Q/A System — Deep learning(1/2)
Deep Learning is a subfield of machine learning concerned with algorithms inspired by the structure and function of the brain called artificial neural networks.
Neural networks are a set of algorithms, modeled loosely after the human brain, that are designed to recognize patterns. They interpret sensory data through a kind of machine perception, labeling or clustering raw input. The patterns they recognize are numerical, contained in vectors, into which all real-world data, be it images, sound, text or time series, must be translated.
The “deep” refers to neural nets with more than one hidden layer.
There are many other complex applications. One of them is face recognition and identification. The faces are converted into mathematical features, and data are passed to multiple layers and the deep learning algorithm is trained enough until a model is able to recognize the faces from images, or also identify whose faces it is able to detect.
Understanding LSTM Networks
Recurrent Neural Networks
Humans don’t start their thinking from scratch every second. As you read this essay, you understand each word based on your understanding of previous words. You don’t throw everything away and start thinking from scratch again. Your thoughts have persistence.
Traditional neural networks can’t do this, and it seems like a major shortcoming. For example, imagine you want to classify what kind of event is happening at every point in a movie. It’s unclear how a traditional neural network could use its reasoning about previous events in the film to inform later ones.
Recurrent neural networks address this issue. They are networks with loops in them, allowing information to persist.
Boring Right? well , it’s supposed to be boring!
Requirements:
Python
Keras
NLTK (Python toolkit for Natural Language Processing)
For Windows:
pip install kerasFor TensorFlow:
export TF_BINARY_URL=https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-0.11.0rc2-py2-none-any.whlpip install — upgrade $TF_BINARY_URL
