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Top 10 Deep Learning Projects for beginners in 2021

  • Jul 01, 2021
  • 7 Minutes Read
Top 10 Deep Learning Projects for beginners in 2021


With the increasing application of artificial intelligence and machine learning in today’s era. From recommendation systems to image processing, there are many applications of deep learning. A more practical approach is as important as theoretical knowledge to work in a real-time work environment. In this blog, we are going to discuss some exciting and fresh deep learning project ideas that can be used by beginners to test their knowledge and build more practical approaches.

What is deep learning? 

Deep learning is an artificial intelligence (AI) function that simulates the functioning of the human brain in processing data and creating patterns to be used in the higher cognitive processes. Deep learning may be a subset of machine learning in AI that has networks capable of learning unsupervised from data that's unstructured or unlabeled. And we conclude that this is called deep neural learning or deep neural network.

Deep learning has evolved hand-in-hand with the digital era, which has caused an explosion of information altogether forms and from every region of the planet. Moreover, if you need 1:1 sessions from deep learning tutors then at favtutor we have expert tutors available who can assist you with any deep learning topic.

Deep learning is employed across all industries for a variety of various tasks. Commercial applications and software that use computer vision, image recognition, open-source platforms with consumer recommendation apps, and medical research tools that explore the likelihood of reusing drugs for brand spanking new ailments are some of the samples of deep learning incorporation. 

10 Deep Learning Project Ideas for Beginners

As a beginner, it might be difficult to come up with ideas for projects, that's why we have decided to curate a list of 10 amazing deep learning projects for beginners.

1) Drowsiness Detection System

The problem statement here is to create a detection system that identifies key attributes of drowsiness and triggers an alert when someone is drowsy before it's too late. 

drowsiness detection system

 Image source 

The training and test data, in the problem statement: Real-Life Drowsiness Dataset formulated by a research team from the University of Texas at Arlington specifically for detecting multi-stage drowsiness. The final goal is to detect not only extreme and visual cases of drowsiness but allow our system to detect softer signals of drowsiness in addition. The dataset has around 30 hrs of videos of 60 unique members. From the dataset, we were able to extract facial landmarks from 44 videos of twenty-two participants. This allowed us to get a sufficient amount of information for both the alert and drowsy state. we will use a 1-D CNN model and send out the numerical features as sequential input files to do and understand the spatial relationship between each feature for the 2 states.

2) Digit Recognition System

Handwritten digit recognition has attained such a lot of popularity from aspiring beginners in the field of machine learning and deep learning to an expert who has been practicing for years. Creating such a system includes a machine to identify and classify the pictures of handwritten digits as 10 digits (0–9). Handwritten digit recognition from the MNIST database is already hugely famous among the deep learning community for several recent decades now, as decreasing the error rate with different classifiers and parameters. 

digit recognization system

A Digit recognition algorithm is that the working of a machine to train itself or recognizing the digits from different sources like emails, bank cheques, papers, images, etc. 

You can find the dataset: here

3) Neural Style Transfer

Neural style transfer is an optimization technique that works on three parameters in the form of image vectors, a content image, a mode reference image (such as an artwork and drawings/paintings by a famous painter), and also the input image you would like to style — and blend them together such the input image is reconstructed to appear just like the content image, but “painted” within the kind of the design image.

Somewhere between where the raw image input is fed in and also the classification label is output, the model is basically a complex feature extractor; therefore by obtaining the intermediate layers, we can describe the content and style of the input images and reconstruct them to a neural style transfer image.

4) Pneumonia Detection With Deep learning

Pneumonia is an infection that causes inflammation in the air sacs in either one or both lungs. It kills more children younger than 5 years old annually than any other communicable disease, like HIV infection, malaria, or tuberculosis. Diagnosis is usually supported by symptoms and physical examination. And the chest X-ray images are used to confirm the diagnosis.

This dataset has 5,856 verified Chest X-Ray images by a genuine source. This data is useful for developing/training/testing classification models with convolutional neural networks(CNN).

You can find the complete dataset: here

5) Crop Disease Detection 

The increasing resistance of crop pathogens to fungicides and pesticides poses a challenge to food preservation and compels the invention of recent antifungal compounds. Productive agriculture systems are always at risk of hazards of climate and pests and diseases causing threats to the food security of any nation. Healthy and productive crops not only are indispensable but are the very nature of humankind, the atmosphere, for food, fiber, energy, and general well-being. The project focuses on creating an algorithm that predicts diseases in crops using RGB images.

crop disease detection

Image Source

We can use a CNN model, to predict the diseases, and find the dataset to the matter statement: here

6) Pose Estimation

Pose estimation is the method of utilizing a Machine learning model to determine the pose and body coordinates of a body from an image or a video by estimating the spatial locations of key body joints (keypoints).

The output stride determines what quantity the output is scaled-down relative to the input image size. It affects the scale of the layers and also the model outputs.

The PostNet model inputs a preprocessed camera image as the input and outputs information about key points. The key points detected are indexed by an element ID, with a confidence score between 0.0 and 1.0. the boldness score indicates the probability that a key point exists in this position.

Find more information: here

7) Diabetic Retinopathy Detection

The main objective of this problem statement is to automate the method using Convolutional neural networks (using Python) to spur up blindness detection in patients before it’s too late. 

We require multiple image preprocessing techniques to approach this problem statement, for example, Image resizing, cropping should be applied to produce out distinctive features from eye images. 

8) Face Detection

Face detection is computer vision and image processing, the problem statements involve finding or detecting human faces in photos.

Locating a human face in a photograph attributes to obtain the coordinates of the face in the image, whereas the concept of localization indicates to differentiate the extent of the face f0eatures, with the help of a bounding box around the face.

9) One-Shot-Pokemon Images:

The dataset aims to detect and output the type of pokemon, using a classic deep convolution network with a softmax activation layer which can only achieve an accuracy of around below 30%. 

The structure of the dataset looks like something like this: There are three directories in the dataset are,

"pokemon-a" and "pokemon-b" are general pokemon images with no background. One can use them for training. "pokemon-TCG-images" are pokemon images cropped from pokemon TCG cards. We have to train models and perform testing. Filenames in all directories start with pokemon-id, aka class label. You have to create an algorithm of training and testing models. You can find the dataset: here

10) Detectron 

The project Detectron is Facebook AI Research's software system that implements state-of-the-art object detection algorithms, including Mask R-CNN. The project is created using Python and supported by the Caffe2 deep learning framework.

The objective of the project Detectron is to supply a high-quality, high-performance codebase for object detection research. it's designed to be flexible so as to support rapid implementation and evaluation of novel research.


The article addresses the definition of deep learning and learned about the application of deep learning and machine learning algorithms. We also discussed the top 10 deep learning projects for beginners and the resources to build them, with the explanation of the model architecture used.   

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About The Author
Apurva Sharma
Hi, I am Apurva Sharma, a data science engineer, deep learning evangelist and DevOps enthusiast