Artificial Intelligence and Deep Learning have witnessed exponential growth in the last few years. The demand for artificial intelligence and deep learning technologies has surged in the industry to understand the customer perspective, automate the system, and much more. If you are a Machine Learning enthusiast or a Machine Learning beginner, the best you can now is work on interesting deep learning projects.
List of Deep Learning Projects
Deep learning is a kind of way to intimate the human brain. In short, deep learning is about how computer programs can learn through observations and make decisions based on their experience, just like the human brain. The deep learning methods are helpful for natural language processing, speech recognition process, and much more. Deep learning architectures such as recurrent neural networks, deep neural networks, and deep belief neural networks are present in numerous areas. This includes speech recognition, bioinformatics, computer visions, text recognition, natural language processing, machine translation, board game programming, etc.
The end goal of deep learning is to develop computer systems that can function independently without taking any input from humans. The best you can do to understand the deeper perspective of deep learning is to try your hands-on projects. To gain experience, you have to work on numerous deep learning projects.
In the Deep Learning Projects article, we will be covering some fascinating deep learning project ideas for beginners. After completing your beginner’s level project, you should understand deep learning in-depth, and afterward, you can try complex hands-on projects.
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Benefits of Using Deep Learning
Deep learning can be used to solve some of the most critical problems in the world through technology.
- The deep learning algorithms can execute feature engineering on their own, improving efficiency and accuracy.
- The deep learning algorithms can be trained to derive insights from the different data formats.
- Deep learning can produce phenomenal results from unstructured data.
- The deep learning algorithms can detect the anomalies or inconsistencies that humans would miss.
- The deep learning algorithms give an advanced level of accuracy in medical tests, which can help diagnose medical conditions.
- The trained deep learning algorithms can efficiently perform the same repetitive and tedious task without slowing down productivity.
Framework for Deep Learning Projects
There are numerous frameworks available in the market that you can choose to work on deep learning projects. If you don’t know what a framework is, then a framework is a collection of support programs, compilers, toolsets, code libraries, and APIs to develop software and create systems.
Below are some easy-to-use frameworks while working on deep learning projects.
1. PyTorch
The PyTorch framework is best suitable for larger projects which often require customization. PyTorch uses Python as a programming language, and PyTorch is among one of the highly recommended frameworks for deep learning projects.
2. TensorFlow
This deep learning framework was developed at Google Brain. Tensorflow is an open-source end-to-end platform for machine learning, a framework that can perform regression, classification, and neural networks.
TensorFlow as a framework is available for both CPUs and GPUs. The user requires a great understanding of NumPy arrays and Python to work efficiently on this framework. This framework is a boon for beginners as well as users working on advanced deep learning projects.
3. Keras
This framework is a neural network library designed on the framework we discussed above, i.e., TensorFlow. This is designed to make machine learning modeling easier. Similar to Tensorflow, it can work on CPUs and GPUs both. This framework can be used with R, PlaidML, Theano, and CNTK ( Microsoft Cognitive Toolkit ).
All three mentioned frameworks are considered among a few of the best frameworks available to work on deep learning projects.
Gluon, Sonnet, MXNet, DL4J, ONNX, and chainer are a few other frameworks that you can use to build your deep learning projects.
Ideas For Deep Learning Projects (For Beginners )
Below are the few beginner-level deep learning project ideas you can try your hands on and then move to the advanced level projects. The beginner’s level project will start with Machine Learning in general.
1. Visual Tracking System
A visual tracking system is designed to track and locate moving objects within a given time frame with the help of a camera. This visual tracking tool has various applications on a day-to-day basis, such as security, surveillance, AR (augmented reality), traffic control, video editing, and communication.
The visual tracking system uses deep learning algorithms to study the sequential video frames, and later it tracks the movement of objects that have been targeted between the frames.
2. Image Classification with CIFAR – 10 dataset
The CIFAR – 10 dataset is a collection of 60,000 images categorized in 10 different classes. The basic idea behind this project is to establish an image classification model that will be able to detect to which class the input image belongs to. The training set contains around 50,000 images, and the test set contains 10,000 images. The training set is divided into five groups with 10,000 images arranged randomly.
This image classification plays an important role in deep learning. While completing these projects, you will come across various untouched topics related to deep learning, which will be exciting for you to learn.
3. Human Face Detection or Face Detection System
This is among one of the most common projects for beginners. The advancement of deep learning has exponential growth to the face recognition system. The face recognition system is the subset of object detection, which focuses on observing the instances of meaningful objects.
With the help of deep learning algorithms, you can build models with high accuracy in detecting the bounding boxes of the human face.
This project will help you start and understand the concept of object detection, and by the end of the project, you will be able to learn how to detect an object in an image.
4. Chatbot
The concept of the chatbot has become very common these days. Chatbot helps organisations time and improves their efficiency by talking to the customers. In short, chatbots are very intelligent and can answer human questions and requests in a record time. Chatbots can engage with humans just like other humans.
All you will need to do is model a chatbot using IBM Watson’s API in this chatbot project. All you will need to have is Python and a Bluemix account on your PC during this project.
5. Breast Cancer Classification
Cancer is one of the most dangerous diseases. The worst part of cancer is that you cannot identify it in the early stages. To cure cancer, one should be able to detect it as early as possible. Cancer cells differ from normal cells, so it is possible to detect cancer in a patient using histopathology images.
In your project, you can build an image classification model to detect whether a person has cancer or not.
6. Music Genre Classification System
Music in the present world works as a healer, and it can calm the person and help them relax in their busy schedules. This is a project idea where you can build a model that can classify the genre of music using neural networks. This is one of the most exciting project ideas that will also help you improve your deep learning knowledge.
During this project, you will be using the FMA( Free Music Archive) dataset. FMA is an open-source and easy-to-access dataset, an exciting library comprising high-quality and legal audios.
In this project, you will need to extract information from the audio samples such as MFCC, spectrograms, etc., and later build a system to classify music genres.
At the end of this project, you will be building a model that can automatically classify the music genre.
7. WaveGlow
This is an advanced-level project. WaveGlow is a kind of flow-based Generative network for speech recognition systems developed by NVIDIA, and this can generate high-quality speech from Mel – spectrograms.
WaveGlow can be implemented using a single network and trained using a single cost function. The end goal is to optimize the likelihood of the training data.
8. Generate Human Faces with DCGAN
DCGAN stands for Deep Convolutional Generative Adversarial Network. DCGAN is a powerful technique to generate images, audio, texts, and videos indistinguishable from real-world data. The basic concept behind this project is to start with random noise and apply DCGAN to generate real-like human faces that don’t even exist in the world.
9. Driver Drowsiness Detection
In this project, you will be creating a detection system to identify the signs of drowsiness, which will alert the drivers and save their and passengers’ lives from accidents. To complete this project, you will be using Python, OpenCV, and Keras.
10. Dog breed identification
In this project, you will be developing a distinguishing model to differentiate between the different breeds of the dogs from the image. In this project, you can use Kaggle’s dog breed dataset.
Also Read: Top 20 Applications of Deep Learning in 2022 Across Industries
Other Deep Learning Projects To Work On
- Detector
- Colouring old B&W photo
- Digit Recognition System
- Image Caption Generator
- OpenCog
- IBM Watson
- Kaggle Titanic Prediction
- House Price Prediction
- Predict Next Sequence
- Language Translator
Conclusion
Irrespective of your level, we hope this article has given you a brief about the deep learning projects you can choose to add to your resume after completion.
These projects carry real-world applications, and the deep learning technology is still new, so you can think of the variations you can bring in the projects which can be helpful to the world.
You can be the next innovator in the Deep Learning domain. Explore our Professional Programs in Artificial Intelligence.
0 Source: GreatLearning Blog
