Deep learning is a subunit of machine learning that includes algorithms inspired by the brain’s function and structure. These complex algorithms are called artificial neural networks, and they form the foundation of all deep learning simulations.
Deep learning algorithms exist in a hierarchy of increasing abstraction and complexity. Therefore, the best way to learn about deep learning approaches is to gain hands-on experience through projects of varying complexity. This guide offers you various ideas for deep learning projects so you can build technical expertise for your resume and portfolio and land your dream career in technology.
5 Skills That Deep Learning Projects Can Help You Practice
Before discussing the different projects, you must be familiar with the skills you’ll practice along the way. Some of these techniques might be already familiar to you, but if not, you can learn all of them through online deep learning courses and by completing projects of various skill levels. When you start taking on new tasks, you’ll not only build your technical skills but your confidence, too.
- Natural Language Processing (NLP). NLP means understanding and processing the interactions between natural human language and computers. NLP technologies process natural language data extensively by analyzing tasks. For example, improving speech recognition has dramatic implications for many different industries, making NLP a very in-demand skill.
- Robotic Process Automation (RPA). RPA is the application of technology that allows professionals to customize computer software to capture and expound existing applications in processing a transaction, triggering responses, manipulating data, and communicating with other digital systems.
- Core Data Science Skills. These skills include big data analytics and data science fields like Python, C++, Java. Additionally, you should build experience with open source development environments like MATLAB, Spark, and Hadoop.
- Computer Science Fundamentals and Data Structures. This includes skills in data structures, Github, software development, and algorithms. These skills help you solve any problems presented to you by a client or company.
- Mathematics for Machine Learning. This skill will help you analyze any algorithm and regulate it according to your needs. You will also be able to understand concepts like gradient descent, mean, median, mode, and more.
Best Deep Learning Project Ideas for Beginners
As a beginner, you might find it hard to start deep learning projects. The suggested ideas below, however, are simple. They will strengthen the skills needed to prepare you for more complex and more demanding projects.
- Deep Learning Skills Practiced: Core Data Science Skills, Mathematics for Machine Learning, Natural Language Processing, Big Data Analysis
In this project, your task is to develop an image classification system that would determine the class of an input image. First, check out CIFAR-10, a large online dataset with over 60,000 colored photos. Each image is classified into one of ten classes, and each class has 6,000 photos in it. Then, divide each digital image and sort it into different sections while arranging it randomly.
- Deep Learning Skills Practiced: Robotic Process Automation, Computer Science Fundamentals, Data Structure, Mathematics for Machine Learning
This project includes a tutorial for source code designed to follow and visualize human faces within digital images. You’ll learn how to perform face recognition tasks in the present time using OpenCV and Python. Face recognition technology is evolving tremendously right now, so this project could help you learn about the latest deep learning and facial recognition technology.
- Deep Learning Skills Practiced: Visual Recognition Technology, Core Data Science Skills, Computer Science Fundamentals and Data Structure
This project helps users design an interface to locate and track moving objects through a camera. You’ll use a deep learning algorithm that will examine sequential video frames and monitor the motion of target objects between frames. This handy tool has several applications including surveillance and security, augmented reality, traffic control, and human-computer interaction.
- Deep Learning Skills Practiced: Predictive Modeling, Neural Network Algorithms, Mathematics for Machine Learning, Natural Language Processing
In this project, users can identify crop diseases using RGB images and deep learning techniques. This is done through various training datasets and deep learning architecture models like AlexNet and GoogLeNet. In doing this project, you need to use Convolution Neural Networks (CNN), which is designed to select an image, determine what disease is present, and identify that disease to the user.
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- Deep Learning Skills Practiced: Computer Vision, Data Structures, Mathematics for Machine Learning, Natural Language Processing, Predictive Modeling
In this Python-based deep learning project, you have to create an interactive app that recognizes the chosen color from any picture. To complete this project, you will install and utilize open-source libraries like OpenCV and NumPy. The user must generate a labeled dataset of all the colors before determining which shade looks similar to the chosen color value. Doing this project would test your knowledge in subjects like Pandas, Computer Vision, and Python libraries.
Best Intermediate Deep Learning Project Ideas
If you’ve already tried some beginner projects and tested your skills at an introductory level, you can check out these more complex deep learning project ideas. These projects test more complex technical skills that will help you stand out in the job market.
- Deep Learning Skills Practiced: Predictive Modeling, Core Data Science Skills, Computer Science Techniques, Machine Learning, Natural Language Processing
This project’s purpose is to create a deep learning model designed to identify numbers from zero to nine. These algorithms identify digits through a combination of deep neural networks and shallow networks, which is possible with statistical techniques like logistic regression. You can use either multinomial logistic regression or softmax regression for this particular project to achieve your final result.
- Deep Learning Skills Practiced: Data Science Skills, Predictive Modeling, Algorithmic Training, Natural Language Processing
For this project, you create a chatbot using IBM Watson’s API. This project aims to control Watson’s deep learning abilities with predictive modeling and design an insightful chatbot that can interact with any given user. For this project, you need Python 2/3 installed on your machine, an active internet connection, and, most importantly, a Bluemix account.
Music Genre Classification System
- Deep Learning Skills Practiced: Python, Core Data Science Skills, Data Structures, Machine Learning
In this project, you will create a deep learning model that utilizes recurrent neural networks that can classify the music’s genre automatically. You’ll be using the Free Music Archive, or FMA dataset. This open-source and easily accessible dataset is excellent for hosting MIR tasks such as organizing vast music collections and browsing. It is also an interactive library that contains tons of legal audio downloads.
To begin this project, you’ll need to download the open-source dataset and create a Python environment for installation. Remember that before you can use the model to categorize audio files by genre, you’ll have to extract related information from audio samples like MFCC and spectrograms.
- Deep Learning Skills Practiced: Robotic Process Automation, Computer Science Fundamentals, Machine Learning Model
This project would be helpful to many researchers, students and professionals where a summarized text is required. For this project, you’ll be using a deep neural network along with natural language processing. The goal of this is to transform several paragraphs into a summarized version.
- Deep Learning Skills Practiced: Computer Science Fundamentals, Machine Learning Model
Nowadays, a lot of people want to add color back into old photos. So for this project, you’ll use a modeled image that can be turned from black and white to a colored one.
Best Advanced Deep Learning Project Ideas
For advanced project ideas, you’re expected to be more knowledgeable in using deep learning methods. If you’re looking to put your skills to the test and boost your resume, here are some of the best deep learning projects to work on.
- Deep Learning Skills Practiced: Robotic Process Automation, Facial Recognition Software, Computer Science Fundamentals, Machine Learning Model
The project’s main objective is to decrease the number of accidents of drivers that fall asleep at the wheel. You’re able to gauge the driver’s symptoms by creating a drowsiness detection software using OpenCV, Python, and Keras. By using OpenCV, you’ll be able to collect and observe the driver’s images through a webcam and then put those into the deep learning model.
- Deep Learning Skills Practiced: Robotic Process Automation, Artificial Intelligence Software, Core Data Science Skills, Mathematics for Machine Learning, Data Structures
This project aims to design an open-source Artificial General Intelligence (AGI) framework that can precisely capture the dynamics and architecture of the human brain. The core components of this project can facilitate AI algorithms and systems with natural language processing, memory, common-sense reasoning, and motor control. Sophia, an AI bot, is one of the best examples of AGI.
- Deep Learning Skills Practiced: Robotic Process Automation, Computer Science Fundamentals and Data Structure, Mathematics for Machine Learning
DeepMimic is an algorithmic neural network that uses reinforcement learning to duplicate motion from a human or other physical agent. Once you’ve set up your animated simulation, you’ll implement motion capture data to train a neural network through reinforcement learning. The expected input here is the configuration of the legs and arms at different time points, and the output includes the spatial and temporal distinctions of these physical locations or movements.
- Deep Learning Skills Practiced: Robotic Process Automation, Core Data Science Skills, Computer Science Fundamentals and Data Structure, Mathematics for Machine Learning, Natural Language Processing
For this project, you’ll be able to empower cross-functional teams to monitor, deploy and optimize ML/Deep Learning models efficiently and quickly. IBM Watson allows data scientists to share GPUs and run more jobs, training, and predictive models across multiple systems.
- Deep Learning Skills Practiced: Robotic Process Automation, Computer Vision, Convolutional Neural Network, Core Data Science Skills, Natural Language Processing
This project creates a deep learning model that generates captions for an image. This generator uses natural language processing techniques and computer vision to determine and analyze the context of an image then explain them accurately using natural human languages like English, Spanish, etc. These open-source data sets will test your knowledge of LTSM, CNN, and other deep learning techniques.
Deep Learning Starter Project Templates
Project templates are another excellent way to dive into deep learning and other complex computer science projects. Below are suggested templates to assist you in your deep learning projects.
- PyTorch Deep Learning Template. The main features of this template are that it can divide each logic piece into a different Python submodule. It doesn’t use a training loop, which means it is ready to go without any additional conditioning. It can also show a summary of your models through torch summary.
- Tensorflow Project Template. This template is a mixture of simplicity, good OOP design, and best practices for folder structure. This template helps you get into the main project immediately to focus on crucial areas like Training, Model, etc.
- The Kaggle Machine Learning Project Template. In this template, the process is already rolled out for you. After that, you’ll just have to prepare the problem, summarize your data, organize the data, evaluate algorithms, improve accuracy, and finalize the model.
- Time Series Project Template. Through this template, you’re able to apply algorithms like SARIMA, SARMAX, ARIMA, Simple Exponential Smoothing, and Holt-Winters. The only thing to do is plug in the data and give the input values.
- Fast-API. This template can be used if part of your goal is to use a quick API for the API framework.
Next Steps: Start Organizing Your Deep Learning Portfolio
It’s common to obsess over what goes into your portfolio or how you can present your resume to your future employer. As a result, you might’ve excluded projects that you feel aren’t relevant to your career path or goals or maybe not good enough in your perspective.
However, employers and clients are more interested in seeing how your skills developed over past projects and experiences. Therefore, it’s best to include a sample of past collaborations, projects, and networks that you may have built. We’ve compiled some of the top tips to organize your portfolio in a way that puts your best foot forward.
Determine How to Present Your Portfolio
Whether you use GitHub, WordPress, or another personal website, you need to decide which platform is best to display your portfolio. Choose whatever mode you feel will have the space, freedom, and customization necessary to show your skills properly. You’ll have more flexibility in customizing your portfolio if you create an entire web page which you can also make visually pleasing to catch the attention of clients or employers.
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Determine Which Projects Highlight Your Skills the Most
Your portfolio represents your skills, creativity, experience, and personality, which is why it’s essential to be intentional with the type of projects you want to include. When you are more directed with what kind of project you want to showcase, this sends clients and employers a clear message of how working with you can provide them a great advantage.
Determine How You’ll Organize Your Portfolio
An organized portfolio means the structure and formatting are the same for every project. Identify what kind of details you should put as you introduce the project. Will it include the goals of the project and the strategies you used, or will it include testimony from a previous client? Whatever formatting you’ve decided, make sure that all projects have it.
Deep Learning Projects FAQ
The difficulty of completing a deep learning project depends on your skills, availability, and commitment. If you are confident that you have the foundational skills required to use deep learning methods, projects would be easier to finish.
On average, deep learning projects take a month to be successfully deployed. However, this depends on the project’s complexity and goals.
The best deep learning project for beginners could be a face detection system or a visual tracking system. You can also check the list above for other suggestions.
You can start a deep learning project by choosing what kind of project fits your skills and expertise. Afterward, articulate your ideas on how to execute them. Be flexible on making some changes along the way, especially if you are still learning about machine learning techniques.
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