Deep Learning Project Ideas
Although a new technological advancement, the scope of Deep Learning is expanding exponentially. This technology aims to imitate the biological neural network, that is, of the human brain. While the origins of Deep Learning dates back to the 1950s, it is only with the advancement and adoption of Artificial Intelligence and Machine Learning that it came to the limelight. So, if you are an ML beginner, the best thing you can do is work on some Deep learning project ideas.
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We, here at upGrad, believe in a practical approach as theoretical knowledge alone won’t be of help in a real-time work environment. In this article, we will be exploring some interesting deep learning project ideas which beginners can work on to put their knowledge to test. In this article, you will find top deep learning project ideas for beginners to get hands-on experience on deep learning.
A subset of Machine Learning, Deep Learning leverages artificial neural networks arranged hierarchically to perform specific ML tasks. Deep Learning networks use the unsupervised learning approach – they learn from unstructured or unlabeled data. Artificial neural networks are just like the human brain, with neuron nodes interconnected to form a web-like structure.
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While traditional learning models analyze data using a linear approach, the hierarchical function of Deep Learning systems is designed to process and analyze data in a nonlinear approach.
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Deep Learning architectures like deep neural networks, recurrent neural networks, and deep belief networks have found applications in various fields including natural language processing, computer vision, bioinformatics, speech recognition, audio recognition, machine translation, social network filtering, drug design, and even board game programs. As new advances are being made in this domain, it is helping ML and Deep Learning experts to design innovative and functional Deep Learning projects. The more deep learning project ideas you try, the more experience you gain.
Today, we’ll discuss the top seven amazing Deep Learning projects that are helping us reach new heights of achievement.
In this article, we have covered top deep learning project ideas. We started with some beginner projects which you can solve with ease. Once you finish with these simple projects, I suggest you go back, learn a few more concepts and then try the intermediate projects. When you feel confident, you can then tackle the advanced projects. If you wish to improve your skills on the same, you need to get your hands on these machine learning courses.
So, here are a few Deep Learning Project ideas which beginners can work on:
Deep Learning Project Ideas: Beginners Level
This list of deep learning project ideas for students is suited for beginners, and those just starting out with ML in general. These deep learning project ideas will get you going with all the practicalities you need to succeed in your career.
Further, if you’re looking for deep learning project ideas for final year, this list should get you going. So, without further ado, let’s jump straight into some deep learning project ideas that will strengthen your base and allow you to climb up the ladder.
1. Image Classification with CIFAR-10 dataset
One of the best ideas to start experimenting you hands-on deep learning projects for students is working on Image classification. CIFAR-10 is a large dataset containing over 60,000 (32×32 size) colour images categorized into ten classes, wherein each class has 6,000 images. The training set contains 50,000 images, whereas the test set contains 10,000 images. The training set will be divided into five separate sections, each having 10,000 images arranged randomly. As for the test set, it will include 1000 images that are randomly chosen from each of the ten classes.
In this project, you’ll develop an image classification system that can identify the class of an input image. Image classification is a pivotal application in the field of deep learning, and hence, you will gain knowledge on various deep learning concepts while working on this project.
2. Visual tracking system
A visual tracking system is designed to track and locate moving object(s) in a given time frame via a camera. It is a handy tool that has numerous applications such as security and surveillance, medical imaging, augmented reality, traffic control, video editing and communication, and human-computer interaction.
This system uses a deep learning algorithm to analyze sequential video frames, after which it tracks the movement of target objects between the frames. The two core components of this visual tracking system are:
- Target representation and localization
- Filtering and data association
3. Face detection system
This is one of the excellent deep learning project ideas for beginners. With the advance of deep learning, facial recognition technology has also advanced tremendously. Face recognition technology is a subset of Object Detection that focuses on observing the instance of semantic objects. It is designed to track and visualize human faces within digital images.
In this deep learning project, you will learn how to perform human face recognition in real-time. You have to develop the model in Python and OpenCV.
Deep Learning Project Ideas: Intermediate Level
4. Digit Recognition System
As the name suggests, this project involves developing a digit recognition system that can classify digits based on the set tenets. Here, you’ll be using the MNIST dataset containing images (28 X 28 size).
This project aims to create a recognition system that can classify digits ranging from 0 to 9 using a combination of shallow network and deep neural network and by implementing logistic regression. Softmax Regression or Multinomial Logistic Regression is the ideal choice for this project. Since this technique is a generalization of logistic regression, it is apt for multi-class classification, assuming that all the classes are mutually exclusive).
In this project, you will model a chatbot using IBM Watson’s API. Watson is the prime example of what AI can help us accomplish. The idea behind this project is to harness Watson’s deep learning abilities to create a chatbot that can engage with humans just like another human being. Chatbots are supremely intelligent and can answer to human question or requests in real-time. This is the reason why an increasing number of companies across all domains are adopting chatbots in their customer support infrastructure.
This project isn’t a very challenging one. All you need is to have Python 2/3 in your machine, a Bluemix account, and of course, an active Internet connection! If you wish to scale it up a notch, you can visit Github repository and improve your chatbot’s features by including an animated car dashboard.
6. Music genre classification system
This is one of the interesting deep learning project ideas. This is an excellent project to nurture and improve your deep learning skills. You will create a deep learning model that uses neural networks to classify the genre of music automatically. For this project, you will use an FMA (Free Music Archive) dataset. FMA is an interactive library comprising high-quality and legal audio downloads. It is an open-source and easily accessible dataset that is great for a host of MIR tasks, including browsing and organizing vast music collections.
However, keep in mind that before you can use the model to classify audio files by genre, you will have to extract the relevant information from the audio samples (like spectrograms, MFCC, etc.).
7. Drowsiness detection system
The drowsiness of drivers is one of the main reasons behind road accidents. It is natural for drivers who frequent long routes to doze off when behind the steering wheel. Even stress and lack of sleep can cause drivers to feel drowsy while driving. This project aims to prevent and reduce such accidents by creating a drowsiness detection agent.
Here, you will use Python, OpenCV, and Keras to build a system that can detect the closed eyes of drivers and alert them if ever they fall asleep while driving. Even if the driver’s eyes are closed for a few seconds, this system will immediately inform the driver, thereby preventing terrible road accidents. OpenCV will monitor and collect the driver’s images via a webcam and feed them into the deep learning model that will classify the driver’s eyes as ‘open’ or ‘closed.’
8. Image caption generator
This is one of the trending deep learning project ideas. This is a Python-based deep learning project that leverages Convolutional Neural Networks and LTSM (a type of Recurrent Neural Network) to build a deep learning model that can generate captions for an image.
An Image caption generator combines both computer vision and natural language processing techniques to analyze and identify the context of an image and describe them accordingly in natural human languages (for example, English, Spanish, Danish, etc.). This project will strengthen your knowledge of CNN and LSTM, and you will learn how to implement them in real-world applications as this.
9. Colouring old B&W photos
For long, automated image colourization of B&W images has been a hot topic of exploration in the field of computer vision and deep learning. A recent study stated that if we train a neural network using a voluminous and rich dataset, we could create a deep learning model that can hallucinate colours within a black and white photograph.
In this image colourization project, you will be using Python and OpenCV DNN architecture (it is trained on ImageNet dataset). The aim is to create a coloured reproduction of grayscale images. For this purpose, you will use a pre-trained Caffe model, a prototxt file, and a NumPy file.
Deep Learning Project Ideas – Advanced Level
Detectron is a Facebook AI Research’s (FAIR) software system designed to execute and run state-of-the-art Object Detection algorithms. Written in Python, this Deep Learning project is based on the Caffe2 deep learning framework.
Detectron has been the foundation for many wonderful research projects including Feature Pyramid Networks for Object Detection; Mask R-CNN; Detecting and Recognizing Human-Object Interactions; Focal Loss for Dense Object Detection; Non-local Neural Networks, and Learning to Segment Every Thing, to name a few.
Detectron offers a high-quality and high-performance codebase for object detection research. It includes over 50 pre-trained models and is extremely flexible – it supports rapid implementation and evaluation of novel research.
This is one of the interesting deep learning project ideas. WaveGlow is a flow-based Generative Network for Speech Synthesis developed and offered by NVIDIA. It can generate high-quality speech from mel-spectograms. It blends the insights obtained from WaveNet and Glow to facilitate fast, efficient, and high-quality audio synthesis, without requiring auto-regression.
WaveGlow can be implemented via a single network and also trained using a single cost function. The aim is to optimize the likelihood of the training data, thereby makes the training procedure manageable and stable.
OpenCog project includes the core components and a platform to facilitate AI R&D. It aims to design an open-source Artificial General Intelligence (AGI) framework that can accurately capture the spirit of the human brain’s architecture and dynamics. The AI bot, Sophia is one of the finest examples of AGI.
OpenCog also encompasses OpenCog Prime – an advanced architecture for robot and virtual embodied cognition that includes an assortment of interacting components to give birth to human-equivalent artificial general intelligence (AGI) as an emergent phenomenon of the system as a whole.
DeepMimic is an “example-guided Deep Reinforcement Learning of Physics-based character skills.” In other words, it is a neural network trained by leveraging reinforcement learning to reproduce motion-captured movements via a simulated humanoid, or any other physical agent.
The functioning of DeepMimic is pretty simple. First, you need to set up a simulation of the thing you wish to animate (you can capture someone making specific movements and try to imitate that). Now, you use the motion capture data to train a neural network through reinforcement learning. The input here is the configuration of the arms and legs at different time points while the reward is the difference between the real thing and the simulation at specific time points.
14. IBM Watson
One of the most excellent examples of Machine Learning and Deep Learning is IBM Watson. The greatest aspect of IBM Watson is that it allows Data Scientists and ML Engineers/Developers to collaborate on an integrated platform to enhance and automate the AI life cycle. Watson can simplify, accelerate, and manage AI deployments, thereby enabling companies to harness the potential of both ML and Deep Learning to boost business value.
IBM Watson is Integrated with the Watson Studio to empower cross-functional teams to deploy, monitor, and optimize ML/Deep Learning models quickly and efficiently. It can automatically generate APIs to help your developers incorporate AI into their applications readily. On top of that, it comes with intuitive dashboards that make it convenient for the teams to manage models in production seamlessly.
15. Google Brain
This is one of the excellent deep learning project ideas. The Google Brain project is Deep Learning AI research that began in 2011 at Google. The Google Brain team led by Google Fellow Jeff Dean, Google Researcher Greg Corrado, and Stanford University Professor Andrew Ng aimed to bring Deep Learning and Machine Learning out from the confines of the lab into the real world. They designed one of the largest neural networks for ML – it comprised of 16,000 computer processors connected together.
To test the capabilities of a neural network of this massive size, the Google Brain team fed the network with random thumbnails of cat images sourced from 10 million YouTube videos. However, the catch is that they didn’t train the system to recognize what a cat looks like. But the intelligent system left everyone astonished – it taught itself how to identify cats and further went on to assemble the features of a cat to complete the image of a cat!
16. 12 Sigma’s Lung Cancer detection algorithm
12 Sigma has developed an AI algorithm that can reduce diagnostic errors associated with lung cancer in its early stages and detect signs of lung cancer much faster than traditional approaches.
According to Xin Zhong, the Co-founder and CEO of Sigma Technologies, usually conventional cancer detection practices take time to detect lung cancer. However, 12 Sigma’s AI algorithm system can reduce the diagnosis time, leading to a better rate of survival for lung cancer patients.
Generally, doctors diagnose lung cancer by carefully examining CT scan images to check for small nodules and classify them as benign or malignant. It can take over ten minutes for doctors to visually inspect the patient’s CT images for nodules, plus additional time for classifying the nodules as benign or malignant.
Needless to say, there always remains a high possibility of human errors. 12 Sigma maintains that its AI algorithm can inspect the CT images and classify nodules within two minutes.
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These are only a handful of the real-world applications of Deep Learning made so far. The technology is still very young – it is developing as we speak. Deep Learning holds immense possibilities to give birth to pioneering innovations that can help humankind to address some of the fundamental challenges of the real world.
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Is Deep Learning just a hype or does it have real-life applications?
Deep Learning has recently found a number of useful applications. Deep learning is already changing a number of organizations and is projected to bring about a revolution in practically all industries, from Netflix's well-known movie recommendation system to Google's self-driving automobiles. Deep learning models are utilized in everything from cancer diagnosis to presidential election victory, from creating art and literature to making actual money. As a result, it would be incorrect to dismiss it as a fad. At any given time, Google and Facebook are translating content into hundreds of languages. This is accomplished by the application of deep learning models to NLP tasks, and it is a big success story.
What is the difference between Deep Learning and Machine Learning?
The most significant distinction between deep learning and regular machine learning is how well it performs when data scales up. Deep learning techniques do not perform well when the data is small. This is due to the fact that deep learning algorithms require a vast amount of data to fully comprehend it. Traditional machine learning algorithms, on the other hand, with their handmade rules, win in this circumstance. Most used features in machine learning must be chosen by an experienced and then hand-coded according to the domain and data type.
What are the prerequisites for starting out in Deep Learning?
Starting out with deep learning isn't nearly as difficult as some people make it out to be. Before getting into deep learning, you should brush up on a few fundamentals. Probability, derivatives, linear algebra, and a few other fundamental concepts should be familiar to you. Any machine learning task necessitates a fundamental understanding of statistics. Deep learning in real-world issues necessitates a reasonable level of coding ability. Deep learning is built on the foundation of machine learning. Without first grasping the basics of machine learning, it is impossible to begin mastering deep learning.