines, in contrast to the natural intelligence (NI) displayed by humans and other animals. In computer science AI research is defined as the study of “intelligent agents”: any device that perceives its environment and takes actions that maximize its chance of successfully achieving its goals. Colloquially, the term “artificial intelligence” is applied when a machine mimics “cognitive” functions that humans associate with other human minds, such as “learning” and “problem solving”.

Adding attention

Attention using TensorFlow.

Batch normalization

A way to use less epochs, less sensitive to initialization and make regularization obsolete sometimes.

Convolutional autoencoder

2D convolution autoencoder for house numbers.

2D Convolution

Adding convolution to the MNIST classification.

Pooling

Adding pooling layers.

1D Convolution

1D CNN on text towards classification of reviews.

Generalization through regularization

Making a model more generic (less training data dependent) through regularization.

Experimenting with optimizers

On the effect optimizers have on the accuracy.

Hidden layers and units

import numpy as np import pandas as pd import matplotlib.pyplot as plt from sklearn.model_selection import train_test_split from sklearn.preprocessing import StandardScaler from keras.models import Sequential from…

Regularization through dropout

Avoiding overfitting through dropout layers.