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HomeBlogData ScienceTop 15 Deep Learning Projects in 2024 [With Source Code]
In recent years, the field of deep learning has gained immense popularity and has become a crucial subset of artificial intelligence. This branch of AI trains artificial neural networks to perform complex tasks such as image classification, speech recognition, and prediction. Data Science aspirants should learn Deep Learning after taking a Data Science certificate online , which would enhance their skillset and create more opportunities for them. Regardless of whether you are a seasoned machine learning expert or just starting, this blog will offer you valuable insights and inspiration for your next project.
Deep learning projects are applications or systems that use deep learning algorithms to perform complex tasks such as image recognition, natural language processing, speech recognition, and prediction.
Now, let’s look at some of the top deep learning projects.
Here is a list of deep learning project topics and ideas that delve into various domains, offering hands-on experience in cutting-edge AI technologies and applications.
| Beginner Level Projects | Intermediate Level Projects | Advanced Level Projects |
| Diabetes Binary Classification on Pima Indians dataset | Handwritten Digit Recognition | Sequence Classification of Movie Reviews |
| Multiclass Classification Of Flower Species | Object Recognition in Photographs | Text Generation With Alice in Wonderland |
| Binary Classification Of Sonar Returns | Predict Sentiment From Movie Reviews | OCR (Optical Character Reader) |
| Regression Of Boston House Prices | Drowsy Driver Detection System | Hand Gesture Recognition System |
| Dog’s Breed Identification | Gender Recognition Using Voice | Lane Detection and Assistance System |
Looking for inspiration for your deep learning projects for the final year with source code? Continue to read this blog, where you can find a variety of deep learning projects with accompanying source code to get you started!
Our first project in this blog will involve the Pima Indians onset of diabetes dataset, a commonly used machine learning dataset available for free download from the UCI Machine Learning repository. This dataset contains medical record data for Pima Indians and indicates whether they had an onset of diabetes within five years. It is a binary classification problem, with the onset of diabetes labeled as 1 and no onset as 0.
For the next project, we will use the standard machine learning problem called the iris flowers dataset. This dataset can be downloaded from the UCI Machine Learning repository.
This dataset is well studied and is a good problem for practicing on neural networks because all of the 4 input variables are numeric and have the same scale in centimeters. Each instance describes the properties of an observed flower measurements, and the output variable is a specific iris species.
In this project, you will learn how to create a neural network for binary classification on the Sonar dataset. The dataset describes sonar chirp returns bouncing off different surfaces and contains 60 input variables representing the strength of the returns at different angles. The goal is to differentiate rocks from metal cylinders.
The Boston house-price dataset describes properties of houses in Boston suburbs and is concerned with modeling the price of houses in those suburbs in thousands of dollars. As such, this is a regression predictive modeling problem.
To address the issue of varying input attribute scales in the Boston house price dataset, it is recommended to standardize the data before modeling it using a neural network. This can improve the accuracy of the model and make it more effective.
Have you ever found yourself pondering about a dog's breed? With so many different breeds out there, it can be challenging to tell them apart. Luckily, with the dog breeds dataset, you can now create a model to identify different dog breeds based on an image. This will be a boon for all dog lovers out there!
To accomplish this task, a convolutional neural network (CNN) is the obvious solution for image recognition. However, due to the limited number of training examples, any CNN trained solely on the provided training images would be highly prone to overfitting. To overcome this issue, the developer utilized transfer learning using Resnet18 to give their model a head start and to significantly reduce training challenges. This allowed the model to be complex enough to accurately identify the dogs, thanks to the deep structure.
These are some deep learning project ideas for final year and beginners alike; now let us discuss some intermediate projects.
A popular demonstration of the capability of deep learning techniques is object recognition in image data. The world of object recognition for machine learning and deep learning is the MNIST dataset for handwritten digit recognition. You can get good results using a very simple neural network model with a single hidden layer that can be used as a baseline for comparison to more complex convolutional neural network models.
You can further go ahead and take a look at a model capable of close to state-of-the-art results by defining a larger CNN architecture with additional convolutional, max-pooling layers and fully connected layers.
A difficult problem where traditional neural networks fall down is called object recognition. It is where a model is able to identify objects in images. The problem of automatically identifying objects in photographs is difficult because of the near-infinite number of permutations of objects, positions, lighting, and so on. It’s a really hard problem. This is a well-studied problem in computer vision and more recently an important demonstration of the capability of deep learning.
Sentiment analysis is a popular natural language processing task where text is analyzed to determine the underlying sentiment or intent. The Large Movie Review Dataset, also known as the IMDB dataset, is commonly used in sentiment analysis tasks.
To get started, you can build a simple Multilayer Perceptron model with a single hidden layer for sentiment analysis on this dataset.
The Drowsy Driver Detection System project aims to tackle one of the most significant causes of road accidents: driver drowsiness. It's not uncommon for drivers to feel fatigued after traveling long distances, leading to potentially fatal consequences if they fall asleep behind the wheel. Our study seeks to prevent such occurrences by developing a drowsy detection agent that can detect closed eyes and alert drivers if they're at risk of dozing off.
To accomplish this, we'll be using Python, Keras, and OpenCV. The process involves collecting images from a camera and feeding them into a deep learning model, which classifies whether the driver's eyes are open or closed. This is done by creating a Region of Interest (ROI) around the face in the image, locating the eyes within the ROI, and passing them through the classifier to determine if they're open or closed. Based on this information, a score is calculated to determine if the person is in a drowsy state and if so, an alarm will be activated to alert them.
It is possible to identify the gender of a person by analyzing their voice. With the help of machine learning, you can train models to differentiate between male and female voices. This requires a dataset of audio clips, each labeled as male or female. Feature extraction techniques are then applied to the data to feed into the classifying model. It is worth noting that this project can be expanded to identify the speaker's mood as well. This can be a rewarding deep-learning project for master's students as well.
In this section, you'll find a collection of deep learning advanced projects complete with source code to help you explore and develop your skills in this exciting field.
Sequence classification is a challenging task in which a model must accurately predict a category for a given sequence of inputs that can vary in length and composition. Recurrent Neural Networks like LSTM are often used for sequence classification, but they are prone to overfitting. To address this issue, you use dropout layers in the model to prevent overfitting by randomly removing neurons during training.
Recurrent neural networks can be used as generative models, meaning they can learn sequences of a problem and generate new sequences for the problem domain. This can help us not only to study how well a model has learned a problem but also to learn more about the problem domain. For this, you develop a small LSTM recurrent neural network that can generate text. You can also use a larger LSTM RNN and experiment with different techniques to improve the model, such as predicting fewer characters, removing punctuation from the source text, using one-hot encoding for input sequences, training the model on padded sentences, increasing the number of epochs, adding dropout to the input layer, tuning the batch size, adding more memory units and layers, experimenting with temperature, and changing the LSTM layers to be stateful. These techniques can help you to further improve the model and generate even more accurate and plausible sequences.
Extracting information from documents can be a challenging task, involving object classification and localization. However, with the advancement of OCR digitization technology, this task has become easier, particularly in the financial, accounting, and taxation fields, where it plays a significant role in expediting document-intensive operations and office automation.
To tackle this challenge, a custom OCR has been developed that combines YOLO and Tesseract to read the contents of a Lab Report and convert it to an editable format. The project's developer has utilized YOLO V3, which was trained on a personal dataset, to detect objects and their coordinates. The detected objects are then cropped and stored in another list, which is fed into Tesseract to obtain the required output.
Suppose you have a smart TV and you want to introduce a cool feature that enables users to operate the TV with hand gestures instead of a remote. To achieve this, the TV is equipped with a webcam that continuously monitors the user's movements, and each gesture is associated with a specific command, such as increasing or decreasing the volume, moving forward or backward in a video, or pausing the playback.
To develop this feature, a project has been created that uses a training dataset consisting of a few hundred videos categorized into one of five classes based on the gesture performed. Each video is typically 2-3 seconds long and divided into a sequence of 30 frames (images). These videos were recorded by various individuals performing one of the five gestures in front of a webcam, similar to what will be used. This training data is then used to teach the smart TV to recognize and associate the different gestures with the corresponding commands.
The automotive industry has made significant strides in autonomous driving technology in recent years. One of the most important aspects of developing self-driving cars is detecting the lane lines on the road. This project involves the implementation of a lanenet model, which uses a deep neural network for real-time lane detection. The model is based on an article presented at the IEEE Intelligent Vehicles Symposium (IV).
To accomplish the real-time lane detection task, this model includes several stages, including an encoder-decoder stage, a binary semantic segmentation stage, and instance semantic segmentation. These stages utilize a discriminative loss function. The model is implemented using TensorFlow, a popular deep-learning framework. By accurately detecting lane lines in real-time, this model could greatly enhance the safety and reliability of autonomous driving technology.
We covered some of the best deep learning projects; here are some additional topics for open-source deep learning project ideas:
There are several reasons why you should consider working on deep learning-based projects:
This blog contains many ideas of deep learning projects for resume, that any student or professional can add to their resume to stand out from other candidates.
Some best platforms where one can leverage deep learning effectively are:
Deep learning is a rapidly evolving field that has become essential in various industries, from healthcare to finance and entertainment. Learning deep learning the smart way involves understanding the core concepts and fundamentals first, followed by hands-on experience with real-world applications. Enrolling in reputable online courses, such as the Deep Learning Course offered by KnowledgeHut, is recommended as a starter. This course provides a strong fundamental understanding of deep learning concepts and techniques, allowing beginners to gain the necessary knowledge to explore this field further.
Once a solid foundation in deep learning has been established, it is beneficial to expand knowledge and skills by pursuing additional certifications. The KnowledgeHut Data Science certificate online is a valuable option to consider. This certificate program focuses on broader data science concepts, providing learners with a comprehensive understanding of the field. By completing this program, individuals can enhance their expertise in data science and gain a well-rounded skill set that complements their deep learning knowledge.
A good deep learning project should involve a problem that interests you and for which there is enough data available. Start by researching existing literature and related work in your chosen domain. Once you have identified your problem and collected your data, decide on a model architecture that best fits your needs. Develop and test your model on your dataset, and then refine it based on your results. Finally, present your findings and conclusions in a clear and organized manner.
Remember to document your work and keep track of your progress throughout the project to ensure successful execution.
Yes, NASA uses deep learning in various applications such as space exploration, satellite image analysis, and robotics. For example, deep learning algorithms are used to analyze satellite images to detect and predict natural disasters like hurricanes, floods, and wildfires.
Some deep learning examples in real life include image recognition, natural language processing, and speech recognition.
Deep learning has a wide range of applications across various industries. Some examples of projects that can be built with deep learning include image and speech recognition, natural language processing, recommendation systems, fraud detection, object detection and tracking, autonomous vehicles, and predictive maintenance. Deep learning can also be used for medical imaging analysis, drug discovery, and genomics. In the field of finance, deep learning can be applied to stock market analysis, portfolio optimization, and credit risk assessment. The possibilities are endless, and with the availability of open-source libraries and cloud computing, it's easier than ever to build deep learning projects.
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