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Deep Learning and Neural Networks: The Future of Machine Learning

Deep Learning and Neural Networks: The Future of Machine Learning

A visual representation of neural networks with interconnected nodes and flowing information, representing the computational processes and learning capabilities of AI systems

Artificial intelligence (AI) has long been inspired by the human mind. The first neural network, called the Mark I Perceptron, was built in 1958 by Frank Rosenblatt and could recognize simple numbers. After that, progress was slow, occurring only in fits and starts...until recently.1

Although scientists grasped the potential for machine learning long ago, they were missing some key components to implement it. Luckily, recent developments in data science have changed that and greatly expanded the capabilities of machine learning, including less human intervention. The increasing availability of big data, improved algorithms, and greater computational power have set the stage for technological leaps and driven the explosion in machine learning, which has in turn led to advancements in deep learning.2

Today, deep learning is one of the fastest-growing fields in data science, fueling over 18,000 startups in the U.S.3 Although it's often used interchangeably with classic machine learning algorithms, deep learning has its own specific features and benefits. Deep learning eliminates the need for multiple systems with multiple rounds of review; it runs on multiple processing layers, can operate independently, completes complex tasks instantly, has faster training, and is significantly less time consuming.

To help you learn more, this post will explain deep learning, its applications, and possible future directions.

What Is Deep Learning?

Deep learning is a branch of machine learning, which is a type of artificial intelligence. Machine learning uses artificial neural networks to carry out a simplified mimicry of how humans learn from data. Unlike traditional machine learning algorithms, which are linear, deep learning algorithms have three or more layers stacked in a hierarchy of increasing complexity and abstraction. These complex algorithms allow greater inputs for greater predictive accuracy.4

There are several types of deep learning, including supervised learning, unsupervised learning, and reinforcement learning.

Supervised Learning

In supervised learning, the algorithms are trained using massive amounts of data that comes with predefined labels, effectively providing the solutions the model should aim to produce. This training process is geared toward tasks such as classification, where the goal is to categorize inputs into distinct classes, and regression, where the algorithm predicts a value. As the ML model trains, it receives feedback on its accuracy—whether its predictions match the actual labels—and uses it to fine-tune the parameters or weights of the model.

To measure the effectiveness of a supervised learning model, it is evaluated against a separate test dataset—containing unseen historical data with known labels—so that it can reliably make predictions on new, unobserved data. Spam detectors are trained with supervised learning.5

Unsupervised Learning

An unsupervised deep learning algorithm operates differently from its supervised counterparts as it is designed to work with unlabeled, raw data automatically. They work to uncover the hidden structure within the input data, without the guidance of predefined labels. These algorithms are good at identifying natural groupings or clusters in the data, which is useful in grouping similar items. They can also be used to find associations and correlations in large datasets, helping uncover rules governing the data.

In this self-directed learning environment, the algorithms don’t receive explicit feedback on their performance. Instead, they are programmed to detect patterns and relationships independently in their deep neural networks. Unsupervised learning is often used in natural language processing applications and genetic research.6

Reinforcement Learning

Reinforcement learning models learn from the consequences of actions taken in an environment. The program makes decisions, receives feedback from the environment through rewards or punishments, and then adjusts its actions accordingly. The learning is driven by the goal of maximizing cumulative rewards over time, not by matching input data to known outputs or discovering hidden structures in raw data.

Reinforcement learning is often thought of as learning from interaction with an environment, as opposed to learning from static data. It is neither supervised nor unsupervised but shares some similarities with both. It is similar to supervised learning in that the model uses feedback to learn, but this feedback is in terms of reward signals, not explicit correct answers. It is similar to unsupervised learning in that the model can explore and discover strategies for acting in an environment, but it's guided by the objective of maximizing reward, not just finding patterns. Self driving cars and some virtual assistants are trained using reinforcement learning.7

Future Trends in Deep Learning

Deep learning is expanding into almost every field, and its applications are everywhere. No one doubts the influence deep learning will have on society now and in the future. However, experts disagree over whether its impact will be positive or negative. The White House’s recent executive order captures the fears and possibilities associated with current and future AI applications. This executive order establishes new standards for safety and security using artificial intelligence, including safety tests, best practices, fraud detection, regulatory bodies, and transparency mandates.8

Going forward, it’s likely that technology will become more regulated as things like AI adoption grow. The European Union (EU) is expected to pass the first major AI legislation this year, and the US may follow suit.9

Some additional emerging trends in deep learning include:10

Transfer Learning and Few-Shot Learning

Transfer learning, where a model trained on one task is used for another related task, has become more popular. Few-shot learning, where models are designed to learn from a very small amount of labeled data, is an emerging area of research that could reduce the need for extensive data preprocessing and large training data sets.

Attention Mechanisms and Transformers

Attention mechanisms—methods that mimic the cognitive act of paying attention—have optimized natural language processing. This trend will likely continue and expand into other areas that the human brain can, such as computer vision, object recognition, and audio processing.

Interpretable AI

There is a growing need for models to train computer systems that are not only effective but also interpretable and explainable. This trend is driven by the need for trust and understanding in AI systems, especially in critical applications like healthcare and autonomous driving.

Edge AI

With the increased computational power of devices, there is a trend toward bringing AI to the edge, meaning that deep learning models are more frequently being used on mobile devices, IoT devices, and on-premises servers. This can reduce latency and address privacy concerns by processing data locally.

Pursue a Career Using Machine Learning

The demand for machine learning specialists is expected to grow 40% between 2023 and 2027.11 Companies looking for professionals with experience in machine learning, deep learning, neural networks, and related skills will only increase.

Contact one of our admissions outreach advisors today to learn more about the online degree programs at New York Tech and the online experience.

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