Deep Learning and Neural Networks: The Future of Machine Learning

Deep learning is a type of artificial intelligence (AI), specifically a type of machine learning. It uses a web of programs called neural networks, which are designed to make decisions in a way that mimics the human brain.¹ By layering these neural networks, computer scientists seek to simulate complex decision-making.

Traditional machine learning programs may use only one or two computational layers. In contrast, deep learning programs use thousands of layers to train a model.²

The earliest deep learning models were developed in 1965. Subsequent rapid advances in technology prompted some outlandish claims that were far from coming to fruition. These claims turned public sentiment against AI and led to an “AI winter” that began in the 1970s.³

Since then, science made more progress on deep learning in the 1990s and early 2000s. As computer hardware advanced enough to handle more complex programming, it became possible to train much larger programs, and the field began to make rapid advancements in AI.³

This article will explore core concepts and real-world applications in deep learning, as well as the technology's challenges and limitations.

Core Concepts and Key Terminology

Neurons are the heart of deep learning. These mathematical functions turn inputs into weights, add a bias, and pass them along in the program. The weights represent the strength of connections between neurons, and the biases are constant values added to allow the network to model more complex patterns. A collection of neurons is called a layer, and it can occur at different computational stages, including input and output layers and hidden layers. The network can then learn complex patterns through activation functions.⁴

Backpropogation is also used to train and optimize deep learning programs. The term stands for “backward propagation of error” and shows how changes to the weights or biases of a neural network impact its accuracy.⁵ By adjusting the internal weights and biases, it enables the network to learn from its mistakes in a way that reduces prediction errors over time.

Popular Architectures and Frameworks

Two common deep learning artificial neural networks include convolutional neural networks (CNNs) and recurrent neural networks (RNNs). CNNs work best when processing grid-like data, such as images. They use layers that first transform inputs before passing them to the next layer. They also capture patterns and are widely used in image classification, object detection, facial recognition, and medical imaging.⁶

RNNs are more geared towards sequential or temporal data, such as text, time series, or speech. These networks use loops that allow information to be retained across time steps. RNNs are used in language models, speech recognition, and language translation.⁶

Many different programming languages have libraries that support deep learning. Some of the most popular ones include TensorFlow (by Google), Keras (high-level API, often used with TensorFlow), PyTorch (by Meta/Facebook), and Theano.⁷

Real-World Deep Learning Applications

Deep learning has applications across many different industries and use cases. With computer vision, deep learning programs can recognize license plates or identify weapons in public areas. They can also be used in natural language processing, which allows models to process and produce human language in chatbots and other applications.⁸

Also, computer scientists employ deep learning models to enable robots to function autonomously in complex environments. Deep learning networks are used by financial institutions to detect anomalies in transactions that could indicate fraud. After the model collects and analyzes data, it applies algorithms that can indicate fraud.⁸

One of the most common examples of deep learning models is recommendation algorithms. These systems make recommendations based on your past behavior. They can accurately suggest music, books, articles, and social media posts you may be interested in.⁸

Deep learning is a critical component of self-driving cars. It helps them navigate the world, all while detecting and avoiding people and objects on the road.⁸

Challenges and Limitations

Although deep learning has tremendous potential, it also has challenges and limitations that need to be carefully considered. Training deep learning models is tremendously computationally expensive. Programmers have to use a powerful graphics processing unit (GPU) and extensive memory, both of which consume resources such as time, money, and energy. Even after training, deep learning programs can run into problems such as overfitting. This occurs when a model adheres too closely to its training data, thus limiting its usefulness in other applications. Overfitting can cause the model to perform poorly on new data.⁹

Deep learning can also have a lack of interpretability, known as the “black box” problem. This issue occurs when a model and its inner-workings are opaque. When programmers don’t understand how a model makes its predictions, they can struggle to identify errors or innate biases. Moreover, programs that don’t incorporate sufficient domain expertise can have problems delivering accurate and reliable results.⁹

Finally, a major challenge of deep learning is making sure it's used responsibly and without causing harm. Without human oversight, models can contain biases that can lead to poor outcomes for women or people of color. They expose personal data to malicious actors. Not considering all the implications of a deep learning model can lead to more unforeseen consequences.⁹

Get Started With the Future of Machine Learning  

An Online Master’s in Data Science from the New York Institute of Technology can equip you with the knowledge and skills you need to thrive in high-demand, data-driven careers. Our rigorous curriculum will teach you about the cutting-edge tools and technologies you need to navigate this challenging industry, while showing you how to create new systems that will shape the future.

Modern businesses run on data. New York Tech's expert faculty will guide you through the practice-oriented aspects of data science so you’ll be prepared to tackle any situation and help lead today’s most innovative companies. Earn your degree from anywhere, on your own schedule. The flexible online format allows you to balance coursework with your professional and personal commitments. For those seeking deeper expertise, an optional thesis track offers the opportunity to develop specialized skills through focused research and real-world experience.

Contact one of our admissions outreach advisors today to learn more.