The best way to show the skillset you’ve is by showcasing the Deep Learning projects that you’ve worked on. With this list of best Deep Learning projects for students, you can gain hands-on working on projects.
Suppose you’re looking for some ideas for deep learning projects or wondering where to find one. Then this is where you stop, and we got your covered with several deep learning sample projects that you can take.
In this article, we’ve curated some of the best beginner Deep Learning projects for students who may have just finished the course on Deep Learning. But this doesn’t mean its not for experienced Deep Learning practitioners; we’ve also listed some advanced projects.
Why is it Important?
By taking these Deep Learning Projects for beginners, you will develop advanced techniques. The more projects you work on, the more experienced you become. These are things that recruiters look for, an outstanding individual.
By displaying your Deep Learning projects on your resume, you imply you’ve got the necessary skills that recruiters are looking for. We recommend that you take multiple projects one after the other or on the same day.
We recommend that you bookmark this page so that you can always refer to what you need.
Beginner Deep Learning Projects for Students
1. Deep Learning NLP: Training GPT-2 from Scratch
This project is perfect for beginners who are looking to explore Transformer-based NLP. Here you will begin by understanding the history of GGPT-2 and Transformer Architecture Basics. Then you learn how to collect data that is need for training and proceed to the fine-tuning process.
To be precise, it trains you on the fine-tuning GPT-2, which is an NLP machine learning model based on Transformer architecture. The project also covers the future of Transformer-based NLP. To get the most out of this project, students are advised to perform further research and experiment.
2. Mining Quality Prediction Using Machine & Deep Learning
Here you will use the ML and Deep Learning techniques to predict mining quality. It teaches you the core concepts of understanding the theory behind Simple and Multiple Linear Regression. You will train and evaluate several regression models using the Python Sci-kit library.
You will get hands-on practice to train Artificial Neural Network models to perform regression tasks. If you need more training on regression models, check out our list of projects on Linear Regression.
3. Predicting House Prices with Regression using TensorFlow
This project is designed to teach you to create, train, and evaluate a neural network in TensorFlow. You can sign up now and complete the project in less than 2 hours. The more sample Deep Learning projects you do, the better your portfolio looks.
The tutorial will teach you the foundations of how to use Keras with TensorFlow and solve regression problems. By the end of the project, you will predict the prices of the house with the highest accuracy. More than 7k students have taken this project to practice Deep Learning. Also, practice TensorFlow by enrolling in TensorFlow projects.
4. Deep Learning Inference with Azure ML Studio
This Deep Learning project-based course will teach you to train a neural network that will recognize handwritten digits. It is achieved by using the Multi-class Neural Network module in Azure Machine Learning Studio.
The programming language that you use in this project is Python; hence Python skill is a prerequisite. This is a project that will leverage the Azure cloud platform to work on models. You will use the data from MNIST, which contains over 70,000 grayscale images of handwritten digits.
5. Sentiment Analysis with Deep Learning using BERT
This project will train how you can perform sentiment analysis with Deep Learning. By merely reading the PyTorch BERT model, you will adjust architecture for multi-class classification. This is an intermediate Deep Learning projects with PyTorch suitable for beginners and freshers who recently finished the course.
By fine-tuning the model, you learn to design and evaluate a loop to monitor model performance. For BERT Classification, you begin by preprocessing and clean data. In the end, you will have finally built a Sentiment Analysis model with Deep Learning.
6. Build a Deep Learning Based Image Classifier with R
Here you will be training the Deep Learning based image classification model with R language. To get started, students should know R programming and the basics of neural networks. By taking on this project, you will solve the image classification problem. It is one of the best Deep Learning image classification projects that you can take.
The step-by-step instructions are outlined to provide you what you will be doing in this project. Every Deep Learning project has a specific outcome, that is the reason we recommend that you more than a few projects. Bookmark this page to refer anytime you want.
7. Anomaly Detection in Time Series Data with Keras
Anomaly Detection in Time Series Data with Keras is an important project to develop essential skills. The anomalies are detected using Autoencoders in the time series data. You will also learn to create interactive plots and charts using Plotly and Seaborn.
This is a 90-minute long project to design and train LSTM autoencoder. This is attained by using Keras API with TensorFlow. The charts that you create will help in developing data visualization skills. Deep Learning projects for the final year will come in handy to get more hands-on practice labs.
8. Traffic Sign Classification Using Deep Learning in Python/Keras
More than 5k students took this guided project on Deep Learning in Coursera. You begin by learning and understanding the CNNs. CNN is the most preferred solution to solve any image data challenge. Any data with spatial relationships is good to apply CNN. Hence this skillset will come in handy in many scenarios.
The goal of the project is to teach you how to build and train the Convolutional Neural Network. CNN is built by using Keras with Tensorflow 2 as a backend. You then proceed to compile and fit the Deep Learning model to training data. Then gauge the performance of your trained CNN. Also, check some of the best Machine Learning projects for beginners.
9. Facial Expression Recognition with Keras
Facial recognition is an awesome deep learning project that everyone should take. Witnessing the creativeness of this technique will drive anyone with crazy ideas. In this project, you will use Keras to develop a facial expression recognition model.
By using OpenCV, automatic detection of faces in the images can be pulled off. But in this project, you will categorize each face based on the facial expression into seven categories. You will then deploy the trained model into a web interface using Flask. Over 13k students have studied this to develop Facial recognition skillset. Computer vision is an exciting skill that goes along with Deep Learning.
10. Understanding Deepfakes with Keras
In this Deep Learning Deepfake project, you will know what it really is by doing a practical project. You will learn the new term DCGAN or Deep Convolutional Generative Adversarial Network. Train DCGAN to create realistic looking synthesized images.
The prerequisite to take this project is that you have the practical knowledge of Neural Networks, Convolutional Neural Network, and Gradient Descent. Python programming is also needed. Deepfakes are nothing but a synthesized image created with real-world, observed data.
11. Using TensorFlow with Amazon Sagemaker
This project tutorial will teach how to train and deploy image classifiers in the AWS cloud. You will create and train the model using the TensorFlow framework in Amazon Sagemaker. Sagemaker have many ML algorithms that are ready to use. But you can still use it for custom training scripts.
You will create your custom script for AWS Sagemaker and train a TensorFlow model. Then finally deploy the trained model to the cloud using Sagemaker. The goal is to teach you to deploy models in Amazon Sagemaker.
12. Explainable AI: Scene Classification and GradCam Visualization
This project is one of the best advanced Deep Learning projects that you can take. You will build a Deep Learning model to predict the scenery in images. Also, it uses the Grad-Cam technique and provides an explanation of how AI models think.
Using Keras with TensorFlow 2.0, you will build a model based on Residual blocks and CNN. This project can be used to detect scenery using the satellite images. You will learn many things and maybe inspired to work on future technologies.
13. Generate Synthetic Images with DCGANs in Keras
In this Deep Learning project in Python, you will design and train DCGAN’s using Keras API. The model that you build here will use the neural network. This neural network is used to learn a transformation from the simple distribution—perfect project to learn about GAN.
This project is created to teach you to build and train and Deep Convolutional GAN with Keras. This, in turn, helps to generate images of fashionable clothes. At the beginning of the project, you will be taught how GAN works and how to implement it. Take the projects on Deep Learning using Python to advance your skillset development.
14. Emotion AI: Facial Key-points Detection
Face detection is an impressive technology we achieved using Artificial Intelligence. With this project, you will practically understand the theory on Deep Neural Networks, and Residual Neural Networks, and CNN.
By taking this, you develop the skills of computer vision, Python, AI, ML, and Deep Learning. The tutorial by the instructor is obvious to understand to learn how to recognize facial points. The project is 2 hours long.
15. Object Detection with Amazon Sagemaker
Students will build, train, and deploy an object detection model using Amazon Sagemaker. To take part in this project, you need an AWS account, and if you don’t have one, you can create it in a few minutes. Students should know Python in order to take advantage of this project.
You will use the SSD Object Detection algorithm to create, train, and deploy a model. By using the IIIT-Oxford Pets Data set, the model will localize the faces of cats and dogs. Every step is explained thoroughly to get started with AWS Sagemaker.
16. Image Classification with CNNs using Keras
In this project, you will learn how to create a Convolutional Neural Network with a TensorFlow as a backend. All the image classification that you encounter in the future can be solved using CNN. If you get any questions regarding the project, you can post in discussion forums, and the instructor will get back.
You will polish Deep Learning skills with this beginner Deep Learning project for students. Using a data set that is available globally, the main skills you learn here is CNN. Both CNN and Keras are essential skills to have.
17. Convolutions for Text Classification with Keras
To get the most out of this project, you should have Python’s skills, fundamental theory on Deep Learning, and experience using TensorFlow or Keras. This project uses the data set from the Toxic Comment Classification Challenge on Kaggle. It is a unique Deep Learning project on Kaggle.
By the end of this project, students will be able to apply word embeddings for text classification. And also, you can perform Binary Text Classification using Deep Learning with Keras. Deep Learning Projects for students can be completed in an hour.
Summary: Is the Deep Learning Sample Projects Worth it?
Deep Learning is used in various fields such as natural language processing, speech recognition, bioinformatics, audio recognition, social network filtering, and more. By advancing the Deep Learning skills, you also advance your career in Software Development.
These beginner Deep Learning projects for students will help you kick start your learning path. You can also find some open source projects on Deep Learning for practice on Google. You may also be interested in Data Science projects for beginners.
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