Top 20 Deep Learning Projects With Source Code

Deep Learning Projects

Despite being a relatively new scientific innovation, the scope of Deep Learning is rapidly expanding. The goal of this technology is to mimic the biological neural network of the human brain. Human brains have neurons that send and receive signals, forming the basis of Neural Networks. While Deep Learning has its roots in the 1950s, it was only recently brought to light by the growth and adoption of Artificial Intelligence and Machine Learning. If you’re new to machine learning, the best thing you can do is brainstorm Deep Learning project ideas. To assist you in your quest, we are going to suggest 20 Deep learning and Neural Network projects.

What is Deep Learning?

Deep learning refers to a class of machine learning techniques that employ numerous layers to extract higher-level features from raw data. Lower layers in image processing, for example, may recognize edges, whereas higher layers may identify human-relevant notions like numerals, letters, or faces. Deep learning uses artificial neural networks, which are supposed to mimic how humans think and learn, as opposed to machine learning, which uses simpler principles. Up until recently, the complexity of neural networks was constrained by processing capacity. Larger, more powerful neural networks are now possible thanks to advances in Big Data analytics, allowing computers to monitor, learn, and react to complicated events faster than people. Image categorization, language translation, and speech recognition have all benefited from deep learning. It can tackle any pattern recognition problem without the need for human intervention.

Use of Deep Learning

We could never have envisaged deep learning applications bringing us self-driving cars and virtual assistants like Alexa, Siri, and Google Assistant just a few years ago. However, these innovations are already a part of our daily lives. Deep Learning continues to fascinate us with its almost limitless applications, including fraud detection and pixel restoration. Apart from these, Deep learning finds its application in the following industries:

  • Chatbots
  • Virtual assistants
  • Entertainment
  • Healthcare
  • Music
  • Robotics
  • Advertising
  • Customer experience
  • Computer vision
  • Language translation

In a real-time work environment, theoretical knowledge alone will not be sufficient. In this article, we’ll look at some fun deep learning project ideas that beginners, as well as experienced, can use to put their skills to the test. The projects covered in this article will serve those who want to get some hands-on experience with the technology. 20 projects along with their GitHub source code link are provided below.

Deep Learning Projects For Beginners

1. Image Classification Using CIFAR-10 Dataset

In this project, you’ll create an image classification system that can determine the image’s class. Because image classification is such an important application in the field of deep learning, working on this project will allow you to learn about a variety of deep learning topics.

Working on image categorization is one of the finest ways to get started with hands-on deep learning projects for students. CIFAR-10 is a big dataset including approximately 60,000 color images (3232 sizes) divided into ten classes, each with 6,000 images. There are 50,000 photos in the training set and 10,000 images in the test set. The training set will be divided into five portions, each containing 10,000 photos that will be organized in random order. The test set will consist of 1000 photos selected at random from each of the ten classes.

Link to the source code

2. Dog’s Breed Identification 

Dog’s Breed Identification 

How frequently do you find yourself wondering about a dog’s breed name? There are numerous dog breeds, and most of them are very similar. Using the dog breeds dataset, we can create a model that can categorize different dog breeds based on an image. Dog lovers will benefit from this endeavor.

To implement this, a convolutional neural network is an obvious solution to an image recognition challenge. Unfortunately, due to the limited number of training examples, any CNN trained just on the provided training images would be highly overfitting. To overcome this, the developer used Resnet18’s transfer learning to give my model a head start and dramatically reduce training challenges. The model was able to be complex enough to accurately identify the dogs thanks to the deep structure.

Link to the source code

3. Human Face Detection

Face detection is a computer vision problem that entails identifying people in photographs. It’s a simple difficulty for people to solve, and classical feature-based algorithms like the cascade classifier have done a good job at it. On typical benchmark face identification datasets, deep learning algorithms have recently attained state-of-the-art results. We can create models that detect the bounding boxes of the human face with excellent accuracy. This project will teach you how to detect any object in an image in general, and get you started with object detection.

Link to the source code

4. Music Genre Classification System

Music Genre Classification System

This is an impressive deep learning project concept. You’ll build a deep learning model that employs neural networks to automatically classify music genres. The model takes as an input the spectogram of music frames and analyzes the image using a Convolutional Neural Network (CNN) plus a Recurrent Neural Network (RNN). The system’s output is a vector of the song’s projected genres. The model has been refined with a tiny sample (30 songs per genre) before testing it on the GTZAN dataset, resulting in an accuracy of 80%.

Link to the source code

Intermediate Deep Learning Projects

5. Drowsy Driver Detection System

One of the leading causes of traffic accidents is driver drowsiness. It’s natural for drivers who travel long distances to fall asleep behind the wheel. Drivers might become tired while driving due to a variety of factors, including stress and lack of sleep. By developing a drowsy detection agent, our study hopes to avoid and reduce such accidents. You’ll use Python, OpenCV, and Keras to create a system that can detect drivers’ closed eyes and alarm them if they fall asleep behind the wheel. Even if the driver’s eyes are closed for a few seconds, this technology will alert the driver, preventing potentially fatal road accidents. We will use OpenCV to collect photos from a camera and feed them into a Deep Learning model that will classify whether the person’s eyes are ‘Open’ or ‘Closed’ in this project. For this project, we’ll take the following approach:

Step 1- Take an image from a camera as input.

Step 2 -Create a Region of Interest around the face in the image (ROI).

Step 3- Use the ROI to find the eyes and input them to the classifier.

Step 4- The classifier will determine whether the eyes are open. 

Step 5- Calculate the score to see if the person is sleepy.

Link to the source code

6. Breast Cancer Detection Ssing Deep Learning

Cancer is a severe disease that needs to be caught as soon as possible. Histopathology photos can be used to diagnose malignancy. Cancer cells differ from normal cells, therefore, we can use an image classification algorithm to identify the disease at the earliest. Deep Learning models have achieved a high level of accuracy in this field. The accuracy of the model depends upon the training data set provided to it.

Breast cancer is the most frequent cancer in women, and the most common type of breast cancer is invasive ductal carcinoma (IDC). Automated approaches can be utilized to save time and reduce errors for detecting and categorizing breast cancer subtypes, which is a crucial clinical activity. 

Link to the source code

7. Gender Recognition Using Voice

Gender Recognition

We can accurately determine a person’s gender by listening to their voice. Machines can also be taught to distinguish between male and female voices. We’ll need audio clips with male and female gender labels. The data is then fed into the classifying model using feature extraction techniques. The link to the source code of the project has been provided below. This project can be extended further to identify the mood of the speaker.

Link to the source code

8. Chatbot

Chatbots

Making a chatbot using deep learning algorithms is another fantastic endeavor. Chatbots can be implemented in a variety of ways, and a smart chatbot will employ deep learning to recognize the context of the user’s question and then offer the appropriate response.

The project given below is a beginner’s walk-through tutorial on how to build a chatbot with deep learning, TensorFlow, and an NMT sequence-to-sequence model.

Link to the source code

9. Color Detection System

Color Detection System

The project given below can predict up to 11 Distinct Color Classes based on the RGB input by users from the sliders. Red, Green, Blue, Yellow, Orange, Pink, Purple, Brown, Grey, Black, and White are the 11 classes. R: Red, G: Green, B: Blue; Each of which is basically an integer ranging from 0 to 255; and these combined Red, Green, and Blue values are utilized to form a distinct Solid Color for every pixel on the computer, mobile, or any electronic screen. This Classifier predicts the solid color’s color class. Also, the color dataset has been humanly developed to make the artificial model(classifier) classify the colors as humanly as possible.

Link to the source code

10. Crop Disease Detection

Crop Disease Detection

When it comes to using technology in agriculture, one of the most perplexing issues is plant disease detection. Despite the fact that research has been done to determine whether a plant is healthy or diseased utilizing Deep Learning and Neural Networks, new technologies are continually being developed.

You must create a model that uses RGB photos to forecast illnesses in crops for this assignment. Convolutional Neural Networks (CNN) are utilized to create a crop disease detection model. CNN uses an image to identify and detect sickness. In a Convolutional Neural Network, there are several steps. These are the steps:

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

Link to the source code

Advanced Deep Learning Projects

11. OCR (Optical Character Reader) Using YOLO and Tesseract for Text Extraction

Extracting information from any document is a difficult operation that requires object classification and object localization. In many financial, accounting, and taxation fields, OCR digitization addresses the difficulty of automatically extracting, which plays a significant role in speeding document-intensive operations and office automation.

This custom OCR combines YOLO and Tesseract to read the contents of a Lab Report and convert it to an editable format. In this case, the developer of the project has utilized YOLO V3 that was trained on a personal dataset. The coordinates of the discovered objects are then supplied to cropping and storing the detected objects in another list. To get the required output, this list is fed into the Tesseract.

Link to the source code

12. Real-Time Image Animation

This is an open-source computer vision project. You must use OpenCV to accomplish real-time image animation in this project.  The model modifies the image expression to match the expression of the person in front of the camera.

Using this repository, you will be able to make face image animations using a real-time camera image of your face, from a webcam animation or, if you already have a video of your face, you may use that to make face image animations. This assignment is particularly valuable if you aim to work in the fashion, retail, or advertising industries. This project’s code is available on GitHub.

Link to the source code

13. Store Item Demand Forecasting

13. Store Item Demand Forecasting

Building a forecasting model to estimate store item demand is difficult due to numerous external factors such as the store’s location, seasonality, changes in the store’s neighborhood or competitive position, a considerable variance in the number of consumers and goods, and so on. With such a large volume of data, no human planner could possibly examine all of the possible elements. Deep learning, on the other hand, makes it easier by taking these characteristics into account at a finer level, by individual store or fulfillment channel.

Link to the source code

14. Fake News Detection Project

Fake News Detection

Consumers can now get the most up-to-date news at their fingertips thanks to the digital age of mobile applications. But, are the things we read on these sites always accurate? No, that is not the case. Take, for example, our favorite chat application WhatsApp in real-time. You would have gotten a lot of notifications about how to cure and prevent the COVID-19 virus. These messages are frequently fraudulent, and the terrible aspect is that many people believe them and even follow them, which has led to some dangerous outcomes. AI is being used by companies such as Facebook, Google, and others to detect and remove false news from their platforms.

There are a variety of approaches for attaining this goal, but the goal of this effort is to identify the fishy ones solely by glancing at the text. There are no graphs, social network analysis, or photos. Three deep learning architectures are presented in this paper and then tested on two datasets (the fake news corpus and the TI-CNN), yielding state-of-the-art results.

  • LSTM (Long Short Term Memory) Based architecture
  • CNN (Convolutional Neural Network) Based architecture
  • BERT (Bidirectional Encoder Representations from transformers) Based architecture

Link to the source code

15. Coloring Old Black and White Photos

Automated picture colorization of black-and-white photos has become a prominent topic in computer vision and deep learning research. Image colorization takes a grayscale (black and white) image as an input and outputs a colorized version of an old movie image.  The output colorized film’s image should represent and match the semantic colors and tones of the input.

The network is built in four parts and gradually becomes more complex.

  1. The alpha network deals with how an image is transformed into RGB pixel values and later translated into LAB pixel values, changing the color space. It also builds a core intuition for how the network learns. 
  2. The network in the beta version is very similar to the alpha version. The difference is that we use more than one image to train the network. 
  3. The full version adds information from a pre-trained classifier. You can think of the information as 20% nature, 30% humans, 30% sky, and 20% brick buildings. It then learns to combine that information with the black and white photo.
  4. The GAN version uses Generative Adversarial Networks to make the coloring more consistent and vibrant. 

Link to the source code

16. Human Pose Detection

Human Pose Detection

Humans are expressive beings. This project was developed using deep learning concepts and it  can detect the pose you make in front of the camera. Several methods for predicting Human Pose Estimation have been proposed. These algorithms frequently start by identifying the component parts, then understand the connections between them to estimate the pose. Activity Recognition, Motion Capture and Augmented Reality, Training Robots, and Motion Tracking for Consoles in the game industry are just a few of the real-world applications of knowing a person’s orientation.

Link to the source code

17. Language Translator Using Deep Learning

Language Translator

Have you ever traveled to a new location and struggled to communicate in the native tongue? I’m sure you’ve tried to imitate the local language and accent with Google Translator at least once. Machine Translation (MT) is a popular topic of computer linguistics that focuses on translation from one language to another. NMT (Neural Machine Translation) has become the most effective method for performing this task as deep learning has grown in popularity and efficiency. We’ve all used Google Translator, which is the industry’s premier machine translation example. An NMT model’s main goal is to take a text input in any language and translate it into a different language as an output.

The developer of the current project has used RNN sequence-to-sequence learning in Keras to translate the English language to the French language.

Link to the source code

18. Typing Assistant

Typing Assistant

Devices these days are capable of finishing our sentences even before we type them. Google began automatically finishing my sentence as soon as I started entering the title  “Auto text completion and creation with De…” It correctly predicted Deep Learning in this scenario! 

The project given below provides the ability to autocomplete words and predicts what the next word will be. This allows you to type faster, more intelligently, and with less effort.

The methodology used to implement the project is as follows:

  1. Counting words in Corpora: Counting of things in NLP is based on a corpus. NLTK (Natural Language Toolkit) provides a diverse set of corpora.
  2. N-Gram model: Probabilistic models are used to compute the likelihood of a complete sentence or to make a probabilistic prediction of the next word in a sequence. In this model, the conditional probability of a word is calculated based on the preceding words.
  3. Bigram model: In this model, we approximate the probability of a word given all the previous words by the conditional probability of the preceding word.
  4. Trigram model: A trigram model looks just the same as a bigram model, except that we condition on the two-previous words.
  5. Minimum Edit Distance: The minimum edit distance between two strings is a measurement of how similar two strings are to one another.

Link to the source code

19. Hand Gesture Recognition System

Hand Gesture

Suppose you want to create a cool feature in a smart TV that recognizes five various gestures made by the user and allows them to operate the TV without using a remote.

The webcam positioned on the TV continuously monitors the movements. Each gesture is associated with a distinct command:

  • Increase the volume, please.
  • Reducing the volume is a no-no.
  • 10 seconds ‘Jump’ backward with the left swipe
  • ‘Jump’ forward 10 seconds with a right swipe
  • Stop: Put the movie on hold.

The project given below achieves that by using training data that consists of a few hundred videos categorized into one of the five classes. Each video (typically 2-3 seconds long) is divided into a sequence of 30 frames(images). These videos have been recorded by various people performing one of the five gestures in front of a webcam – similar to what the smart TV will use.

Link to the source code

20. Lane Detection and Assistance System

Automatic driving technology has advanced rapidly in recent years. One of the major concerns in the manufacturing of self-driving cars is the detection of the lane line. The given project is the implementation of lanenet model for real-time lane detection using a deep neural network model. In this project, you will implement a Deep Neural Network for real-time lane detection using TensorFlow, based on an IEEE IV conference article.  For a real-time lane detection task, this model includes an encoder-decoder stage, a binary semantic segmentation stage, and instance semantic segmentation using a discriminative loss function

Link to the source code

Conclusion

We have collected 20 deep learning projects that you can develop to polish your skills and improve your portfolio. The technology is still in its infancy; it is continually evolving as we speak. Deep Learning has enormous potential for spawning ground-breaking ideas that can aid humanity in addressing some of the world’s most pressing issues.

Frequently Asked Questions

How do I start a deep learning project?

You can always start with small projects and then move on to tough ones once you are confident enough.

What is CNN deep learning?

A Convolutional Neural Network (ConvNet/CNN) is a Deep Learning system that can take an input image, assign relevance (learnable weights and biases) to various aspects/objects in the image, and distinguish between them.

What is Keras API?

Keras is a Python-based deep learning API that runs on top of TensorFlow, a machine learning platform. It was created with the goal of allowing for quick experimentation.

What is Kaggle used for?

Kaggle is a website where you may share ideas, get inspired, compete against other data scientists, acquire new information and coding methods, and explore real-world data science applications.

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