In this tutorial, you’ll learn how to construct and implement Convolutional Neural Networks (CNNs) in Python with the TensorFlow framework.

TensorFlow is a popular deep learning framework. In this tutorial, you will learn the basics of this Python library and understand how to implement these deep, feed-forward artificial neural networks with it.

To be precise, you’ll be introduced to the following topics in today’s tutorial:

• You’ll be first introduced to tensors and how they differ from matrices; Once you understand what tensors are then, you’ll be introduced to the Tensorflow Framework, within this you will also see that how even a single line of code is implemented via a computational graph in TensorFlow, then you will learn about some of the package’s concepts that play a major role in you to do deep learning like constants, variables, and placeholders.

• Then, you’ll be headed to the most interesting part of this tutorial. That is the implementation of the Convolutional Neural Network: first, you will try to understand the data. You’ll use Python and its libraries to load, explore, and analyze your data. You’ll also preprocess your data: you’ll learn how to visualize your images as a matrix, reshape your data and rescale the images between 0 and 1 if required.

• With all of this done, you are ready to construct the deep neural network model. You’ll start by defining the network parameters, then learn how to create wrappers to increase the simplicity of your code, define weights and biases, model the network, define loss and optimizer nodes. Once you have all this in place, you are ready for training and testing your model.

• Finally, you will learn to work with your own dataset. In this section, you would download the CIFAR-10 dataset from Kaggle, load the images and labels using Python modules like glob & pandas. You will read the images using OpenCV, one-hot the class labels, visualize the images with labels, normalize the images, and finally split the dataset into train and test set.

#python #tensorflow #deep-learning #machine-learning