20+ Deep Learning Projects for Beginners with Source Code

As a beginner in the data industry, it can be overwhelming to step into AI and deep learning. After taking a deep learning course or two, you might find yourself getting stuck on how to proceed. You don't know what to learn next because you have the theoretical know-how of the concepts and no hands-on experience working with diverse deep learning frameworks and tools.This article will break down the steps you can take to enhance your deep learning skills. To answer any questions you might have before you start pursuing a career in deep learning, let's put first things first -

• Why is deep learning important?
• Is it difficult to build deep learning models?
• Why build deep learning projects?

Deep Learning Project for Beginners with Source Code Part 1

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Finally, this article will take you through 20+ cool deep learning projects you can build as a beginner in the industry.

Why build deep learning projects?

With the technological advancements and the increase in processing power over the last few years, deep learning , a branch of data science that has algorithms based on the functionalities of a human brain, has gone mainstream. The most popular advancements in machine learning are applications of deep learning — self-driving cars, facial recognition systems, and object detection systems. Deep learning based deep neural network algorithms have transformed industries like agriculture, retail, and manufacturing. 

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The global Artificial Intelligence market is expected to grow over $120B by 2025. There is thus a rapid increase in the demand for deep learning engineers.  Many large multinational companies like Microsoft, Amazon, Facebook, and Nvidia recruit deep learning practitioners to build and train neural networks.

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Once you learn the basics of deep learning algorithms and understand how to build models using existing libraries, you can start implementing hands-on, real-world deep learning projects. If you already know the basics of deep learning concepts and have a working knowledge of libraries like Keras, PyTorch, and Tensorflow, you can get started right away.

For beginners looking to get an entry-level job in the machine learning industry, deep learning projects for beginners are the best way to demonstrate that you have the skills necessary to do the job. Potential recruiters skim through hundreds of resumes for each job application. If you don't have a Master's degree in machine learning, the only way to prove that you have the skills necessary to do the job is by building projects. Creating interesting and cool deep learning projects around the concepts you learned will help your data science portfolio stand out compared to other applicants and increase your chances of landing a job in the industry. So, if you're looking for a field that promises both intellectual fulfillment and great career prospects, then deep learning is the perfect choice! As you've seen from the reasons listed above, possessing deep learning skills is likely to open up a world of exciting opportunities for you in the field of AI. So why not take the leap and explore the fascinating world of deep learning? 

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20+ Deep Learning Project Ideas for Beginners in 2024

This article will provide you with 20+ simple deep learning projects for beginners. All of these are relatively easy projects to build as long as you have basic programming knowledge.

Simple Deep Learning Projects for Beginners/Students

Here are a few deep learning project ideas for beginners to practice deep learning topics.

1. Cat vs. Dog Image Classification System 

If you are a beginner in deep learning, this is a project you should start with.First, you will need to find a labeled dataset of cat and dog images. There are many of these available on Kaggle.

Image Credit: Freepik.com 

You can then build a deep learning model that takes in these images as input, implements a few image processing methods and  and predicts whether the input image is of a cat or a dog.To build this model, you can create a convolutional neural network from scratch using Keras or Tensorflow. There are many examples of building neural networks to differentiate between cats and dogs so that you can download the source code for this online.If you prefer to use transfer learning, you can download a pre-trained deep learning model like VGG16 to create the classifier.After building this model, you can choose to deploy it to the web. You can even create a web application that allows users to input an image of their pet and come up with a prediction. This is one of the easiest deep learning beginner projects that is a must try for all the newbies in deep learning. 

2. Covid-19 Detection in Lungs

One of the biggest challenges following the Covid-19 pandemic is the detection of the disease in patients. You can build an image recognition model that can detect the presence of Covid-19 from an X-Ray or CT-Scan image of a patient's lungs.

Image Credit: Freepik.com 

To accomplish this project on deep learning, you will need to find a dataset of labeled X-Ray images and train a convolutional neural network(CNN) on it.

3. Digit Recognition System

In this deep learning project, you will need to build a model that can recognize handwritten digits.The most popular dataset available for this purpose is the MNIST digit classification dataset. This dataset consists of 70,000 images of handwritten digits for you to train and test your model.

To build this digit recognition system, you can either use either of the two deep learning algorithms- a simple feed-forward neural network or a convolutional neural network.One disadvantage of doing this MNIST digit recognition project  is that it is relatively popular. It has been done by beginners in deep learning many times before, so this project won't help your resume stand out.However, it is a great project to start with as a beginner in the industry and is a helpful learning experience before moving on to more complex or advanced deep learning projects.

4. Facial Recognition Application

A human facial recognition application is an interesting deep learning project to add to your portfolio. You can find an existing dataset of labeled faces on the Internet. Then, train these images on top of a pre-trained model or a convolutional neural network. The facial recognition technology is an exciting application of deep learning for beginners and is a must try!

5. Face Mask Detection

During the Covid-19 pandemic, governments in many parts of the world have made it mandatory to wear face masks when going out. However, many people choose to disobey the law and are seen in public without face masks. An automated face mask detection system based on deep learning architectures can help solve this problem.

You can build this project using computer vision libraries in Python like OpenCV and Keras in Python. There are many open datasets of labeled images you can find for this purpose on the Internet. To further enhance this project, you can build a model that detects the presence of face masks on a live webstream. There are many learning resources available online that can teach you to do this.

Intermediate-level Projects on Deep Learning

Here are a few DL projects ideas for those who have already have a fair experience in the domain.

6. Cyber-Attack Prediction

The advancement in technology has brought with it an increased risk of cyber-attacks. Predicting the onset of cyber-attacks is a huge problem for large organizations. Many of these organizations rely on third-party AI solutions to prevent and detect cyber-attacks before they occur. You can build a deep learning model trained on labeled web traffic data. Then, you can use this model to predict whether incoming traffic is an attack.

7. Automated Attendance System

This project is an extension of the facial recognition system project described above. Many schools and universities find it difficult to track student attendance. Students can easily manipulate the system by signing or taking attendance on another student's behalf, so there is no sure way of ensuring that a student is present in class. You can build a facial recognition system that takes attendance for students.

To do this, you will first need to compile a database of labeled student images. Then, you will need to train a convolutional neural network on these images.After this, every time a student scans their face to record attendance in class, the facial recognition system needs to match their face to the images present in the database.This neural network project is slightly more advanced than the other projects mentioned in this article, mainly because it involves knowledge beyond Python and deep learning.You will need to understand databases and web development before taking on a project like this.Regardless, this project will be a great learning experience for you since it assumes many more than just machine learning skills. It will also be a great value add to your data science or machine learning portfolio.

8. Text Generator

Text generation has always been one of the most interesting and challenging applications of deep learning techniques. Sequential data like the text is often difficult to generate because it involves an understanding of context. Even if a model generates words that are often closely associated with each other based on past datasets, one wrong word can render the entire sentence meaningless. This is a fun project to do and will be nice to showcase on your portfolio. Even if your text generator ends up producing complete gibberish, it will be fascinating for potential recruiters to read an AI-generated story.

 9. Dating App Algorithm

This is an unconventional project and by far a unique one on this list. Several dating sites use machine learning algorithms to pair users who are compatible with each other. You can take this a step further and use deep learning to identify a user's age and appearance. This way, users of an app can specify the appearance of an individual they would like to match with, and your algorithm can filter and find matches that fit in with this requirement. Furthermore, many people on dating sites lie about their age. You can train a deep learning model to identify an individual's age based on their picture, and your model can filter out individuals who are underage or lying about their age.

10. Emotion Recognition

Another project you can build is a model that can identify human emotions. The best part about building a model like this is creating your own dataset of training images. Take around 100 pictures of yourself displaying various emotions — happy, sad, angry, frustrated, etc. Then, you can use a pre-trained model or train a convolutional neural network on these images.

After that, you can upload more of your images and let the model detect the emotion behind the photo. You can also create a live video stream and show different emotions, allowing the model to perceive how you feel in real-time.

Best Deep Learning Projects for Advanced Professionals

Here are a few challenging deep-learning-based projects for experts in the field.

11. Breast Cancer Detection

In this project, you can use deep learning models to identify breast cancer using a labeled dataset of mammograms. There are many open datasets on Kaggle that contain images obtained from mammography so that you can download one of them as your training dataset. If you want to build and train your deep learning model from scratch, you can create a recurrent neural network (RNN) or a convolutional neural network (CNN). If you want to use a pre-trained model, you can use Res-Net, VGG16, or VGG19. All of these models are included as a function in the Keras library.

12. Object Detection System

An object detection system might sound similar to a facial recognition system, but they are two different things. Once you build an object detection system, your model will capture all objects within an image and not just a face. This project has excellent business applications, as many security companies use object detection systems to identify threats. Even Tesla autopilot uses object detection to identify objects on the road to avoid collision when driving.To build an object detection system, you can use several pre-trained models like Resnet50, Yolo, and SSD. Many of these libraries are built into Keras and Tensorflow, so it will be easy for you to get started.

13. Recommender System

Recommender systems are one of the most popular applications of machine learning. Companies like Netflix, Spotify, Linked In, and Amazon use recommender systems to provide their users with relevant content. Netflix held a worldwide contest a couple of years ago. They provided the public with an open movie dataset, and people from all over the world competed to build a user rating prediction model based on the data provided. Netflix provided a 1 million dollar cash prize to the winning team.

Although the contest is no longer open, the dataset is still available to the public on Kaggle. You can build an artificial neural network and train it on Netflix's open dataset to come up with user rating predictions.

14. Celebrity Look-alike Model

This project is another extension of a facial recognition system. However, in this project, you will use a training dataset of celebrity faces. Once you train the model, you can upload your image to test it. The model will return the celebrity you look most like, telling you who your celebrity look-alike is. This is a fun project to have on your portfolio, especially since other people can use it too if they want to find their celebrity look-alike.

15. Chatbot

A chatbot is another deep learning application you can build. It will be a great project to showcase on your portfolio because of its business value. Many companies use chatbots to communicate with their clients and provide customer support. Building a deep learning model that can interact with humans and generate responses is a great value add to businesses.

You can build the model with the help of Python libraries like NLTK and Keras. You can also deploy the model you built by launching the model alongside an online chat service like Facebook messenger.Once you integrate your model into an online chat service, the chatbot will reply to anybody who messages you. Python Chatbot Project is an excellent choice to make your deep learning project portfolio stand out.You can even link your Facebook messenger page to your portfolio so that anyone who comes across your page will be able to converse with your chatbot.

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Deep Learning Projects on Kaggle

This section contains Kaggle deep learning projects that you can implement by utilizing the dataset available on Kaggle.

16. Plant Diseases Detection

With the increasing demand for food production and the potential impact of crop diseases on food security, the challenge provides a valuable opportunity for researchers and practitioners to develop innovative deep learning models for plant disease detection. 

Participants in this challenge must use deep learning models to categorize photos of plant leaves into several disease groups. The dataset includes about 186,000 photos of plant leaves that have been labeled with one or more of the rust, scab, or complicated disease categories. To appropriately categorize the images into each category, you are expected to build a deep learning model. You can use image classification models such s VGG-16, ResNet, and Inception can be used as a starting point, and various transfer learning techniques and model architectures can be explored to improve the accuracy of the model. Additionally, data augmentation and preprocessing techniques can be employed to enhance the robustness of the model to different image variations. If you are someone who wants to contribute to the field of plant pathology and agriculture, this project is a must.

Kaggle Challenge Link: https://www.kaggle.com/competitions/plant-pathology-2021-fgvc8/overview 

17. Landmark Recognition

Landmark Recognition is a challenging problem in computer vision, as it requires identifying landmarks from images with varying angles, lighting conditions, and occlusions. The dataset provided for this Kaggle challenge includes over a million images of landmarks from around the world, making it one of the largest and most diverse datasets for landmark recognition. Participants in the challenge must develop and train a deep learning model that can accurately classify each image into its corresponding landmark category. To address this problem, transfer learning techniques can be employed to leverage pre-trained models and speed up the training process. Additionally, advanced data augmentation techniques and model ensembling can be used to improve the model's accuracy and generalization ability. This challenge provides an opportunity for researchers and practitioners in the field of computer vision to explore and develop innovative solutions for landmark recognition.

Kaggle Challenge Link: https://www.kaggle.com/competitions/landmark-recognition-2021 

18. Skin Cancer Detection

This Kaggle challenge requires participants to develop a deep learning model that can accurately classify skin lesion photographs into benign and malignant categories. The dataset includes over 33,000 skin lesion photos, each labeled with its corresponding diagnosis. To achieve high accuracy in skin lesion classification, deep learning models such as convolutional neural networks (CNNs) can be used. Transfer learning techniques and pre-trained models such as VGG, ResNet, and Inception can also be leveraged to speed up the training process and improve model performance. Additionally, techniques such as data augmentation, regularization, and hyperparameter tuning can be used to enhance the model's accuracy and robustness. 

Kaggle Challenge Link: https://www.kaggle.com/competitions/siim-isic-melanoma-classification/overview 

Deep Learning Projects on GitHub

This section will be helpful if you are looking for deep learning projects with source code on github.

19. StyleGAN2

A deep learning model called StyleGAN2 can produce excellent photos of people, places, and other items. On GitHub, you can find the official TensorFlow implementation of StyleGAN2 as well as pre-trained models and scripts for creating your own models. Anyone interested in learning more about the cutting-edge of generative deep learning models should check out this repository.

Repository Link: https://github.com/NVlabs/stylegan2 

20. PyTorch-YOLOv3

YOLOv3 is a popular object recognition model capable of accurately and quickly detecting numerous objects. Version three of the YOLO model makes use of PyTorch, a widely used deep learning framework, and comes with pre-trained weights. This repository contains code for training and testing the model, along with pre-trained weights for object detection tasks. Anyone interested in creating real-time object detection applications utilizing deep learning, as well as researchers wishing to experiment with YOLOv3 and PyTorch, should check out this repository.

Repository Link: https://github.com/eriklindernoren/PyTorch-YOLOv3 

21. Tacotron 2

An implementation of a deep learning model for producing human-like speech from text can be found in the Tacotron 2 project on GitHub. The Tacotron 2 model is intended to translate text input into a corresponding audio output that is clear and natural-sounding. Along with the model's PyTorch implementation, the repository offers pre-trained models and sample applications. The Tacotron 2 model is built on a sequence-to-sequence architecture, using a combination of convolutional neural networks (CNNs) and recurrent neural networks (RNNs) to generate speech. The repository contains code for audio preprocessing and post-processing, data preprocessing and visualization.  You must check out this deep learning project if you are interested in developing speech synthesis applications using deep learning.

Repository Link: https://github.com/NVIDIA/tacotron2

Now Is The Best Time To Get Started on Your Deep Learning Journey!

To embark on your deep learning journey, the best way u can start working on one (or all) of the deep learning project ideas listed above. Most of these projects are relatively simple to get started with, such as the MNIST digit classification model. Once you get well acquainted with the basics and are well-versed with Python and libraries like Keras, you can go ahead and start building more significant projects like the automated attendance tracking system. Building deep learning and visual computing projects can be time-consuming and challenging at times. It is easy to feel lost along the way due to the number of skills you need to learn.

However, remember that building end-to-end deep learning projects will equip you with the tools necessary to become a deep learning engineer. Showcasing these projects on your resume will also help you stand out to potential recruiters, and you will be able to land a job in the data industry reasonably quickly.

Getting acquainted with building a variety of these projects will help you build up your skills, and you can even join deep learning contests on Kaggle that give away large cash prizes.

FAQs on Deep Learning Project Ideas

How do I start with a deep learning project?

To start a deep learning project, first, choose a problem to solve or a task to automate. Then, collect and preprocess data relevant to the task. Next, choose a deep learning framework and create a model architecture. Train the model on the data, evaluate its performance, and refine it if necessary. Finally, deploy the model and monitor its performance.

How do you optimize the performance of your deep learning model? 

To optimize the performance of your deep learning model, you can experiment with different hyperparameters, regularization techniques, data augmentation, pre-trained models, advanced architectures, hardware acceleration, ensembling, early stopping, and visualization. By carefully fine-tuning and improving these aspects, you can achieve better accuracy, speed, and generalization in your model.

What can you build with deep learning?

Deep learning can be used to build a wide variety of applications, including image and speech recognition, natural language processing based systems, recommendation systems, fraud detection, autonomous vehicles, robotics, and medical diagnosis. With its ability to learn from large amounts of data and make accurate predictions, deep learning has the potential to transform many industries and fields.

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