If you are interested in creating learning algorithms that can inform modern artificial intelligence, studying neural networks will put you on the cutting edge of these developments. Consider taking a machine learning, deep learning, or neural network course to get into the exciting field of artificial intelligence.
Read on to uncover more about neural networks and their modern applications. We will also discuss how a neural network works, how to learn it step-by-step, and the best courses for in-person and online study.
What Is a Neural Network?
An artificial neural network functions in much the same way as the neural network in an animal or human brain. It is a collection of connected nodes that make up artificial neurons. The first layer of nodes holds a number that corresponds to a pixel in an image. The last layer predicts what the machine believes the original image might be.
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This trial and error process begins with the input layer that you want the machine to learn and ends with what the machine “thinks” is the correct answer in the output layer. Neural networks are a valuable tool for neuroscientific research, machine learning algorithms, and computer science.
There are two main types of neural networks. Convolutional neural networks, or CNN, are used for photo tagging, and recurrent neural networks, or RNN, are used in speech recognition or machine language translations.
Neural Network Applications
So, what are neural networks used for? They are used to allow a machine to learn from many data sources in ways that humans can’t. Below are just some of the applications of neural networks today.
Restoring Image Colors
Instead of hiring a colorist to spend months restoring a photo or a black and white video, neural networks can do it instantly. The neural network can recognize color patterns in our world, like the blue sky and white clouds. The more information the network retains, the better its images become.
Sony used neural networks to generate a recording of “Ode to Joy” as it was originally intended, as well as a Brazillian guitar version. The idea of machines making music seems more like AI than deep learning, but it is neural networks that teach the machine the notes and sequences.
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Helping Endangered Species
A neural network was created to identify endangered whales via images, which can be applied to machine learning to help save the endangered species.
Reading Human Handwriting
In this case, the input is a handwritten letter or number, which the neural network will learn from to be able to predict what the image says. Although this requires a lot of trial and error, it is incredible that a computer can be trained to read variations of human handwriting.
Replicating Human Speech
Human speech is arguably the most spine-chilling application of a neural network. Much like a child learning to talk, a machine can learn to form words and sentences, starting with gibberish at first, and slowly improving using natural language processing.
As you may know, the tags used on Facebook photos are created using neural networks. These neural networks are also being used to reconstruct pixelated images into a recognizable form, which has massive potential for solving crimes.
Google Translate now has an algorithm that can translate a word or phrase into another language based only on a photo of the word.
Developing Self-Driving Cars
Self-driving cars are one of the most well-known applications of machine learning. In the future, we might all be able to sit back, relax, and watch the world go by as our cars take us where we need to go.
How Neural Networks Function
Below is an example of a straightforward neural network and how it can be used to decipher different dog breeds based on photos.
- Input layer. This is the input dimensions. Using dog breeds as an example, your input dimensions may be the fur color and weight of a dog. These dimensions are added along with images of each dog breed.
- Activation function. Activation functions are mathematical equations that determine whether a neuron should be activated or not. They are attached to each neuron and are activated based on whether the neuron input is relevant for the dog prediction.
- Hidden layers. The hidden layers are located in the middle and represent bias. This is where the neural network “calculates” its prediction for which dog breed the photo could contain, taking into account all of the numerical data.
- Output layer. This is the computer’s vision of what it thinks the image represents. It will provide a percentage chance of whether the image is of a poodle, dalmatian, or another dog breed.
- Backpropagation. After the output has been determined, it’s time to see if the answer is correct. If the machine guessed wrong, it must go back to calculate the error and adjust the biases.
- Gradient descent. Gradient descent is part of this backpropagation. In machine learning, the gradient descent is used to update the parameters of the model using vectors.
Learning Neural Networks: How Long Does It Take?
How long it will take to learn about neural networks depends on what you want to achieve from your studies and your background knowledge of programming and algorithms.
It can take years to become proficient in programming and machine learning. However, if you already have these skills, it could take just weeks or months to understand neural networks.
If your overall goal is to become a computer engineer or software developer, this may require more detailed study in the form of a college degree or professional certification.
How to Learn Neural Networks: Step-by-Step
Below are four steps you can take to learn neural networks, without factoring in college studies. These steps will give you the foundational knowledge you need to start machine learning projects as a beginner.
- Programming languages. The first step is to work on building your programming skills. Languages like Python, Python algorithms, R, Java, and C++ are essential for building neural networks and getting a job in machine learning or AI.
- Linear algebra, probability, and statistics. Understanding these mathematical skills is also crucial for the work ahead. Learning Hidden Markov models, Naive Bayes, and Gaussian Mixture models will further enhance your resume.
- Big data. In neural networks, you may be working with large volumes of data, so understanding data is also an important step. Examples of software you can look into include Spark, Cassandra, and Hadoop.
- Algorithms and frameworks. Learning deep learning algorithms and machine learning algorithms is the final step. The theories and mechanics of algorithms will help you build neural networks.
The Best Neural Networks Courses and Training
There are plenty of exciting courses out there to help advance your learning about neural networks. Whether you are a beginner seeking free programming lessons or a programmer who wants to take an advanced machine learning or deep learning course, there is something for everyone.
Best In-Person Neural Network Courses
Below are two in-person programs that will prepare you for a career in a neural network field such as computer engineering. These are degree programs, so you should be confident that you want to major in computer engineering or computer science before starting.
Massachusetts Institute of Technology (MIT)
- Bachelor of Science in Computer Science & Engineering
- Professional Certificate Program in Machine Learning & Artificial Intelligence
- Where: Cambridge, MA
- Time: 16 days to 4 years
In this exciting MIT program and professional certificate, students will learn about computer science, engineering, and AI. This includes coding languages, robotics, algorithms, software development, and computer architecture. This is a great course if you plan to go into computer engineering or machine learning.
- Introduction to Neural Networks
- Where: West Lafayette, IN
- Time: 3 credits
In this Purdue course, students will learn about information processing with neural networks, algorithms, and network models and architecture. The course also covers image processing, optimization, pattern recognition, simulation, system identification, nonlinear prediction, and communications.
Best Online Neural Network Courses
Below are some of the best paid and free online courses, covering both deep learning and neural networks.
Deep Learning AI
- Neural Networks and Deep Learning
- Where: Online
- Time: 4 months
- Prerequisites: Intermediate level
- Price: Varies (7 days free)
In this course offered on Coursera, you will study the principles of deep learning and how to build neural networks, and will also start your own machine learning projects. The course will cover convolutional networks, LSTM, RNNs, Adam, Dropout, Xavier, and BatchNorm.
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Students will work on case studies in health care, sign language, autonomous driving, music generation, and natural language processing.
- Advance Your Skills in Deep Learning & Neural Networks
- Where: Online
- Time: 16 hours
- Prerequisites: None
- Price: Varies (First month free)
Best Free Online Neural Network Courses
Here is a look at just some of the best free online courses for programming, mathematics, machine learning, and computer science. These courses all contain information vital to learning neural networks.
- Machine Learning Crash Course
- Where: Online
- Time: 15 hours
- Prerequisites: None
- Price: Free
In this Google course, you will learn to apply fundamental machine learning concepts. Students will get experience by using Kaggle Competitions and can explore a full library of training resources.
- Where: Online
- Time: Varies
- Prerequisites: None
- Price: Free
Khan Academy is a non-profit organization that shares knowledge and skills for free. Students can use the website to gain new computer skills alongside their academic studies or take the courses as a professional to brush up on neural network-related topics including programming, linear algebra, and computer science.
- Introduction to Machine Learning for Coders
- Where: Online
- Time: 24 lessons
- Prerequisites: Coding experience, high school mathematics
- Price: Free
This course was recorded by the University of San Francisco as part of the school’s master’s program in data science. To fully understand this course, you will need at least one year of coding experience and high school-level math knowledge.
Best Neural Network Books
Here are a few texts you may want to browse as you expand your knowledge of neural networks. In them, you will discover how to make your own neural networks and build intelligent systems.
Make Your Own Neural Network, Tariq Rashid
This guide covers all aspects of how neural networks work. Readers only need an understanding of high school level mathematics to grasp this book, as it includes an introduction to calculus. Rashid’s goal with this book is to make neural networks accessible to everyone.
This book also covers coding in Python, and how to make your own functioning neural network.
Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems, Aurélien Géron
From Geron’s book, you will learn how to build intelligent machine learning systems. It also covers neural nets, deep reinforcement learning, convolutional nets, recurrent nets, deep neural nets, and how to use Scikit-Learn.
Deep Learning, Ian Goodfellow
Goodfellow’s text covers concepts related to neural networks in linear algebra, probability theory and information theory, numerical computation, and machine learning. It also goes into deep feedforward networks, optimization algorithms, regularization, convolutional networks, sequence modeling, and practical methodology.
Readers will also learn more about natural language processing, computer vision, speech recognition, and online recommendation systems.
Best Online Neural Network Resources
The Internet has endless resources for learning more about neural networks. Let’s look at a few of these resources that are meant for students at all levels, including articles, tutorials, and software.
Deep Learning AI includes a page on how to initialize neural network parameters. Initialization can have a significant impact on convergence as you train your deep neural networks.
If you are looking for a more in-depth research paper, Jurgen Schmidhuber gives an excellent overview of neural networks in this research paper from 2014.
Should You Study Neural Networks?
If you want to teach a computer how to compose music, play a game of chess, or begin to speak, then learning neural networks is the way to go. Advancements in machine learning and AI improve all the time, and you could be a part of the tech evolution.
If you’re still looking for more information on how to learn coding or big data, which are both useful in neural networks, head to our blog to check out more articles. We hope you have found the perfect artificial neural network course to begin your own neural network learning.
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