Book Image

Hands-On Artificial Intelligence for Beginners

By : Patrick D. Smith, David Dindi
Book Image

Hands-On Artificial Intelligence for Beginners

By: Patrick D. Smith, David Dindi

Overview of this book

Virtual Assistants, such as Alexa and Siri, process our requests, Google's cars have started to read addresses, and Amazon's prices and Netflix's recommended videos are decided by AI. Artificial Intelligence is one of the most exciting technologies and is becoming increasingly significant in the modern world. Hands-On Artificial Intelligence for Beginners will teach you what Artificial Intelligence is and how to design and build intelligent applications. This book will teach you to harness packages such as TensorFlow in order to create powerful AI systems. You will begin with reviewing the recent changes in AI and learning how artificial neural networks (ANNs) have enabled more intelligent AI. You'll explore feedforward, recurrent, convolutional, and generative neural networks (FFNNs, RNNs, CNNs, and GNNs), as well as reinforcement learning methods. In the concluding chapters, you'll learn how to implement these methods for a variety of tasks, such as generating text for chatbots, and playing board and video games. By the end of this book, you will be able to understand exactly what you need to consider when optimizing ANNs and how to deploy and maintain AI applications.
Table of Contents (15 chapters)

GloVe

Globalized Vectors (GloVe) was developed by the Stanford NLP group in 2014 as a probabilistic follow-up to Word2Vec. GloVe was designed to preserve the analogies framework used by Word2vec, but instead uses dimensionality reduction techniques that would preserve key statistical information about the words themselves. Unlike Word2vec, which learns by streaming sentences, GloVe learns embeddings by constructing a rich co-occurrence matrix. The co-occurrence matrix is a global store of semantic information, and is key to the GloVe algorithm. The creators of GloVe developed it on the principle that co-occurrence ratios between two words in a context are closely related to meaning.

So how does it work, and how is it different from Word2vec? GloVe creates a word embedding by means of the following:

  1. Iterating over a sentence, word by word
  2. For each word, the algorithm looks at...