Best 20 Project In Deep Learning can be used in a portfolio
Introduction:
In the ever-expanding landscape of artificial intelligence and machine learning, deep learning has emerged as a powerful paradigm with profound implications across various domains. From natural language processing to computer vision and beyond, the applications of deep learning are vast and diverse. In this article, we embark on a journey to explore a myriad of projects that serve as invaluable learning opportunities in the realm of deep learning. Whether you’re a novice seeking to delve into the world of neural networks or a seasoned practitioner looking to expand your repertoire, these projects offer a rich tapestry of challenges and insights to ignite your curiosity and fuel your innovation. Join us as we navigate through a curated selection of projects that promise to inspire, educate, and empower you on your deep learning journey.
Covid19:
1. Bing Coronavirus
○ Classify Bing Queries as either specific (e.g. about a specific location) or generic. You might have to figure out a more exact definition of specific or generic though.
Dataset: https://github.com/microsoft/BingCoronavirusQuerySet
2. Covid Clinical Data
○ Rank and sort high-risk patients using clinical data. Pick an interpretable approach if you can.
Dataset: https://covidclinicaldata.org
Text
3. Autonomous Tagging of StackOverflow Questions
○ Make a multi-label classification system that automatically assigns tags for questions posted on a forum such as StackOverflow or Quora.
Dataset Link: https://www.kaggle.com/stackoverflow/stacklite
4. Keyword/Concept identification
○ Identify keywords from millions of questions:
Dataset Link: https://www.kaggle.com/c/facebook-recruiting-iii-keyword-extraction/data
5. Topic identification
○ Multi-label classification of printed media articles to topics.
Dataset Link: https://www.kaggle.com/c/wise-2014/data
Natural Language Understanding
6. Sentence to Sentence semantic similarity
○ Can you identify question pairs that have the same intent or meaning?
Dataset Link: https://www.kaggle.com/c/quora-question-pairs/data
7. Fight online abuse
○ Can you confidently and accurately tell whether a particular comment is abusive?
○ Dataset: https://www.kaggle.com/c/jigsaw-toxic-comment-classification-challenge
8. Open Domain question answering
○ Can you build a bot that answers questions according to the student’s age or curriculum?
○ Dataset: https://ncert.nic.in/textbook.php
9. Automatic text summarization
○ Can you create a summary with the major points of the original document?
○ Abstractive (write your summary) and Extractive (select pieces of text from the original) are two popular approaches
○ Dataset: http://cs.nyu.edu/~kcho/DMQA/
10. Copy-cat Bot
○ Generate plausible new text that looks like some other text
○ Example Dataset: https://github.com/mgupta1410/pm_modi_speeches_repo
11. Sentiment Analysis
○ Do Twitter Sentiment Analysis on tweets sorted by geography and timestamp.
○ Dataset: https://inclass.kaggle.com/c/si650winter11/data
Forecasting
12. Univariate Time Series Forecasting
○ How much will it rain this year?
Dataset Link: http://research.jisao.washington.edu/data_sets/widmann
Multi-variate Time Series Forecasting
○ How polluted will your town’s air be? Pollution Level Forecasting ○ Dataset: https://archive.ics.uci.edu/ml/datasets/Beijing+PM2.5+Data
13. Demand/load forecasting
○ Find a short-term forecast on electricity consumption of a single home ○ Dataset: https://archive.ics.uci.edu/ml/datasets/individual+household+electric+power+consumption
14. Predict Blood Donation
○ We’re interested in predicting if a blood donor will donate within a given time window.
○ Dataset: https://archive.ics.uci.edu/ml/datasets/Blood+Transfusion+Service+Center
Recommendation systems
15. Movie Recommender
○ Can you predict the rating a user will give on a movie?
○ Do this using the movies that users have rated in the past, as well as the ratings similar users have given similar movies.
○ Dataset: http://www.netflixprize.com/
and can use this data also: https://grouplens.org/datasets/movielens/
16. Search + Recommendation System
○ Predict which Xbox game a visitor will be most interested in based on their search query
○ Dataset: https://www.kaggle.com/c/acm-sf-chapter-hackathon-small/data
17. Can you predict Influencers in the Social Network? ○ How can you predict social influencers?
○ Dataset: https://www.kaggle.com/c/predict-who-is-more-influential-in-a-social-network/data
vision
18. Image classification
○ Object recognition or image classification task is how Deep Learning shot up to its present-day resurgence
○ Datasets:
1. https://www.cs.toronto.edu/~kriz/cifar.html
19. Diagnosing and Segmenting Brain Tumors and Phenotypes using MRI Scans
20. Identify endangered right whales in aerial photographs
Dataset: https://www.kaggle.com/c/noaa-right-whale-recognition
Conclusion:
As we reach the culmination of our exploration into deep learning projects, it’s evident that the realm of artificial intelligence is brimming with opportunities for learning and innovation. Throughout this journey, we’ve encountered a diverse array of projects spanning natural language understanding, image processing, forecasting, recommendation systems, and more. Each project offers a unique lens through which to understand and apply deep learning techniques, whether through analyzing vast datasets, tackling real-world challenges, or pushing the boundaries of what’s possible with modern technology.
Yet, beyond the technical intricacies and algorithmic complexities lies a deeper narrative — one of creativity, collaboration, and human ingenuity. Deep learning projects are not just about building models and optimizing performance metrics; they’re about solving problems, addressing societal needs, and making a meaningful impact on the world around us.
As we reflect on the projects showcased in this article, let us not only celebrate the achievements of the past but also look forward to the possibilities of the future. Whether you’re a student embarking on your first deep learning project or a seasoned researcher pushing the boundaries of AI, remember that the journey is as important as the destination. Embrace curiosity, foster collaboration, and never cease to explore the vast frontier of deep learning.
In the end, it’s not just about the lines of code or the accuracy scores — it’s about the stories we tell, the lives we touch, and the transformative potential of technology to shape a better tomorrow. So, let us continue to harness the power of deep learning to unlock new insights, drive innovation, and create a brighter, more equitable future for all.
