Neural networks (NN) is a branch of machine learning that uses algorithms to extract meaning from complex datasets that are too convoluted for the human brain. They are used in everyday life through things like Gmail sorting, ridesharing apps, and suggestions on Amazon. As a result, Glassdoor estimates that neural network programmers earn about $79,413 per year.
If you are interested in starting a career in this field, neural net projects and deep learning project ideas will allow you to apply skills as you learn. You can discover more about linear models, the systems-thinking approach, and numeric values. This article covers some of the best neural network projects for beginners and advanced programmers.
5 Skills That Neural Network Projects Can Help You Practice
One of the best ways to learn NN is through practice and repetition to solve real-life problems. You can work on skills like creating a decoding method and encoding method that you can use when training algorithms and complex systems. Below are some skills you should expect to develop while working on neural network projects.
- Programming Languages. Developers use different types of coding languages when building neural networks, and popular languages include Java, Python, and C++. As a result, practicing through projects will allow you to hone your coding skills in these and other programming languages.
- Statistical Models. Neural networks are a branch of deep learning. You need to have a strong knowledge of statistics and various statistical models to allow you to superimpose a machine learning algorithm. Neural network projects will help you to develop your skills in statistics and other related concepts.
- Deep Learning Libraries. Machine libraries such as TensorFlow enable developers to create large-scale neural networks which feature many layers. Other libraries that you should become familiar with include PyBrain, scikit-learn, and PyTorch.
- Mathematics. Mathematics is a fundamental subject used in building neural networks. Linear algebra, multivariate calculus, conditional probabilities, and binomial distributions are a few mathematical skills that you’ll develop as you learn neural networks. You should also be familiar with algebra, calculus, and probability in general.
- Algorithms. Neural networks are made up of sets of algorithms that govern their functions. This means that a solid foundation in algorithms will help you master deep learning. You’ll get to practice and learn algorithms and develop your skills as you work on NN projects.
Best Neural Network Project Ideas for Beginners
It is best to start with simple and easy projects as a new NN developer and save more experimental projects for later. This will help you learn more effectively while building a solid foundation with NN basics. Below are some of the best ideas for beginners to create successful projects and define the dimensions of project success.
Sensor Signal Projects
- Neural Network Skills Practiced: Database management, NLP algorithms
According to BMC Medical Informatics and Decision Making, more than 70 percent of Americans are stressed on a regular basis. You can use deep NN for developing robust, accurate, and non-invasive stress and emotion classification, with the help of multi-tiered neural network architecture.
Convolutional Neural Network Model
- Neural Network Skills Practiced: Binary image classification models, Jupyter Notebook, object detection algorithms
A convolutional neural network (CNN) comprises an input layer, an output layer, and a hidden layer. These networks are usually applied to analyze visual imagery. These models have also been used for natural language processing (NLP) and related concepts such as speech recognition. You can start your own application by taking an MNIST or CIFAR-10 tutorial.
You can use deep learning architecture for a wide range of projects, including image processing for self-driving cars. Autonomous driving applications use CNNs to receive image feedback, which is passed along to a series of output decisions that ultimately result in autonomous driving.
Cryptographic Applications with Artificial Neural Networks
- Neural Network Skills Practiced: Cryptography, encryption, algorithms
Cryptographic algorithms help encrypt and decrypt data from end to end. It involves computational security to avoid data leaks in electronic communications. Such algorithms entail mathematical computations performed on messages and data. In this project, you can try working on cryptography using a sequential machine.
Predict the Next Digit in a Sequence
- Neural Network Skills Practiced: Recurrent neural networks models, standard sequence prediction models
Predicting the next digit in a sequence is one of the very basic projects you can undertake in deep learning. Sequence prediction uses models that identify and analyze historical data to predict the next values in a sequence. There are four basic models for sequence prediction: one-to-one, one-to-many, many-to-one model, and many-to-many.
Build Your Own Neural Net from Scratch
- Neural Network Skills Practiced: Frameworks, libraries, deep learning
This is a great project for beginners as it could be used for laying a solid foundation in neural networks. Consider working on a NN project using a deep learning library or framework. This will help you understand how neural networks actually work and develop your skills in anomaly detection and discriminant analysis.
Best Intermediate Neural Network Project Ideas
The following NN project ideas are best suited for deep learning developers and engineers with advanced knowledge in the subject. Find a project that interests you and matches your skill level to upskill and prepare to become a machine learning engineer or artificial intelligence expert.
Real-Time Face Recognition with Python and OpenCV
- Neural Network Skills Practiced: Haar Cascade Classifier algorithms, Open CV, Python
The aim of this project is to build and implement a real-time facial recognition system. Consider Haar Cascade Classifier, a popular algorithm used for object detection. This project utilizes classifiers that can identify human faces to recognize them as the target object. This type of facial recognition is becoming more common in the tech field.
Project in Python – Breast Cancer Classification with Deep Learning
- Neural Network Skills Practiced: Python
You should have advanced knowledge of Python before committing to this deep learning project. This project aims to build a breast cancer classifier that can accurately classify a histology image as malignant or benign. You can use a medical dataset and build a neural network to analyze its results to classify types of cancer cells.
Text Summarizer with Deep Neural Networks
- Neural Network Skills Practiced: Sentiment analysis, training dataset
Advancements in natural language processing and deep learning have made it possible to build automatic text summarization systems that can shorten long text. The aim of this project is to create your own summarization model in Python. Automatic text summarizers are a common portfolio project that uses statistical methods.
Neural Style Transfer
- Neural Network Skills Practiced: Database management, NN models, generative models
The idea behind this project is to create a model that can create art by using an image, then transfer the same style to a different but targeted image. Such approaches are what made smartphone apps like Prisma popular. If you want to become an app developer, this is a good project to practice.
Chatbot Using Deep Learning
- Neural Network Skills Practiced: Python
Chatbots are intelligent systems and software designed to communicate with humans. Chatbots have become common as they offer customer interaction services on ecommerce websites. This project aims to build a chatbox using deep learning techniques in Python to enhance customer experience with actual values and content alerts.
Advanced Neural Network Project Ideas
Below are some interesting neural net project ideas that you should consider if you’re an expert in this field. These projects will build on the skills practiced in beginner and intermediate projects. They can also help you create an advanced portfolio to use when applying for jobs in the field.
Vision and Control in Autonomous Flying Vehicles
- Neural Network Skills Practiced: Autonomous systems, sensor technology
The autonomous industry has seen massive changes over the past few decades because of major advancements in technology, deep learning, and neural networks. The aim of this project is to develop a vision and control system for autonomous flying vehicles using NNs. Sensor technology can be used with neural networks to measure the optic flow.
- Neural Network Skills Practiced: Python, deep learning
Biometric facial recognition uses complex mathematical algorithms to make these systems safe and secure, which makes this project a good step up from the earlier facial recognition project on this list. Convolutional neural networking allows engineers and developers to extract a wide range of features from images which can also be used for facial recognition.
Driver Drowsiness Detection
- Neural Network Skills Practiced: Python, database management, training datasets
The aim of this project is to develop a drowsiness detection system for people driving for long periods. A significant number of accidents happen because of human error, including fatigue behind the wheel. For this project, you will create an accurate prediction system that will develop a drowsy driver alert system.
Language Translator with RNNs
- Neural Network Skills Practiced: Preprocessing, modeling
This project will help you build a deep neural network designed to function as part of a machine translation pipeline. You can program the pipeline to accept one language and return a different one. For instance, you can have the pipeline accept English and have it return Spanish, similar to apps like Google Translate.
Colorizing Black and White Images
- Neural Network Skills Practiced: Tensorboard, FloydHub, Jupyter Notebook
The aim of this project is to develop a model to colorize historic black and white photos. This process has traditionally been done through Photoshop, but it is possible to construct a colorization neural net. You will need to program the network to recognize shapes and shades of grey to choose the most accurate colors for that image.
Neural Network Starter Project Templates
Starter templates can significantly reduce the time spent working on projects. You can also use starter templates if you are a beginner to help you understand how neural networks work. Below are some NN starter templates you can consider when working on your next project.
- Open Source Machine Learning Framework for Everyone. Tensorflow is an open source end-to-end application used in deep machine learning. It provides stable Python and C++ APIs, and it is an ideal template to learn from as a beginner.
- Flamev2. This is a PyTorch deep learning training template designed to help you train and analyze neural nets. You can use it for many different projects, making it a versatile option for any programmer.
- Deep Learning Project Template for PyTorch. This is a deep learning template based on PyTorch that is typically used to build neural nets. It also supports distributed learning.
- C Attl3. This is a header-only neural network template library written in C++11. It also uses Eigen to facilitate the easy construction of neural nets.
- Machine Learning Template. This template is designed for machine learning projects in Python. This is designed as a template repository.
Next Steps: Start Organizing Your Neural Network Portfolio
One way to increase your marketability in the job industry is by working on related projects and highlighting them as part of your portfolio. Technical portfolios offer a chance to showcase your skills and demonstrate to a potential employer what you are capable of. You should organize your portfolio to highlight specific projects to stand out to employers.
Showcase Your Best Projects
You should highlight the projects that are in line with the job you are looking for, the company, and the industry. You should also keep your portfolio updated with your most recent high-quality projects. Your projects can show off your skills with complex, multi-loop, and nonlinear systems as well as exploratory analysis and machine learning models.
Keep It Simple and Easy to Read
Anyone going through your portfolio should be able to easily access specific projects or sections. Consider creating a navigation bar that stays at the top of the page regardless of where you are in your portfolio. You should make the content visible and consider the technical factors of creating a portfolio.
Include Important Links
A portfolio is a web page you create to showcase your projects and experiences. You should remember to include valid links to your website, LinkedIn, or other professional sites. For instance, you can include a bio section with a valid link that ushers the reader to your personal information and project experiences.
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Neural Network Projects FAQ
You’ll need to have a solid foundation in math, especially in calculus, linear algebra, probability, and statistics. You should also know how to code and have a knack for machine learning algorithms such as linear logistic regression.
The best way to get started in this field is by using online tutorials and resources to help you lay a solid foundation. After learning, try to implement some applications using Python or C. You can also work with TensorFlow. When you feel confident, you can start developing through a starter template.
Some projects might be similar as neural networking is a subcategory of deep learning and machine learning. However, machine learning projects are designed to incorporate algorithms that analyze data to discover meaningful patterns of interest while NN projects use algorithms in machine learning for data modeling.
Projects in most tech-related fields help programmers and developers learn through practice and repetition. They allow you to experience and learn new things which ultimately help you develop your skills. By the end of the project, you’ll have both theoretical and practical skills.
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