What if machines could learn the same way as humans? By examples, pattern recognition, and analysis and decision-making in real-time.
Deep learning makes all this possible and more.
What is deep learning?
A part of the artificial intelligence club, deep learning is a machine learning technique whose biggest strength is accuracy. As a result, it is achieving results and making possible what was impossible before. Deep learning is what powers voice control in tablets, TVs, phones, and hands-free speakers. It is what drives the driverless cars and helps them to distinguish between people and objects on the road.
The ‘deep’ in deep learning refers to the many layers through which data is processed and transformed. The learning part of deep learning can be either supervised, unsupervised, or semi-supervised. The architectures used for deep learning include recurrent neural networks, deep belief networks, and deep neural networks.
As of now, deep learning has been applied in various fields where results have surpassed those produced by humans. These fields include: social network filtering, computer vision, natural language process, machine translation, bioinformatics, speech recognition, and audio recognition among others.
Why deep learning projects
If you are aiming for a career in deep learning, then deep learning projects are the best way to ensure your entry. This is a highly practical and technical field. So, the best way to demonstrate passion and skills is to have worked on different projects (and have a proof for all of them). Theoretical knowledge is only 20-30% in this field; the rest is gained through experience.
Deep learning project ideas
All in all, deep learning is the technology of the future. This is why hordes of students and professionals are trying their hand to become skilled at it. If you are one of them, then the following deep learning ideas will help you gain some much-needed practical experience while also sprucing up your resume.
This project is called the MNIST Handwritten Digit Classification. It will familiarize you with image recognition, image classification, deep learning, and logical regression. You will learn how to convert pixel data into an image using an MNIST data set that is simple and easily accessible. The dataset consists of 60,000 pre-processed and formatted images of 28×28 pixelated handwritten digits.
Text-based projects can cover a lot of ground from text generation to classification.
One of the most popular ones is for Sentiment Analysis wherein, by analyzing the language, the algorithm is able to tell whether the writer was feeling positive, negative, or neutral. This kind of analysis has been used to detect early depression and anxiety on social media by analyzing people’s tweets. You can access the TensorFlow and Keras code for a similar project on GitHub. There’s also a specialized project for a sarcasm detector.
Another kind of text-based project includes text generation projects. Using RNN (recurrent neural networks) the same can be done.
There are equal parts fun and challenging to make. Audio signals can be electronically represented as both in both the digital and analog formats. Thus, signal processing can occur in any domain. The digital processors mathematically operate on the digital representation of the signal while analog processors operate directly on the electrical signal.
After processing, the classification process is carried out. For this, you can make a project like the Urban Sound Classification project where you classify the types of sound.
An alternative to processing and classification is audio generation. This also comes under the domain of computational creativity since a creative piece is being created using computational methods.
Image-based projects relate to object recognition, image restoration, event detection, video tracking, motion estimation, scene reconstruction, and 3D pose estimation. As you can see, a lot of ground is covered.
A common and education project to begin with, in this category, relates to real-time face detection and consequent emotion classification.
For image generation, there are a variety of projects to pick up. You can complete images by using deep learning. If you are not a fan of monochrome, then you can restore color to black-and-white images
Another project having far-reaching consequences for security is the real-time analysis of behavior in crowded areas.
The above projects cover the entire spectrum of possibilities that deep learning opens up. By implementing them, you’ll gain a greater understanding of deep learning and will also be able to find your niche.
So, what are you waiting for? It’s time for some practical deep learning. If you are curious about learning how to recognise gestures, analyze X-ray images, predict customer churn across telecom providers, and lots more, check out upGrad & IIIT-B’s PG Program in MachineLearning and Deep Learning course.
Latest posts by upGrad (see all)
- Blockchain Developer Resume: Complete Guide & Samples  - January 7, 2020
- Python Interview Questions & Answers You Must Know – Frequently Asked in 2020 - January 7, 2020
- How to Become a Hadoop Administrator in 2020: Everything You Need to Know - January 7, 2020