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How Do I Start a Deep Learning Project?

After exploring simple machine learning projects in detail, one must move ahead toward more challenging tasks and try their hand at deep learning. Deep learning consists of a collection of algorithms based on neural networks. Such algorithms are widely used to solve complex problems in data science, such as image classification and object detection. But. as a beginner, it can be challenging to transition smoothly from machine learning to deep learning. However, you don’t have to worry because you have landed on the perfect page. 

Follow the ProjectPro guide to start your journey into deep learning. If you have already worked on a few deep learning projects, this page will also prove helpful, as there is a separate section for intermediate and advanced professionals.

Deep Learning Projects for Beginners

For an effortless head start in developing your skills for implementing deep learning algorithms for solving real-world problems, try out the list of deep learning mini projects with source code below.

1) Fruit Identification Project

One of the most common applications of deep learning algorithms is classifying images into different categories. Why not let this be your first deep-learning project?

Source Code: Deep Learning Project- Real-Time Fruit Detection using YOLOv4 

2) Extracting Text from Images

Extracting textual information from images can benefit many users as they will save a lot of time if they have to type everything manually. You can also build your text detection system in Python. Use image processing techniques and a CRNN model to the TRSynth100K dataset and start building that system with the help of the source code below. 

 

Source Code: Deep Learning Project for Text Detection in Images using Python

3) Building a CNN with the PyTorch Project

Following the cliche of using the TensorFlow framework to solve an image classification problem is plain and boring until you haven’t explored PyTorch. As the next step, use PyTorch to build a CNN from scratch and design the different layers of a CNN on your own.

 

Source Code: Build a CNN Model with PyTorch for Image Classification

4) Multi-Class Image Classification Project

As the number of classes to predict increases, the solution becomes increasingly challenging. However, it also opens the door to understanding many activation and loss functions. So, download a dataset with three or more classes to predict and build a convolutional network to classify the given images.

 

Source Code: Build a Multi-Class Image Classification Model Python using CNN 

5) Human Face Recognition

Here is a fun project to make your journey of implementing deep learning projects more enjoyable. Collect the images of your family members and implement the predefined FaceNet model to build a system that can identify them correctly. This project on deep learning will help you understand how faces are extracted from images.

 

Source Code: Build a Face Recognition System in Python using FaceNet

6) Customer Churn Prediction

This deep learning project was developed in the R programming language. It uses a churn prediction model to estimate the number of Telecom company customers most likely to be subject to churn. This approach will allow the company to be sure about discontinuing a specific service on its platform.

 

Source Code: Deep Learning with Keras to Predict Customer Churn

Deep Learning Projects for Intermediate Professionals

At ProjectPro, you will find projects that fit beginners and relevant projects for intermediate professionals to upgrade their skills. Below, you will find a few samples of such projects. You can also use these projects if you are looking for deep learning projects for the final year with source code.

7) Enhancing the Cancer Treatment 

This deep learning end-to-end project assists in building a personalized medicine system by understanding the effect of genetic variants through deep learning models. It is also an NLP project as it introduced popular techniques like Lemmatization, Stemming, Tokenization, etc., which are used widely in NLP projects.

 

Source Code: Personalized Medicine: Redefining Cancer Treatment

8) Multi-Class Text Classification

Natural Language Processing methods and deep learning algorithms combined can solve many interesting problems in data science. In this project, you will explore the models RNN and LSTM by working on the customer complaints about the financial products dataset.

 

Source Code: Build Multi-Class Text Classification Models with RNN and LSTM

9) Music Genre Classification

Music is therapy for most people, but only if they listen to their favorite songs. So, collect audio files of your friends’ favorite songs and segregate them into categories based on their genre. After that, the LSTM/CNN algorithm will be implemented to classify them automatically.

 

Source Code: Music Genre Classification Project using Deep Learning

10) Text Summarization

Reading long texts can be time-consuming, and the content may only sometimes be relevant. In such cases, a summary of a long text can be helpful for many readers. Using the BART model, work on building a text summarization system and understanding how transformers are used in fine-tuning the model.

 

Source Code: Abstractive Text Summarization using Transformers-BART Model

11) Building a ChatBot

Engaging with customers on a personal level is only sometimes possible. In such cases, companies resort to chatbots that notify the customer care team members only when human intervention is needed. Use NLP techniques like tokenization, lemmatization, POS Tagging, Stemming, etc., and machine learning algorithms to build a chatbot in Python from scratch.

 

Source Code: NLP chatbot example application using Python

12) Fake News Classification

This deep learning project uses natural language processing techniques to detect and categorize misleading information, leading to media literacy and combating misinformation online while also contributing to developing trustworthy information ecosystems.

 

Source Code: NLP and Deep Learning For Fake News Classification in Python 

Advanced Deep Learning Projects with Source Code

After practicing the deep learning projects mentioned in the previous sections, it is time to level up your game with the advanced deep learning projects below.

13) Coloring Black and White Images using CNN

If one asks to think of Albert Einstein’s face, one will likely think of a black-and-white image. That’s because colored images weren’t a thing back in those days. However, as technology advances, one can now decode the colors of such images with the help of deep learning methods. So, build an image coloring system by implementing RPN, bounding box regressor, and transfer learning. To annotate the images, use the VGG annotator and MSCOCO datasets for training.

 

Source Code: Build CNN for Image Colorization using Deep Transfer Learning

14) Speech Emotion Recognition with ANN

The way humans communicate can reveal a lot about how they are feeling. In this project, you will learn how to use an artificial network to detect the emotion from the speech with the help of the RAVDESS dataset. Working on this project will help you understand how Fourier Transform plays a vital role in audio processing.

 

Source Code: End-to-End Speech Emotion Recognition Project using ANN

15) Anomaly Detecting with Autoencoders

Autoencoders are specialized deep-learning algorithms with similar architectures at input and output levels. They detect and remove noise in data. This project will learn how to deploy the complete model using Flask.

Source Code: Build Deep Autoencoders Model for Anomaly Detection in Python

16) Image Segmentation

It takes seconds for a tiny spark to evolve into a fire disaster. Thus, using computer vision for the early detection of such incidents can be an excellent way to avoid such accidents. This project will use the Mask R-CNN object detection model to build a fire detection system and learn about transfer learning and VGG annotator.

 

Source Code: Image Segmentation using Mask R-CNN with Tensorflow

17) Face Emotion Recognition

A Face emotion recognition system relies on deep learning models to identify human emotions from facial expressions, enabling emotion-aware applications, human-computer interaction, and psychological research into affective computing and emotional intelligence.


Source Code: Facial Emotion Recognition Project using CNN with Source Code 

 

As you may already know, there are only so many deep-learning projects. Each of the projects for deep learning mentioned above is equally important and must be thoroughly understood to become an expert in deep learning. However, if you are interested in exploring deep learning projects by looking at their algorithms, follow the categorization in the next section.

Types of Deep Learning Project Ideas

Deep Learning projects have been split into easy-to-browse categories for an effortless selection of deep learning projects based on your bias in Artificial Intelligence. Whether your bias is Natural Language Processing, Computer Vision, Machine Learning, or all, these interesting Deep Learning projects will give you the perfect opportunity to upgrade your skills and help land you a dream job.

 

First, we have listed projects that turn end-to-end machine learning project ideas into reality and gradually motivate Data Science enthusiasts to transition from machine learning to deep learning. Next, you will find a section on those projects that use NLP algorithms. After that, you will see a section on projects inspired by machine learning project ideas that mostly use deep learning algorithms. These projects are a good start for those curious about discovering how to learn deep learning through real-world applications-based projects.

Machine Learning and Deep Learning Projects

Below, you will find the latest deep learning projects that can also be considered end-to-end machine learning projects. You will find these deep learning projects helpful if you target projects that include both types of learning.

1) Cats vs. Dogs Classification

Cats vs. Dogs classification using ML and DL is a typical project many beginners implement when working on deep learning projects. The project is a benchmark problem in image classification tasks and pet adoption platforms while providing insights into animal behavior and species differentiation.

2) Gender Recognition

A gender recognition system uses facial analysis and machine learning to infer gender from images or videos, facilitating personalized marketing strategies, demographic analysis, and gender-aware applications in various domains such as security and retail.

3) Breast Cancer Detection

Breast cancer detection utilizes machine learning algorithms to analyze medical imaging data, aiding in early diagnosis and treatment planning for improved patient outcomes. Coloring black and white images employs image processing techniques to enhance visual representation, enriching historical photographs or medical scans with vibrant hues and details.

4) Recognizing Human Pose

Many fitness tracker applications track humans' activities through various mobile phone sensors. This project will use deep neural networks to help understand how such applications leverage sensor information to identify human activities.

NLP and Deep Learning Projects

This list is the perfect guide for deep learning projects implementing NLP methods. 

5) ChatBot

This project on deep learning uses neural networks to build a conversational bot or chatbot for your website from scratch. If you don’t know, ChatBots are increasingly becoming popular; thus, learning how to make one for an NLP enthusiast is a must.

 

Source Code: Building a ChatBot from Scratch

6) Language Translator

A language translator using Natural Language Processing (NLP) and Deep Learning (DL) techniques interprets and converts text or speech from one language to another with high accuracy and fluency. Advanced neural network architectures enable seamless cross-lingual communication and comprehension, catering to diverse linguistic needs in today's globalized world.

Deep Learning Computer Vision Projects

This list contains the best deep learning projects with source code. A few deep learning projects are simple and can also be considered perfect for those searching for computer vision projects for beginners.

7) Polyps Detection

Small Clumps of human cells found inside the body of humans are called Polyps. They are usually harmless, but they can evolve into cancer. This deep-learning project uses images from colonoscopy videos to make a system that correctly identifies polyps. 

 

Source Code:  Medical Image Segmentation

8) Similar Images Finder

The Google Similar Image Finder is a perfect and highly advanced example of this deep learning project. As you must have guessed by now, this deep learning project uses deep learning algorithms to build a system that can swiftly search for images similar to the one provided at the input.

 

Source Code: Image Similarity Application

9) Driver Drowsiness Detection

Data collected from car sensors can be leveraged to detect signs of driver fatigue in real-time video streams. Using OpenCV's computer vision capabilities, you can track a driver's facial landmarks and eye movements to identify drowsiness, accurately enhancing road safety.

10) Neural Style Transfer

Neural style transfer applies deep learning to create visually appealing compositions by transferring artistic styles onto target images. This technique offers unique aesthetic effects for art, design, and multimedia applications while advancing computer graphics and image processing research.

 

Not always have a bias toward the Artificial Intelligence subdomain. They are intrigued by specific algorithms used in deep learning end-to-end projects. ProjectPro experts have grouped projects on deep learning based on their algorithms. If you are curious about any particular algorithm or want to learn deep learning through an algorithm that you are already familiar with, check out these categories.

Convolutional Neural Network Projects for Beginners in Deep Learning

Because of the complex algorithms, the deep learning projects using CNN listed below can be labeled as CNN projects for beginners and as deep learning intermediate projects.

11) Handwritten Digits Recognition

This project is one of the most popular deep-learning Python projects from ProjectPro’s repository. It uses the famous MNIST dataset to design a handwritten digit recognition system, one of the most interesting deep-learning project ideas.

12) T-Shirt Image Classification

This project is one of the most interesting deep-learning beginners projects. It is the first choice of a project to understand object detection. It assists in classifying objects that need to be covered in standard datasets. This project aims to perform binary classification by training a predefined CNN model.

 

Source Code: Tensorflow Transfer Learning Model for Image Classification

13) Crop Disease Detection 

Crop disease detection utilizes image processing techniques to analyze plant images and identify signs of diseases or abnormalities. Deep learning algorithms, such as convolutional neural networks, enable timely diagnosis and intervention, aiding farmers in crop management and yield preservation. To learn more, we recommend you read ‘Plant Disease Detection Using Image Processing and Machine Learning’ by Kulkarni et al.

14) Object Detection

This deep learning project is based on convolutional neural networks, which are used to locate and classify objects in images or video frames. These networks enable autonomous driving, surveillance, and augmented reality applications and contribute to advancements in robotics and image understanding.

15) Dog Breed Classification

Dog breed identification utilizes computer vision algorithms to classify dog breeds accurately from images. It assists pet owners and breeders in breed recognition and selection while contributing to animal genetics and behavior research.

Neural Networks and Deep Learning Projects

This section has deep learning projects implementing ideas using Neural Networks. These projects will help you understand deep neural networks thoroughly. As neural network algorithms are often considered machine learning algorithms, you can feel each as an end-to-end machine learning project.

16) Face Detection and Recognition System

A face detection and recognition system uses computer vision algorithms to locate and identify human faces within images or video footage. Analyzing facial features and patterns enables automated identification and verification tasks, facilitating security, surveillance, and personalized user experiences.

17) Stock Price Prediction

Stock price prediction leverages machine learning and deep learning to forecast future prices of financial assets or commodities. It aids investors and traders in decision-making and risk management and contributes to economic modeling and market analysis research.

 

Source Code: Stock Price Prediction Project using LSTM and RNN 

18) Recommender Systems

Recommender systems provide personalized recommendations using collaborative filtering or content-based approaches. They enhance user experience and engagement in e-commerce and content platforms while driving business sales and customer satisfaction.

 

Source Code: Build a Collaborative Filtering Recommender System in Python 

19) Loan Application Classifier

This deep-learning project uses the German Credit Dataset to classify loan applications. 

 

Source Code: Analysis of German Credit Dataset-based Deep Learning Project

Why Build Projects in Deep Learning with Python?

Python is an object-oriented programming language with many useful libraries for smoothly deploying data science projects. Here are a few key points highlighting Python's benefits for deep learning tasks.

  • Libraries like Scikit-learn support the easy implementation of machine learning algorithms.

  • TensorFlow and Keras provide the perfect framework for building deep learning algorithms.

  • Python supports modular programming through its functions.

  • Python is easy to learn and is one of the most beginner-friendly programming languages.

  • Libraries like NumPy, Pandas, Matplotlib, etc., support quick analysis of datasets, which assists in fine-tuning algorithm parameters.

  • The language is open source and freely available. Additionally, it is well-supported by a strong community of developers.

Many features make Python suitable for implementing deep learning projects. To understand what makes it appropriate, you need to know how deep learning is used in the real world, and working on practical projects is one of the best approaches. So, gear yourself to explore ProjectPro’s repository to learn how to implement deep learning project ideas. 

Frequently Asked Questions on Deep Learning Projects

1) How to do deep learning projects with the cloud?

Deploying a Deep Learning Model on the Google Cloud Platform

  • Step 1: Log into Google Cloud to create an f1-micro instance on Compute Engine.

  • Step 2: The next step is to take a machine learning model from an open-source platform like Github and train it before exporting it.

  • Step 3: Considering the instance's limited memory space, you must add swap memory. This will enable you to install all the required deep-learning learning libraries in your instance.

  • Step 4: You'll need to write a Python script to serve your model on the web using the Starlette ASGI web framework.

  • Step 5: You'll need to use Docker to create a container for your application. It allows you to execute the application anywhere in its environment.

  • Step 6: All you have to do now is start your Docker container using your machine's External IP address, which you can locate on Compute Engine.

2) What are some good projects on deep learning?

  • Image Segmentation using Mask R-CNN with Tensorflow - In this Deep Learning Project on Image Segmentation, you will learn how to implement the Mask R-CNN model to train and build predictions over your input images.

  • Multi-Class Text Classification with Deep Learning using BERT - You'll use BERT, one of the most popular state-of-the-art Transformer models for Multi-Class Text Classification. Also, you will pre-train the model on two NLP tasks: Masked Language Modelling (MLM) and Next Sentence Prediction (NSP).

  • Build Deep Autoencoders Model for Anomaly Detection in Python. In this deep learning project, you will use Flask to develop and deploy a deep autoencoder model to learn distributions and correlations between aspects of regular transactions.

3) Why should you work on ProjectPro’s Deep Learning Projects?

  • Deep Learning has revived AI, becoming a dominant technology in computer vision, language translation, voice recognition, and self-driving cars. If you want to understand how these deep learning systems work and develop your own, then ProjectPro’s deep learning projects are for you.

  • Go hands-on with implementing deep learning techniques like neural networks, convolutional networks, and recurrent neural networks -skills employers eagerly seek.

  • Develop a verified project portfolio with hands-on deep learning projects that will showcase the most in-demand skills you acquire to employers.

4) Who should work on ProjectPro’s Deep Learning Project Ideas?

  • Anyone who is interested in learning about technology revolutionizing how we interact with the world around us.

  • Programmers who want to dive into the lucrative Machine Learning and AI career path will learn a lot from these deep learning projects or beginners.

  • Students who want to implement deep learning concepts through practical projects using TensorFlow, Keras, and Python.

5) What are the key learnings from ProjectPro’s Deep Learning Projects?

  • Understand what deep learning is in practicality and how it differs from machine learning.

  • Understand what Neural Networks are and how you can train a neural network.

  • Learn to implement recurrent neural networks to perform language translations.

  • Learn to implement convolutional neural networks for image classification.

  • Understand the concept of generative adversarial networks for generating images.

  • Become a deep learning guru by independently developing the ability to solve diverse real-world use cases using deep learning techniques.

6) What will you get when you enroll in ProjectPro’s Deep Learning Projects?

  • Deep Learning Project Source Code: Examine and implement end-to-end real-world interesting deep learning project ideas, such as Image Recognition and language translation.

  • Recorded Demo – Watch a video explanation of how to execute the deep learning project examples.

  • Mentor Support – Get your technical questions answered with mentorship from experienced data scientists. If you are stuck working on any of these deep learning projects, our industry experts will be happy to guide you through the projects. One can avail of that by opting for the mentorship track for each project, where you can post your queries to the instructor and have a healthy discussion.

  • Complete Project Solution Kit—You will get access to the dataset, solution, and supporting reference material, if any, for every deep learning project.

7) What are some cool AI Projects?

Here are a few cool AI projects for you to explore:

  • Face Mask Detection

  • Dog and Cat Classification

  • Social Distancing Checker

  • Text Recognition

  • Face Recognition

8) What can you do with Deep Learning?

Deep Learning is the subdomain of artificial intelligence that leverages artificial neural networks to develop better and more efficient algorithms. One can use them for Image Classification, Object Detection, Face Recognition, Text Identification, etc.

9) Is Deep Learning AI?

Deep Learning is an emerging field considered a subdomain of Artificial Intelligence (AI). It involves implementing algorithms based on artificial neural networks.

10) How is deep learning used in the real world?

Deep Learning is used to solve various practical problems in the real world.

  • Deep Learning algorithms form the basis of many face recognition systems, such as attendance and face unlocking systems.

  • Convert handwritten text into digital information through text detection systems.

  • Deep Learning algorithms are often in the background of applications that identify fake news.

  • It is readily being used to improve healthcare treatments and disease identification.

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