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

LeNet for CIFAR10 Data

Now that we have learned to build and train the CNN model using MNIST data set with TensorFlow and Keras, let us repeat the exercise with CIFAR10 dataset. 

The CIFAR-10 dataset consists of 60,000 RGB color images of the shape 32x32 pixels. The images are equally divided into 10 different categories or classes: airplane, automobile, bird, cat, deer, dog, frog, horse, ship, and truck. CIFAR-10 and CIFAR-100 are subsets of a large image dataset comprising of 80 million images. The CIFAR data sets were collected and labelled by Alex Krizhevsky, Vinod Nair, and Geoffrey Hinton. The numbers 10 and 100 represent the number of classes of images.  



More details about the CIFAR dataset are available at the following links: and

We picked CIFAR 10, since it has 3 channels, i.e. the depth of the images is 3, while the MNIST data set had only one channel. For the sake of brevity...