Then there are some fresh Deep Learning projects for students. A more practical approach like building projects is as important as theoretical knowledge to work in a real-time work environment.

What is Deep Learning? 

There is an increasing demand for artificial intelligence and machine learning in today’s era. From recommendation systems to image processing, there are many applications of deep learning as well. 

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 higher cognitive processes. Deep learning can be called a subset of machine learning in AI.

It has networks capable of learning unsupervised from data that's unstructured or unlabeled. Deep learning has evolved hand-in-hand with the digital era, which has caused an explosion of data in every shape and form. According to Glassdoor, deep learning engineers can get annual packages up to $140,000.

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 few applications. 

It is also an important subject in universities right now. But since it is quite a new domain, students cannot easily resolve their doubts. For that, you can connect with a deep learning tutor online for 1:1 private lessons.

5 Deep Learning Project Ideas:

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. This is really an interesting idea to test your skills on.

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Here is the training and test data for 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 extreme and visual cases of drowsiness but also 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, you can extract facial landmarks from 44 videos of 22 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 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. You can check the dataset for Digital Recognizer.

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

3) Neural Style Transfer

Neural style transfer is an optimization technique that works on three parameters in the form of image vectors. There is a content image, a mode reference image (artwork by a famous painter), and also the input image you would like to style.

We 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 into a neural-style transfer image. This is really a complex deep project idea that can take you deep down into the concept of the subject.

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.

Chest X-ray images are used to confirm the diagnosis. So, in this dataset, we have 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).

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.

   

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We can get a dataset of agriculture crop images and use a CNN model to predict the diseases.

 

Conclusion

Here we have addressed the definition of deep learning and discussed deep learning projects for beginners to build, with an explanation of the model architecture used.  Now you need to find which problem interests you and start working on it.