Deep Learning Python Project: CNN based Image Classification

Name: Deep Learning Python Project: CNN based Image Classification
Rating: 4.1 (301 reviews)

Master Image Classification with CNN on CIFAR-10 dataset: A Deep Learning Project for Beginners using Python

Created byDr. Raj Gaurav Mishra

Last updated 8/2024

English

What you'll learn

Understand the fundamentals of Convolutional Neural Networks (CNNs)
Learn how to preprocess image data for deep learning tasks
Implement a CNN model architecture for image classification from scratch
Train and evaluate CNN models using the CIFAR-10 dataset
Learn how to implement Hyperparameter Tunning within a CNN model architecture
Gain practical experience in building and deploying image classification models
Add this as a Deep Learning portfolio project to your resume

Course content

5 sections • 11 lectures • 1h 27m total length

Course Introduction1:42
Explore practical deep learning for image classification with CNNs on CIFAR-10, from fundamentals to coding and interpreting results. Enhance performance through hyperparameter tuning and LeNet-5 assignments.

Brief Introduction to Convolutional Neural Networks (CNN)9:28
Explore what CNNs are and how they learn. Learn the CNN architecture, including convolutional, pooling, and fully connected layers, for binary and multiclass classification, segmentation, and medical imaging.
Overview of CIFAR-10 Dataset3:09
Explore the CIFAR-10 dataset: 60,000 color images of 32x32 across 10 classes, with 50,000 training and 10,000 testing, normalized to 0-1 for cnn image classification.

Coding Part 1: Steps Involved4:38
Implement a CNN-based image classifier on CIFAR-10 through 11 steps, from importing libraries to building, compiling, training, evaluating, and visualizing model performance.
Coding Part 2: Image classification using custom CNN Model26:36
Construct a custom convolutional neural network for image classification on cifar-10 using TensorFlow and Keras, covering data loading, normalization, model building, training, and evaluation.

Overview of Basic Hyperparameter Tuning in CNN7:17
Learn basic CNN hyperparameter tuning by optimizing filters, dense units, and dropout with learning rate choices, without changing architecture, using grid, random, or Bayesian search on CIFAR-10.
Coding Part 3: CNN with basic Hyperparameter Tunning18:20
Explore image classification with a convolutional neural network on CIFAR-10 and tune hyperparameters with Keras Tuner random search, adjusting convolutional layers, dense units, dropout, and learning rate for optimal accuracy.
Overview of Advanced Hyperparameter Tuning in CNN4:04
Explore hyperparameter tuning for a cnn on CIFAR-10, selecting 1–3 convolutional and dense layers, 3x3 filters, relu, 2x2 pooling, dropout, and learning rates 0.01, 0.001, 0.0001 to optimize image classification.
Coding Part 4: CNN with advanced Hyperparameter Tunning7:42
Explore CNN based image classification on CIFAR-10 with advanced hyperparameter tuning, building a tunable model with 1–3 convolutional layers and 1–3 dense layers, with dropout and max pooling.

Requirements

A computer and an internet connection to watch this course.
Basic understanding of Python Programming.
Basic understanding of Deep Learning, although we will cover fundamental concepts.
No software development experience needed, you will learn everything you need to know.

Description

Who is the target audience for this course?

This course is designed for beginners who are eager to dive into the world of deep learning and artificial intelligence. If you are a student, an aspiring data scientist, or a software developer with a keen interest in machine learning and image processing, this course is perfect for you. No prior experience with deep learning is required, but a basic understanding of Python programming is beneficial.

Why this course is important?

Understanding deep learning and convolutional neural networks (CNNs) is essential in today’s tech-driven world. CNNs are the backbone of many AI applications, from facial recognition to autonomous driving. By mastering image classification with CNNs using the CIFAR-10 dataset, you will gain hands-on experience in one of the most practical and widely applicable areas of AI.

This course is important because it:

Provides a solid foundation in deep learning and image classification techniques.
Equips you with the skills to work on real-world AI projects, enhancing your employability.
Offers a practical, project-based learning approach, which is more effective than theoretical study.
Helps you build an impressive portfolio project that showcases your capabilities to potential employers.

What you will learn in this course?

In this comprehensive guided project, you will learn:

Introduction to Deep Learning and CNNs:
- Understanding the basics of deep learning and neural networks.
- Learning the architecture and functioning of convolutional neural networks.
- Overview of the CIFAR-10 dataset.
Setting Up Your Environment:
- Installing and configuring necessary software and libraries (TensorFlow, Keras, etc.).
- Loading and exploring the CIFAR-10 dataset.
Building and Training a CNN:
- Designing and implementing a convolutional neural network from scratch.
- Training the CNN on the CIFAR-10 dataset.
- Understanding key concepts such as convolutional layers, pooling layers, and fully connected layers.
Evaluating and Improving Your Model:
- Evaluate the performance of your model using suitable metrics.
- Implementing techniques to improve accuracy and reduce overfitting.
Deploying Your Model:
- Saving and loading trained models.
- Deploying your model to make real-time predictions.
Project Completion and Portfolio Building:
- Completing the project with a polished final model.
- Documenting your work to add to your AI portfolio.

By the end of this course, you will have a deep understanding of CNNs and the ability to apply this knowledge to classify images effectively. This hands-on project will not only enhance your technical skills but also significantly boost your confidence in tackling complex AI problems. Join us in this exciting journey to master image classification with CNNs on CIFAR-10!

Who this course is for:

Beginners interested in deep learning and image classification.
Data science enthusiasts looking to expand their skills in computer vision.
Students or professionals seeking hands-on experience with CNNs.
Developers interested in building practical deep learning projects.
Anyone aiming to enhance their understanding of CNNs through a guided project.
Anyone willing to add a Deep Learning portfolio project to his/her resume.

Deep Learning Python Project: CNN based Image Classification

What you'll learn

Explore related topics

Course content

Introduction to the Course1 lecture • 2min

Fundamentals of CNN and Overview of the Dataset2 lectures • 13min

Image classification using custom CNN Model on CIFAR-10 Dataset2 lectures • 31min

Image classification using custom CNN Model with Hyperparameter Tunning4 lectures • 37min

Assignment: Image classification using LeNet-5 CNN Model on CIFAR-10 Dataset2 lectures • 5min

Requirements

Description

Who this course is for: