Best Deep Learning Projects Source Code Free Download

1. IMAGE CLASSIFICATION USING CIFAR 10 DATASET – In this project, you’ll build an image classification system.

This project will help you learn about deep learning subjects because image categorization is quite vital.

Students can start practical deep learning projects by categorizing pictures. CIFAR-10 has 60,000 color photos (3232 sizes) grouped into ten classes of 6,000 images each.

Training set features 50,000 photos, test set has 10,000. Each component of the training set will have 10,000 random photos.

2. DOG BREED IDENTIFICATION – Do you ever wonder about dog breeds? Most dog breeds are alike. Using the dog breeds dataset, we can categorize dog breeds based on an image.

This helps dog lovers.
Convolutional neural networks are an apparent solution for image recognition. Due to the small number of training images, any CNN trained on them would be overfit.

Resnet18’s transfer learning gave my model a head start and reduced training issues. The deep structure allowed the model to reliably identify dogs.

3. HUMAN FACE RECOGNITION – Face detection is a problem in computer vision that involves figuring out who is in a picture.

People can easily solve this problem, and traditional feature-based algorithms like the cascade classifier have done a good job of it.

Deep learning algorithms have recently reached the top level of performance on benchmark face identification datasets.

We can make models that can find the edges of a person’s face with great accuracy.
This project will get you started with object detection and teach you how to find any object in an image.

4. MUSIC GENRE CLASSIFICATION WITH DEEP LEARNINGS – This is a great idea for a deep learning project. You’ll make a deep learning model that uses neural networks to automatically sort music into different genres.

The spectogram of music frames is fed into the model, and a Convolutional Neural Network (CNN) and a Recurrent Neural Network are used to analyze the image (RNN).

The output of the system is a vector of the likely genres of the song. Before testing it on the GTZAN dataset, the model was tweaked with a small sample of 30 songs from each genre.

This gave it an accuracy of 80%.

5. DROWSY DRIVER DETECTION – Drivers who are too tired are one of the main causes of car accidents.

When people drive long distances, it’s normal for them to fall asleep at the wheel. Drivers can get tired for many reasons, such as being stressed or not getting enough sleep.

By making a way to tell if someone is tired, our study hopes to stop and reduce accidents like these. You’ll use Python, OpenCV, and Keras to make a system that can recognize when a driver’s eyes are closed and sound an alarm if they fall asleep at the wheel.

Even if the driver closes his or her eyes for a few seconds, this technology will alert the driver, preventing accidents that could be deadly.

In this project, we will use OpenCV to get pictures from a camera and put them into a Deep Learning model that can tell if a person’s eyes are “Open” or “Closed.”

This project will be done in the following way:

Step 1: Use a picture from a camera as a starting point.
Step 2: Make a Region of Interest around the face in the picture (ROI).
Step 3: Use the return on investment (ROI) to find the eyes and put them into the classifier.
Step 4: The classifier will tell if the eyes are open or closed.
Step 5: Add up the points to find out if the person is tired.

6. BREAST CANCER DETECTION USING DEEP LEARNING – Cancer is a dangerous disease that needs to be caught as soon as possible. Histopathology pictures can be used to figure out if someone has cancer.

Normal cells and cancer cells are different, so we can use an image classification algorithm to find the disease as soon as possible. In this field, Deep Learning models have reached a high level of accuracy.

The data set used to train the model determines how well it works.
Breast cancer is the most common type of cancer in women, and invasive ductal carcinoma is the most common type of breast cancer (IDC).

Automated methods can be used to save time and cut down on mistakes when finding and putting breast cancer subtypes into groups, which is a very important clinical task.

7. GENDER RECOGNITION BY VOICE – By listening to a person’s voice, we can tell with high accuracy what gender they are.

Machines can also be taught to tell the difference between a man’s voice and a woman’s. We’ll need audio clips that say “male” or “female.”

Using techniques for extracting features, the data is then fed into the model that is used to classify the data.

The project’s source code can be found at the link below. You can add more to this project to figure out how the speaker feels.

8. DEEP LEARNING CHATBOX – Using deep learning algorithms to make a chatbot is another great idea.

Chatbots can be used in many different ways, and a smart one will use deep learning to understand the context of the user’s question and give the right answer.

This project is a step-by-step guide for beginners on how to make a chatbot using deep learning, TensorFlow, and an NMT sequence-to-sequence model.

9. COLOR DETECTION WITH DEEP LEARNINGS – The below project can forecast up to 11 Color Classes based on RGB slider input. Red, Green, Blue, Yellow, Orange, Pink, Purple, Brown, Grey, Black, and White.

R: Red, G: Green, B: Blue; Each is an integer from 0 to 255; These combined Red, Green, and Blue values generate a distinct Solid Color for every pixel on a computer, mobile, or electronic screen. This Classifier predicts solid color class.

Also, the color dataset was human-created to make the artificial machine classify colors as humanly as feasible.

10. PLANT DISEASE DETECTION – Plant disease detection is one of the most difficult things to do with technology in agriculture.

Even though research has been done on how Deep Learning and Neural Networks can be used to tell if a plant is healthy or sick, new technologies are always being made.
For this task, you need to make a model that uses RGB photos to predict plant diseases.

A model for finding crop diseases is made with the help of Convolutional Neural Networks (CNN). CNN uses an image to find out who is sick and how sick they are.

There are several steps in a Convolutional Neural Network. Here’s what to do:
1. How Convolution Works.
2. ReLU layer
3. Pooling.
4. Flattening.
5. Full connection.