Deep Learning for Image Classification in Python with CNN

Deep Learning for Image Classification in Python with CNN, available at $39.99, has an average rating of 4, with 35 lectures, based on 20 reviews, and has 1084 subscribers.

You will learn about 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 This course is ideal for individuals who are Beginners starting out to the field of Deep Learning or Industry professionals and aspiring data scientists or People who want to know how to write their image classification code It is particularly useful for Beginners starting out to the field of Deep Learning or Industry professionals and aspiring data scientists or People who want to know how to write their image classification code.

Enroll now: Deep Learning for Image Classification in Python with CNN

Summary

Title: Deep Learning for Image Classification in Python with CNN

Price: $39.99

Average Rating: 4

Number of Lectures: 35

Number of Published Lectures: 35

Number of Curriculum Items: 35

Number of Published Curriculum Objects: 35

Original Price: ?799

Quality Status: approved

Status: Live

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

Who Should Attend

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

Target Audiences

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

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 anduse 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.

Course Curriculum

Chapter 1: Fundamentals

Lecture 1: Introduction

Lecture 2: Artificial Intelligence

Lecture 3: Machine Learning

Lecture 4: Deep Learning

Lecture 5: Artificial Neural Networks (Conventional / Traditional)

Lecture 6: Backward Propagation of Errors

Lecture 7: Gradient Descent

Lecture 8: Stochastic Gradient Descent

Lecture 9: Convolutional Neural Networks (CNN)

Lecture 10: Input Layer, Convolutional Layer

Lecture 11: Pooling Layer, Activation Function Layer

Lecture 12: Fully Connected Layers / Dense Layer, Dropout Layer

Lecture 13: Image Classification and its Applications

Lecture 14: How image classification is done?

Lecture 15: Transfer Learning

Lecture 16: Architecture of ResNet (Residual Networks)

Chapter 2: Building, Evaluating and Predicting Image Classification Model

Lecture 1: Download Dataset

Lecture 2: What is inside train folder?

Lecture 3: What is the .hdf5 file?

Lecture 4: What is inside test folder?

Lecture 5: What is inside our_prediction folder?

Lecture 6: Image Classification Python Code

Lecture 7: Enabling GPU in Google Colab

Lecture 8: Is GPU connected to Colab notebook?

Lecture 9: Connect Google Colab with Google Drive

Lecture 10: Check the Number of Images in the Dataset

Lecture 11: Image Augmentation

Lecture 12: Transfer Learning

Lecture 13: Fine Tuning / Freezing of the Layers

Lecture 14: Model Compilation

Lecture 15: Callbacks: EarlyStopping

Lecture 16: Callbacks: ModelCheckpoint

Lecture 17: Training

Lecture 18: Testing

Lecture 19: Prediction

Instructors

Karthik Karunakaran, Ph.D.
Transforming Real-World Problems with the Power of AI-ML

Rating Distribution

1 stars: 0 votes

2 stars: 4 votes

3 stars: 1 votes

4 stars: 7 votes

5 stars: 8 votes

Frequently Asked Questions

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