10 Deep Learning Project Ideas For Beginners

Introduction

 

Have you ever trained a child? Or taught them how to speak or write or to play? When they don’t obey your instructions, what do you do? You use your ways, like scolding them or staring at them continuously, don’t you?

 

But what if you have to train a computer? You use your ways there too. But here, those ways are a bit different. Here you teach a computer to learn on it’s own, gain the ability to think and learn from what it has been taught till now. This way of teaching a computer to learn comes from a type of Machine learning called “Deep Learning”.

 

Deep Learning when entered the mainstream technological market, it started gaining more and more popularity. Many students started to see it as a perfect career opportunity. This is one of the main reasons why students and even working professionals wanting to switch careers are in a hurry to create more and more projects. 

 

For people already acquainted with the field, it becomes easy to create projects and execute them. But for the beginners, trying to get into the fields, these projects matter a lot.

 

This blog is for those beginners who are looking for deep learning project ideas to add to projects and add to their CV. Let us dive in and look at the best deep learning project ideas for beginners

 

(Related reading: Machine Learning project ideas)

 

 

Deep Learning Project Ideas/Topics 

 

1. Classification of images using the CIFAR-10 dataset

 

Working on image categorization is one of the finest ways to get started with hands-on deep learning projects for students. CIFAR-10 is a big dataset including approximately 60,000 colour images (3232 size) divided into ten classes, each with 6,000 images. 

 

There are 50,000 photos in the training set and 10,000 images in the test set. The training set will be divided into five portions, each containing 10,000 photos that will be organised in a random order. The test set will consist of 1000 photos selected at random from each of the ten classes.

 

In this project, you'll create an image classification system that can determine the image's class. Because image classification is such an important application in the field of deep learning, working on this project will allow you to learn about a variety of deep learning topics.

 

 

2. Sentiment Analysis

 

Experimenting with sentiment analysis is a great idea, especially if you're good with language.

 

If you're not sure what sentiment analysis is, it includes a machine classifying or clustering text into positive and negative perceptions.

 

Feature selection, as with many natural language projects, may be a challenge here as well. However, studying the patterns of the texts in question is frequently the first step in understanding sentiments in text. This allows you to identify the key features in your dataset that may be used as training criteria.

 

After that, you may train your model using appropriate classification techniques like Naive Bayes or decision trees. Finally, this project introduces you to the fundamentals of text manipulation and spam detection. Python has a wide range of techniques and reasoning for sentiment analysis.

 

 

3. Visual tracking system

 

A visual tracking system uses a camera to monitor and locate moving objects in a specific time period. It's a useful tool that may be used for security and surveillance, medical imaging, augmented reality, traffic control, video editing and communication, and human-computer interaction, among other things.

 

This system analyses sequential video frames with a deep learning technique before tracking the movement of target objects between frames. The following are the two main components of this visual tracking system:

 

• Localization and depiction of the target
• Data association and filtering

 

 

4. Digit Recognition System

 

As the name implies, the goal of this project is to create a digit recognition system that can classify digits according to a set of rules. You'll be working with the MNIST dataset, which contains images (28 X 28 size).

 

Using a combination of shallow and deep neural networks, as well as logistic regression, this research attempts to construct a recognition system that can categorise digits ranging from 0 to 9. 

 

For this job, Softmax Regression or Multinomial Logistic Regression is the best option. This technique is suitable for multi-class classification since it is a generalisation of logistic regression (provided that all classes are mutually exclusive).

 

 

5. Recommendation System Using Python

 

While perusing the internet or utilising apps like YouTube and Netflix, you've probably come across a recommendation algorithm. Most internet advertising systems use it to filter the adverts you view, and it can sometimes feel as if the internet knows what you're thinking.

 

A recommender may learn about your content interests based on what you often search on the internet. It then utilises this information to suggest relevant articles that it thinks you might be interested in.

 

It's possible that yours isn't as complicated. However, to get started, you can construct something really simple. A product recommendation, for example, is a great place to start.

 

To create a product recommender, for example, you'll need to collect information on products as well as people's opinions on them. Of course, the number of favourable and negative reviews, the product niche, the number of purchases, and other factors may be considered.

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6. Face Detection System

 

For novices, this is one of the best deep learning project ideas. Facial recognition technology has evolved dramatically as a result of deep learning advancements. 

 

Face recognition is a subset of Object Detection that is concerned with observing the instance of semantic objects. It's made to track and visualise people's faces in digital photos.

 

You will learn how to conduct real-time human face recognition in this deep learning project. The model must be created using Python and OpenCV. (Source)

 

 

7. Loan Prediction

 

Loan eligibility models are now included in many lending and banking apps. If you're interested in adding machine learning techniques to your existing fintech skills, this is a great place to start.

 

However, you're unlikely to scale this up for app integration. However, you'll discover how most enterprise apps determine whether or not someone is eligible for a loan.

 

To begin, you'll need a dataset with some financial data. You'll next train your model to understand certain patterns and anticipate loan eligibility when it receives new data, based on the earnings and spending trends in this data.

 

 

8. System for Classifying Music Genres

 

This is an intriguing deep learning project concept. This is a fantastic assignment for honing and improving your deep learning abilities. You'll build a deep learning model that employs neural networks to automatically classify music genres. 

 

You will use an FMA (Free Music Archive) dataset for this project. FMA is an online library that offers high-quality, legal audio downloads. It's a free, open-source dataset that may be used for a variety of MIR applications, including exploring and organising large music libraries.

 

However, keep in mind that you'll need to extract essential information from the audio samples before you can use the model to classify audio files by genre (like spectrograms, MFCC, etc.).

 

 

9. Detection of Face Masks

 

Many governments around the world made it essential to wear face masks when venturing out during the Covid-19 outbreak. Many people, however, prefer to flout the rule and go out in public without wearing face masks. This difficulty can be solved with the use of an automatic face mask detecting system. 

 

In Python, libraries such as OpenCV and Keras can be used to create this project. On the Internet, you can access several open datasets containing labelled photos for this purpose. 

 

You may improve this project by creating a model that recognises the presence of face masks on real-time webcams. There are numerous online learning sites that can show you how to achieve this. (from)

 

 

10. Producing Text

 

Text generation has long been one of deep learning's most fascinating and challenging applications. Because it requires a grasp of context, sequential data, such as text, is sometimes difficult to generate. 

 

Even if a model creates terms that are frequently connected with one another based on previous datasets, one incorrect word can make the entire sentence unintelligible. This is a fun project to work on and will look great on your portfolio. 

 

Even if your text generator produces pure gibberish, reading an AI-generated story will be exciting for potential recruiters.

 

(Must read: Machine Learning vs Deep Learning)

 

Summarizing this, I would like you to know that a deep learning project on the resume is worth the same as a degree. More the projects, more the chances of you being the perfect candidate for pursuing a deep learning career.