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Gold Blog8 Deep Learning Project Ideas for Beginners


Have you studied Deep Learning techniques, but never worked on a useful project? Here, we highlight eight deep learning project ideas for beginners that will help you sharpen your skills and boost your resume.



By Aqsa Zafar, Ph.D. Scholar in Machine Learning | Founder at MLTUT | Solopreneur | Blogger.

 

1. Dog’s Breed Identification

 

There are various dog breeds, and most of them are similar to each other. As a beginner, you can build a Dog’s breed identification model to identify the dog’s breed.

For this project, you can use the dog breeds dataset to classify various dog breeds from an image. You can download the dog breeds dataset from Kaggle.

I also found this complete tutorial for Dog Breed Classification using Deep Learning by Kirill Panarin.

 

2. Face Detection

 

This is also a good deep learning project for beginners. In this project, you have to build a deep learning model that detects the human faces from the image.

Face recognition is computer vision technology. In face detection, you have to locate and visualize the human faces in any digital image.

You can build this project in Python using OpenCV. For the complete tutorial, check this article, Real-time Face Recognition with Python & OpenCV.

 

3. Crop Disease Detection

 

In this project, you have to build a model that predicts diseases in crops using RGB images. For building a Crop disease detection model, Convolutional Neural Networks (CNN) are used.

CNN takes an image to identify the disease and detect it. There are various steps in Convolutional Neural Network. These steps are:

  1. Convolution Operation.
  2. ReLU Layer.
  3. Pooling.
  4. Flattening.
  5. Full Connection.

You can download the Agriculture crop images dataset from Kaggle.

 

4. Image Classification with CIFAR-10 Dataset

 

Image classification is the best project for beginners. In an image classification project, you have to classify the images into various classes.

For this project, you can use CIFAR-10 Dataset, which contains 60,000 color images. These images are categorized into 10 classes, such as cars, birds, dogs, horses, ships, trucks, etc.

Source: CIFAR-10 dataset.

For training data, there are 50,000 images, and for test data, 10,000 images are used. Image classification is one of the most used applications of deep learning. You can download the CIFAR-10 dataset here.

 

5. Handwritten Digit Recognition

 

To explore and test your deep learning skills, I think this is the best project to consider. In this project, you will build a recognition system that recognizes human handwritten digits.

You can check this tutorial for Handwritten Digit Recognition using Python.

This tutorial uses the MNIST dataset and a special type of deep neural network that is Convolutional Neural Networks.

 

6. Color Detection

 

This is a beginner-level project where you have to build an interactive app. This app will identify the selected color from any image. There are 16 million colors based on the different RGB color values, but we only know a few colors.

To implement this project, you need to have a labeled dataset of all the colors that we know, and then you need to calculate which color resembles the most with the selected color value.

In order to implement this project, you should be familiar with Computer Vision Python libraries OpenCV and Pandas.

You can check all the details regarding this project here.

 

7. Real-time Image Animation

 

This is an open-source project on computer vision. In this project, you have to perform image animation in real-time using OpenCV. I have taken this image from the project’s GitHub repository.

Source: GitHub.

As you can see in the image, the model mimics the expression of the person in front of the camera and changes the image expression accordingly.

This project is useful, especially if you are planning to enter into the fashion, retail, or advertising industry. You can check the code of this project at GitHub and Colab notebook too.

 

8. Driver Drowsiness Detection

 

Road Accident is a serious problem, and the major reason is the sleepy drivers. But you can prevent this problem by creating a driver drowsiness detection system.

Driver Drowsiness Detection system detects the drowsiness of the driver by constantly assessing the driver’s eyes and alerting him with alarms.

For this project, a webcam is necessary to monitor the driver’s eyes. Python, OpenCV, and Keras are used to alert the driver when he feels sleepy.

You can check this complete project tutorial here, Driver Drowsiness Detection System with OpenCV & Keras.

Original. Reposted with permission.

 

Bio: Aqsa Zafar, Ph.D. scholar in Data Mining researches "Depression Detection from Social Media via Data Mining," and writes about Data Science and machine learning at MLTUT to share knowledge and experience in the field.

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