Book Image

Python: Advanced Guide to Artificial Intelligence

By : Giuseppe Bonaccorso, Rajalingappaa Shanmugamani
Book Image

Python: Advanced Guide to Artificial Intelligence

By: Giuseppe Bonaccorso, Rajalingappaa Shanmugamani

Overview of this book

This Learning Path is your complete guide to quickly getting to grips with popular machine learning algorithms. You'll be introduced to the most widely used algorithms in supervised, unsupervised, and semi-supervised machine learning, and learn how to use them in the best possible manner. Ranging from Bayesian models to the MCMC algorithm to Hidden Markov models, this Learning Path will teach you how to extract features from your dataset and perform dimensionality reduction by making use of Python-based libraries. You'll bring the use of TensorFlow and Keras to build deep learning models, using concepts such as transfer learning, generative adversarial networks, and deep reinforcement learning. Next, you'll learn the advanced features of TensorFlow1.x, such as distributed TensorFlow with TF clusters, deploy production models with TensorFlow Serving. You'll implement different techniques related to object classification, object detection, image segmentation, and more. By the end of this Learning Path, you'll have obtained in-depth knowledge of TensorFlow, making you the go-to person for solving artificial intelligence problems This Learning Path includes content from the following Packt products: • Mastering Machine Learning Algorithms by Giuseppe Bonaccorso • Mastering TensorFlow 1.x by Armando Fandango • Deep Learning for Computer Vision by Rajalingappaa Shanmugamani
Table of Contents (31 chapters)
Title Page
About Packt
Tensor Processing Units

Understanding convolution

Convolution is the central concept behind the CNN architecture. In simple terms, convolution is a mathematical operation that combines information from two sources to produce a new set of information. Specifically, it applies a special matrix known as the kernel to the input tensor to produce a set of matrices known as the feature maps. The kernel can be applied to the input tensor using any of the popular algorithms.

The most commonly used algorithm to produce the convolved matrix is as follows:

N_STRIDES = [1,1]
1. Overlap the kernel with the top-left cells of the image matrix.
2. Repeat while the kernel overlaps the image matrix:
    2.1 c_col = 0
    2.2 Repeat while the kernel overlaps the image matrix:
        2.1.1 set c_row = 0
        2.1.2 convolved_scalar = scalar_prod(kernel, overlapped cells)
        2.1.3 convolved_matrix(c_row,c_col) = convolved_scalar
        2.1.4 Slide the kernel down by N_STRIDES[0] rows.
        2.1.5 c_row = c_row  + 1