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Deep Learning for Image Classification in Python with CNN
Convolutional Neural Networks for Computer Vision With Keras and TensorFlow on Google Colab Platform : Hands-on
This Course Includes
udemy
3.8 (20 reviews )
1 total hour 
english
Online - Self Paced
course
Udemy
About Deep Learning for Image Classification in Python with CNN
Welcome to the "Deep Learning for Image Classification in Python with CNN" course. In this course, you will learn how to create a Convolutional Neural Network (CNN) in Keras with a TensorFlow backend from scratch, and you will learn to train CNNs to solve custom Image Classification problems. Please note that you don't need a high-powered workstation to learn this course. We will be carrying out the entire project in the Google Colab environment, which is free. You only need an internet connection and a free Gmail account to complete this course. This is a practical course, we will focus on Python programming, and you will understand every part of the program very well. By the end of this course, you will be able to build and train the convolutional neural network using Keras with TensorFlow as a backend. You will also be able to visualise data and use the model to make predictions on new data. This image classification course is practical and directly applicable to many industries. You can add this project to your portfolio of projects which is essential for your following job interview. This course is designed most straightforwardly to utilize your time wisely.
Happy learning.
How much does an Image Processing Engineer make in the USA? (Source: Talent)
The average image processing engineer salary in the USA is $125,550 per year or $64.38 per hour. Entry-level positions start at $102,500 per year, while most experienced workers make up to $174,160 per year.
What You Will Learn?
- Understand the fundamentals of Convolutional Neural Networks (CNNs).
- Build and train a CNN using Keras with Tensorflow as a backend using Google Colab.
- Assess the performance of trained CNN.
- Learn to use the trained model to predict the class of a new set of image data.