Deep Learning for Image Classification in Python with CNN

Deep Learning for Image Classification in Python with CNN

Deep Learning for Image Classification in Python with CNN

Convolutional Neural Networks for Computer Vision With Keras and TensorFlow on Google Colab Platform

Language: english

Note: 0/5 (0 notes) 528 students  New course 

Instructor(s): Karthik K

Last update: None

What you’ll 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

 

Requirements

  • Basic knowledge of Python Programming

 

Description

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 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.


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Who this course is for

  • Beginners starting out to the field of Deep Learning
  • Industry professionals and aspiring data scientists
  • People who want to know how to write their image classification code

 

Course content

  • Fundamentals
    • Introduction
    • Artificial Intelligence
    • Machine Learning
    • Deep Learning
    • Artificial Neural Networks (Conventional / Traditional)
    • Backward Propagation of Errors
    • Gradient Descent
    • Stochastic Gradient Descent
    • Convolutional Neural Networks (CNN)
    • Input Layer, Convolutional Layer
    • Pooling Layer, Activation Function Layer
    • Fully Connected Layers / Dense Layer, Dropout Layer
    • Image Classification and its Applications
    • How image classification is done?
    • Transfer Learning
    • Architecture of ResNet (Residual Networks)
  • Building, Evaluating and Predicting Image Classification Model
    • Download Dataset
    • What is inside train folder?
    • What is the .hdf5 file?
    • What is inside test folder?
    • What is inside our_prediction folder?
    • Image Classification Python Code
    • Enabling GPU in Google Colab
    • Is GPU connected to Colab notebook?
    • Download TensorFlow and CUDA
    • Compare the Speed of GPU with CPU
    • Connect Google Colab with Google Drive
    • Check the Number of Images in the Dataset
    • Image Augmentation
    • Transfer Learning
    • Fine Tuning / Freezing of the Layers
    • Model Compilation
    • Callbacks: EarlyStopping
    • Callbacks: ModelCheckpoint
    • Training
    • Testing
    • Prediction

 

Deep Learning for Image Classification in Python with CNNDeep Learning for Image Classification in Python with CNN

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