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

The YOLO object detection algorithm 

A recent algorithm for object detection is You look only once (YOLO). The image is divided into multiple grids. Each grid cell of the image runs the same algorithm. Let's start the implementation by defining layers with initializers:

def pooling_layer(input_layer, pool_size=[2, 2], strides=2, padding='valid'):
    layer = tf.layers.max_pooling2d(
    add_variable_summary(layer, 'pooling')
return layer

def convolution_layer(input_layer, filters, kernel_size=[3, 3], padding='valid',
    layer = tf.layers.conv2d(
weights_initializer=tf.truncated_normal_initializer(0.0, 0.01),
    add_variable_summary(layer, 'convolution')
return layer

def dense_layer(input_layer, units, activation=tf.nn.leaky_relu...