Top 20 Deep Learning Projects with Source Code

A few years ago, self-driving cars and digital assistants like Alexa, Siri, and Google Assistant were unimaginable. However, these inventions are now a part of our daily existence. How did this happen? It was made possible through Deep Learning. 

Deep Learning has revolutionized various sectors of the economy and has led to significant breakthroughs in computer vision, natural language processing, speech recognition, and recommendation systems. It utilizes neural networks to analyze large amounts of data. In this blog, we will learn Deep Learning projects with source code along with proper examples.

What is Deep Learning?

Deep learning is a category of ‘Machine Learning Methods’ that uses multiple layers to separate higher-level features from unstructured input. Instead of using machine learning, which relies more on basic concepts, Deep Learning makes use of artificial neural networks. It has been useful for speech recognition, language translation, and image categorization. It is also capable of dealing with any pattern recognition challenge without the need for human involvement.

Also Read: Deep Learning Vs Machine Learning

How to Start With Deep Learning Project Ideas?

If you wish to work on an independent deep-learning project, here are some steps you can follow: 

• Understand the Basics: Learn the fundamental concepts of deep learning, like convolutional neural networks (CNNs) and recurrent neural networks (RNNs) to get an understanding of the domain. Further, work on your programming skills and familiarize yourself with deep learning frameworks like TensorFlow, Keras, and more. 
• Choose a Specialized Domain: Deep learning is a vast field encompassing several domains like audio processing, natural language processing, facial recognition, object recognition, autonomous vehicles, etc. Choose a domain you wish to work on for your project.
• Select a Project Idea: Now, select a project idea you want to develop. Begin with simple beginner-friendly projects and proceed to more advanced ones. 
• Collect Relevant Data: Once you have a project idea, gather relevant data sets to work on. You can use public datasets from Kaggle, Google Dataset Search, etc. 
• Plan Your Project: Next, plan your project. State objectives, what deep learning model to use, and design the neural network architecture. 
• Write the Code: Use the coding language you are proficient in and begin writing the code. Design and modify the project architecture according to your plan. 
• Test Your Project: Use the selected datasets to evaluate the performance of your project. Identify areas with scope for improvement and optimize the project accordingly. 
• Document Your Changes: Record the changes in the project with the help of version control platforms like Github. It will help you keep track of the changes as well as receive feedback on your project. 

Basic Deep Learning Projects For Beginners

These projects are beneficial for first-year students or individuals transitioning to the deep learning domain. They will allow you to apply the concepts and techniques learned through coursework into practical applications. Further, it will foster a deeper understanding of complex concepts and lay a strong foundation for future learning. So, if you’re just starting out as a beginner in the deep-learning space, here are some basic deep-learning project ideas for you:

1. Cat vs Dog Classification 

Objectives:

The project is to create a deep learning model that uses pictures of cats and dogs as input, a few image processing techniques, and a prediction algorithm to determine if the incoming image is of a cat or a dog. 

Convolutional neural networks (CNN) are essential for projects like this that pertain to image classification. This is because they are capable of automatically learning features from raw pixel data. Working on this project will help you in learning how to create a neural network for classification and prepare the dataset for training and testing. 

You can use Asirra (animal species image recognition for restricting access), a popular dataset for this project. It has 25,000 images with an equal number of cats and dogs.

Salient Features:

• Resizes and normalizes images for model input.
• Accurately classifies if the image is of a dog or cat.
• Takes into consideration the changes in brightness and contrast of images.
• You can integrate your project code into other projects to extend its application to a website or mobile application. 
• You can test your own images to verify the accuracy of the model.

Technologies Required:

• VGG16
• Keras
• TensorFlow
• OpenCV

Source Code. 

To learn more about Deep Learning and how it can be used in projects, you can pursue a Deep Learning course.

2. Face Detection System 

Objectives: Develop a deep learning model that scans images and identifies people’s faces to match them with the database’s face data. When there is a match, the system will display the name of the individual identified. 

Convolutional neural networks (CNNs) facilitate the system to process visual data and accurately detect data. You can improve the accuracy of your project with the help of deep learning techniques like the single shot multibox detector (SSD), or you only live once (YOLO). 

Face recognition is a subset of object recognition where the focus is on observing the instance of semantic objects. The application of this project is wide-ranging, from facial recognition for security purposes to sentiment analysis. Therefore, it is used in various industries like marketing, healthcare, e-commerce, social media, etc. 

Salient Features:

• It identifies and verifies individuals based on their facial features.
• It is capable of detecting faces in real-time.
• It incorporates a database for storing and retrieving facial data. 

Technologies Required:

• Python
• TensorFlow
• Dlib
• FaceNet
• OpenCV

Source Code

3. Image Classification Using CIFAR – 10 Dataset 

Objectives: This project aims to create a system for categorizing images that can assess the input images and determine which class they fall under. Further, the purpose of this project is to develop an image classification program that can be trained on the well-known CIFAR-10 dataset. It is one of the most widely used picture libraries for computer vision algorithm training. 

Image classification is an essential application in deep learning and will help you gain an understanding of fundamental concepts of the domain. 

In this project, there are different training and test sets. The CIFAR-10 dataset has 60,000 images out of which 50,000 are in the training set. This set is divided into five different sections containing 10,000 images each. The rest of the 10,000 images from the CIFAR dataset are in the test set. 

Salient Features:

• It classifies the image into one of the CIFAR-10 dataset categories.
• This dataset contains 60,000 photos divided into 10 classifications. 
• The classes in the dataset used in the project are mutually exclusive, ensuring no overlapping and accurate results.

Technologies Required:

• Python
• Matplotlib
• NumPy
• TensorFlow
• Keras

Source Code

Deep Learning Project For Intermediate Level

Deep learning projects provide valuable practical experience to students. You can view these as an opportunity to strengthen your expertise and expand your skill set. These projects are crucial for career advancement as they highlight your proficiencies and abilities. Here are some projects on deep learning that you can explore if you’re already familiar with the concepts and fundamentals of deep learning:

4. Chatbot 

Objectives: Due to its capacity to automate a variety of customer care operations, chatbots have grown to be fairly popular among companies. In this project, you will be creating a chatbot that can mimic human conversations using text messaging. The chatbot needs to be trained and set up so that it can respond to people appropriately.

You can use deep learning neural networks, artificial intelligence, and sequence-to-sequence models to ensure your chatbot generates contextually relevant answers. Further, you can train your system in any particular niche, like virtual assistants or language trainers. It can continuously improve its answers by adapting to the user inputs. 

Salient Features:

• It is designed to build conversational AI applications.
• It includes several pre-trained models to perform tasks like sentiment analysis, entity recognition, etc. 
• It supports multiple languages.

Technologies Required:

• Natural Language Processing Libraries
• Python
• Keras
• Docker
• spaCy

Source Code

5. Digit Recognition System 

Objectives: In this project, you will create a model that can recognize handwritten numbers. The MNIST digit classification dataset is the most common dataset available for this use. You may train and test your model using 70,000 photos of handwritten numbers from this dataset.

The project uses shallow and deep neural networks along with logistic regression to accurately classify the digits. You can use multinomial logistic regression or softmax regression here because it is ideal for multi-class classification (if all the clauses are mutually exclusive). 

Salient Features:

• It identifies and classifies handwritten digits from 0-9.
• It has real-time digit recognition functionality.
• The image is normalized and standardized before processing. 

Technologies Required: 

• Convolutional Neural Networks (CNNs)
• Python
• PyTorch
• MNIST dataset
• OpenCV

Source Code

6. Gender Recognition Using Voice

Objectives: Develop a deep learning project that can recognize a voice and identify if it is a man’s or a woman’s voice. To do this, male and female-gender-labeled audio samples are required, and the machines have to be extensively trained to give the correct output. 

If you do not wish to use audio files of varied lengths, you can convert them into fixed-length vectors suitable for classification with feature extraction. Mel Spectrogram and MFCC are common types of feature extraction methods you can use in this project. 

Salient Features:

• It accurately classifies a voice as a male or female based on its acoustic properties. 
• The model can be trained to identify the mood of the speaker.
• It is compatible with various audio file types, expanding the range of inputs.

Technologies Required: 

• Python
• Scikit-learn
• LibROSA (for music and audio analysis)
• Pandas 
• NumPy

Source Code

7. Face Mask Detection

Objectives: Develop a deep learning model that can detect whether an individual in the image or video is wearing a face mask. It aims to assist in public health safety measures. Deploying this detector to embedded devices can reduce the cost of manufacturing physical mask detection systems. 

You must train your model as a two-class object detector comprising a with_mask class and a without_mask class. This will ensure that the detector can successfully detect in case the face is covered. 

Salient Features:

• Capable of detecting if someone is wearing a face mask in real-time on live video feeds.
• Works with both still images and video streams.
• User-friendly interface for ease of use.
• Option to deploy the mask detection system into real-world applications.

Technologies Required:

• Python
• OpenCV
• TensorFlow
• Keras
• MobileNetV2
• Haar Cascades 

Source Code

8. Cyber Attack Prediction

Objectives: You should select a deep learning model for your project based on the requirements. Since you need the model to classify the network traffic, convolutional neural networks (CNN) will be the best choice. Once the network data is collected and pre-processed, the model will identify the most significant features indicative of an attack. CNN will help recognize the pattern and facilitate easy and accurate prediction of cyber attacks. 

Salient Features:

• Accurately classifies the cyber attacks into different categories.
• It can visualize and interpret patterns in data to understand complex attacks.
• It can continually be trained with new data to detect new attacks.

Technologies Required:

• Python 
• Scikit-learn
• Pandas
• NumPy
• Matplotlib

Source Code

Deep Learning Projects For Final Year Students

In most of the tech job interviews, recruiters ask for a portfolio to analyze your technical expertise in the domain. You can work on different deep-learning projects to build this portfolio and stay a step ahead of the competition. Here are some advanced-level deep-learning projects that you can practice in the final year of your college:

9. Store Demand Forecasting 

Objectives: In this project, you will build a model to estimate the demand forecast for a shop or a store. This model will take into account the sales, store information, and other factors like holidays and promotions.

Some common deep learning models used for time series forecasting include: 

• Long Short-Term Memory (LSTM)
• Convolutional Neural Networks (CNN)
• Gated Recurrent Units (GRU) 

You should train your model using a regression framework to predict continuous sales values. Further, to validate your model’s performance, you can use metrics like MAPE (Mean Absolute Percentage Error), MAE (Mean Absolute Error), or RMSE (Root Mean Squared Error). 

Salient Features:

• It predicts future demands based on historical data. 
• It takes into account factors like the store’s location, seasonality, and changes in the neighborhood or business’s competitive position to generate the demand. 
• It utilizes time series data to identify patterns and trends in product sales.

Technologies Required: 

• Python 
• Pandas
• NumPy
• Matplotlib
• Scikit-learn

Source Code

10. Human Face Generator

Objectives: The objective of this project is to create a deep-learning model that can create human faces. The program will produce original human faces that are not based on any photographs of people. 

The technology you will use in this project is called a generative adversarial network or GAN. This algorithm architecture comprises two neural networks, ‘generator’ and ‘discriminator’. These two networks compete against each other to produce the desired result. The ‘generator’ creates realistic-looking fake images, and the ‘discriminator’ differentiates between real and fake images. Together, these help in creating images identical to real-life photographs.

Salient Features:

• It creates high-quality and realistic human faces.
• It allows the customization of generated faces based on parameters, such as gender, age, and facial expressions.

Technologies Required:

• Python
• TensorFlow
• Keras
• OpenCV
• CelebA
• Compute Unified Device Architecture (CUDA)

Source Code

11. Lung Cancer Detection

Objectives: This project aims to develop a deep-learning model that can help in diagnosing lung cancer. The project’s objective is to create a program that will help analyze CT scan images and help identify the areas with cancer.

Usually, it takes about ten minutes for the doctors to inspect a CT scan and then additional time to identify if the cancer is benign or malignant. Furthermore, there is always a possibility of human error. Therefore, this project is much needed to save time and ensure error-free inspection of cancer nodules. You can use the LUNA 16 grand challenge dataset that is available online for your project. 

This lung cancer detection project can address several factors affecting the smooth detection of nodules. These are:

• Non-nodules are highlighted, and others are ignored in CT scans due to radiological heterogeneity. 
• Nodules in different shapes and sizes. 
• Challenges in the detection of small nodules.

Salient Features:

• Identifies lung cancer from CT scans and possibly classifies it according to various types and stages.
• It can be trained to analyze 3D images. 
• Performs segmentation and normalization before image processing. 

Technologies Required:

• Convolutional Neural Networks (CNNs)
• LIDC-IDRI Dataset
• Python
• DICOM Processing Libraries
• TensorFlow
• Keras
• SimpleITK or VTK

Source Code

12. Automated Attendance System

Objectives: Develop a project that automates the process of attendance marking using facial recognition. The aim is to streamline attendance management and ensure high accuracy in identifying individual faces for marking correct attendance.

Using the convolutional neural networks model, it increases the accuracy of prediction when there are a large number of students. 

Some of the benefits of this project include: 

• Significant environmental impact as institutions can save a lot of paper. 
• Efficiency in record maintenance and management. 
• Helps save time and resources for the organization.

Salient Features:

• Use face recognition to accurately mark attendance. 
• Real-time processing makes it suitable for live classes or meetings. 
• The organizations can find the defaulters with a single click.

Technologies Required:

• Convolutional Neural Networks (CNNs)
• Python
• OpenCV
• TensorFlow
• Keras

Source Code

13. Recommendation System

Objectives: Develop a recommendation system using a deep learning model that recommends content or items to users effectively. The project aims to improve user engagement through personalized recommendations. 

About 70-80% of content watched on Netflix comes from recommendations. The number is similar for other OTT and related platforms. This has increased the usage of a recommender system. Further, deep learning-based systems have fared better than traditional ones as they help analyze complex interaction patterns and precisely identify the user’s preferences. You can make different recommendation systems for specific domains like books, movies, restaurants, or other products or services. 

Salient Features:

• It tailors suggestions according to individual user preferences.
• It adapts and evolves according to the user behavior and feedback and recommends accordingly.
• It uses the MovieLens 1M dataset for training the model. 
• It utilizes a deep deterministic policy gradient (DDPG) algorithm for recommendation. 

Technologies Required:

• Python
• Pandas
• NumPy
• TensorFlow
• Deep Reinforcement Learning Algorithms

Source Code

14. Emotion Recognition

Objectives: Develop a project to recognize and classify human emotions from facial expressions. This project is aimed at industries like entertainment, mental health assessment, and marketing. 

Some of the applications of an emotion recognition system include: 

• It can help the police in deepfake detection. 
• In healthcare services, it can support autism therapy. 
• Businesses can optimize their marketing plans according to sentiment analysis done based on emotion recognition systems. 
• It can also automate clicking selfies, which can be a new feature in any of the smartphones. 
• It can identify drowsiness in the drivers and raise an alarm accordingly. 

Salient Features:

• It utilizes facial expressions as a form of input to recognize and classify emotions.
• It can distinguish between basic emotions like happiness, anger, sadness, frustration, etc.
• It is capable of recognizing emotions in real time.
• It is capable of recognizing emotions in real-time.

Technologies Required:

• Convolutional Neural Networks (CNNs)
• Python
• Keras
• OpenCV
• Dataset for Training

Review the Emotion Recognition Source Code.

Deep Learning Projects on Github

GitHub is a platform that allows developers or programmers to create, store, manage, and share their project codes. It is essentially used for version control, but also facilitates task management, bug tracking, software feature requests, and more. There are several deep learning projects on vast topics available on GitHub. Some of the popular projects you can work on include:

15. StyleGAN2: Image Generator

Objectives: In this project, you can develop a deep learning model that can generate high-quality photos of people, places, and other items. StyleGAN2 is an example of an image generator, which is a deep learning open-source project developed by NVIDIA research. It focuses on generative adversarial networks (GANs) for generating high-quality images. The model provides more control over attributes and features, such as facial expressions, age, and gender, of generated images. 

Salient Features:

• Ability to produce high-resolution and realistic images.
• Reduces the common artifacts found in the generated images.
• Produces a combination of different styles from different source images.

Technologies Required:

• Python
• TensorFlow
• NVIDIA GPU

Source Code

16. PyTorch-YOLOv3: Object Recognition Model

Objectives:

Deep neural networks like CNN are ideal choices for developing an object recognition model because they ensure faster and more accurate results for single and multiple object recognition. Additionally, for efficient results, ensure that you preprocess your dataset well. 

Some challenges you can experience for object detection include: 

• Variability in object appearance within the images. They can be in various shapes, sizes, or orientations. 
• Scale variation is another challenge. Detecting small and large objects in the same image requires your model to operate on multiple scales.
• Objects to be identified may be obstructed by other objects.
• Background clutter makes it difficult to distinguish foreground from background. 

Salient Features:

• Fast processing speed enables it to be used in real-time applications.
• It can detect objects of various sizes.
• It is useful in wide-ranging applications from self-driving vehicles to security systems.

Technologies Required:

• PyTorch
• Python
• Compute Unified Device Architecture (CUDA) 

Source Code

17. Tacotron 2: Text-to-Speech Model

Objectives: Develop a deep learning model to produce human-like speech from text. The aim is to create a model that can translate a given text into clear and natural-sounding audio output.

For this project, deep neural networks are trained on large sets of recorded speech. The training of the model will also include learning to predict audio features from the text. The functionality of this project works as follows: 

• Text-to-speech models convert input text into a visual representation of the spectrum of frequencies of sound (spectrogram). 
• The spectrogram is converted into audible sound using vocoder models or speaker encoders. 

You can further fine-tune your models on different datasets to let them adapt to different languages and voices. 

Salient Features:

• Produces highly natural speech based on the input text.
• Can be trained on different datasets to generate various voice types and accents.
• Uses advanced neural network architectures like convolutional neural networks (CNNs) and recurrent neural networks (RNNs) for speech generation.

Technologies Required:

• Python
• NVIDIA GPUs
• WaveGlow
• Compute Unified Device Architecture (CUDA)
• PyTorch

Source Code

Deep Learning Projects on Kaggle

Kaggle offers wide-ranging datasets for individuals to utilize in their projects. Some of the deep learning projects on Kaggle include: 

18. Skin Cancer Detection

Objectives: In this project, you will develop a deep learning project that can identify melanoma, a type of skin cancer, in images of skin lesions. The deep learning model should classify whether a lesion in an image is malignant or benign. 

You can utilize a dataset with skin lesion images from dermatology databases for this project. While preprocessing the data, enhance the images by isolating the lesion from the surrounding skin using the segmentation technique. 

Salient Features:

• High accuracy in differentiating between malignant melanoma and benign skin lesions.
• Takes into consideration the diverse skin types, lesion locations, and image qualities. 
• A user-friendly interface for the enhancement of the diagnosis process. 

Technologies Required:

• Python
• Deep Learning Libraries (TensorFlow & Kera)
• Convolutional Neural Networks 
• OpenCV
• ResNet
• VGG
• Inception
• SQL

Source Code

19. Plant Diseases Detection

Objectives: In this project, you will develop a deep learning model that categorizes photos of plant leaves into different disease groups. Kaggle provides a dataset with 186,000 photos of plant leaves that are labeled as rust, scab, or complicated disease categories. You have to develop a deep learning model that can detect the plant disease and categorize the images accordingly. 

This project is essential in today’s time when climate change and modern farming practices (increasing use of pesticides) are significantly affecting plant life. It is crucial to diagnose plant diseases before they lead to the extinction of vulnerable species. Therefore, this project aims to timely detect plant diseases. 

Salient Features:

• Ability to accurately recognize plant diseases from images.
• Identifies a wide range of plant diseases including their variations.
• Analyzes images in real time and provides an instant diagnosis.

Technologies Required:

• ResNet
• Inception
• VGG-16
• Python
• SQL
• OpenCV
• Scikit-learn
• Matplotlib

Source Code

20. Landmark Recognition

Objectives: Landmark recognition by computer becomes challenging due to images with different lighting conditions, varying angles, and occlusions. Therefore, in this project, you will develop a deep-learning model that can classify several images of landmarks in their corresponding categories. 

Salient Features:

• Ability to accurately distinguish between a wide range of global landmarks based on their visual features.
• Retrieval of landmark information (historical and cultural) based on image inputs.
• User-friendly interface for easy uploading and retrieval of information about the landmarks.

Technologies Required:

• Python
• TensorFlow
• AWS
• SQL
• Seaborn
• ResNet
• Inception
• VGG

Source Code

Conclusion

A great way to familiarize yourself with deep learning is by pursuing projects. You can build as many different types of deep-learning projects. Even a beginner can start with simple deep-learning projects before moving on to more complex ones. 
Have you worked on a deep-learning project before? Share your project suggestions for deep learning in the comments section below. If you are interested in pursuing a career in deep learning, here are some deep learning interview questions you can practice.

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