8 results ·
● Live web index
E
eajournals.org
research
https://eajournals.org/wp-content/uploads/sites/21/2025/05/The-Rise-of-Deep-L…
Neural networks, the European Journal of Computer Science and Information Technology,13(17),88-98, 2025 Print ISSN: 2054-0957 (Print) Online ISSN: 2054-0965 (Online) Website: https://www.eajournals.org/ Publication of the European Centre for Research Training and Development -UK 89 cornerstone of deep learning, have shown exceptional performance in tasks such as image and speech recognition, natural language processing, and autonomous decision-making. European Journal of Computer Science and Information Technology,13(17),88-98, 2025 Print ISSN: 2054-0957 (Print) Online ISSN: 2054-0965 (Online) Website: https://www.eajournals.org/ Publication of the European Centre for Research Training and Development -UK 94 Reinforcement Learning The integration of deep learning with reinforcement learning has led to significant breakthroughs in AI capabilities: Deep Reinforcement Learning: Researchers have achieved remarkable results in complex decision-making tasks by combining deep neural networks with reinforcement learning. Fig. 2: Quantitative Impacts of Deep Learning Advancements in AI Research [3, 6] European Journal of Computer Science and Information Technology,13(17),88-98, 2025 Print ISSN: 2054-0957 (Print) Online ISSN: 2054-0965 (Online) Website: https://www.eajournals.org/ Publication of the European Centre for Research Training and Development -UK 96 Future Prospects As computational resources continue to expand and datasets grow larger, the potential for deep learning and neural networks in AI is boundless.
P
pmc.ncbi.nlm.nih.gov
official
https://pmc.ncbi.nlm.nih.gov/articles/PMC8372231/
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medium.com
article
https://medium.com/@mustaphaliaichi/neural-networks-and-deep-learning-a-compr…
# Neural Networks and Deep Learning: A Comprehensive Introduction | by Mustaphaliaichi | Medium. Within this vast field, Neural Networks and Deep Learning have emerged as transformative technologies, powering breakthroughs in areas ranging from image recognition to natural language processing. In the following sections, we’ll break down complex ideas into digestible parts, guiding you from the basics of artificial neurons to the intricacies of implementing and fine-tuning neural networks using Keras, a popular deep learning library. This simple model forms the building block of artificial neural networks, allowing them to learn complex patterns and relationships in data. Understanding this transition from biological to artificial neurons provides a foundation for grasping how neural networks process information and learn from data. From the biological inspiration of artificial neurons to the practical implementation of multi-layer perceptrons using Keras, and finally to the art of fine-tuning hyperparameters, we’ve explored the key elements that make neural networks such a transformative force in the field of artificial intelligence.
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str.llnl.gov
official
https://str.llnl.gov/past-issues/june-2016/deep-neural-networks-bring-pattern…
Deep learning algorithms are now being used to train a new generation of artificial neural networks (ANNs) that potentially offer game-changing performance.
I
ibm.com
article
https://www.ibm.com/think/topics/deep-learning
Unlike the explicitly defined mathematical logic of traditional [machine learning algorithms](https://www.ibm.com/think/topics/machine-learning-algorithms), the artificial neural networks of deep learning models comprise many interconnected layers of “neurons” that each perform a mathematical operation. By using machine learning to adjust the strength of the connections between individual neurons in adjacent layers—in other words, the varying [model *weights*](https://www.ibm.com/think/topics/model-parameters) and *biases—*the network can be optimized to yield more accurate outputs.While neural networks and deep learning have become inextricably associated with one another, they are not strictly synonymous: “deep learning” refers to the training of models with at least 4 layers (though modern neural network architectures are often much “deeper” than that). Self-supervised learning has since emerged as a prominent mode of training neural networks, particularly for the [foundation models](https://www.ibm.com/think/topics/foundation-models) underpinning generative AI. For instance, each output node of a deep [classification model](https://www.ibm.com/think/topics/classification-machine-learning) might perform a [*softmax* function](https://docs.pytorch.org/docs/stable/generated/torch.nn.Softmax.html) that essentially takes a numerical input and scales it to a probability, between 0–1, that the input belong a potential classification category.
A
aws.amazon.com
article
https://aws.amazon.com/compare/the-difference-between-deep-learning-and-neura…
# What’s the Difference Between Deep Learning and Neural Networks? ## What’s the difference between deep learning and neural networks? A neural network is the underlying technology in deep learning. Thus, artificial neural networks are the core of a deep learning system. The terms *deep learning* and *neural networks* are used interchangeably because all deep learning systems are made of neural networks. There are several different types of neural network technology, and all may not be used in deep learning systems. Next are some key differences between feedforward neural networks and deep learning systems. There are two main types of deep learning systems with differing architectures—convolutional neural networks (CNNs) and recurrent neural networks (RNNs). The number of parameters in a simple neural network is relatively low compared to deep learning systems. In contrast, deep learning algorithms are more complicated than simple neural networks as they involve more layers of nodes. | | **Deep learning systems** | **Simple neural networks** |.
O
online.nyit.edu
research
https://online.nyit.edu/blog/deep-learning-and-neural-networks
[Skip to main content](https://online.nyit.edu/blog/deep-learning-and-neural-networks#main-content). * [Curriculum](https://online.nyit.edu/ms-data-science/curriculum). * [Careers](https://online.nyit.edu/ms-data-science/careers). * [Study at New York Tech](https://online.nyit.edu/ms-data-science/new-york). * [Apply Now](https://online.nyit.edu/blog/deep-learning-and-neural-networks#apply-now). [](https://online.nyit.edu/). * [Curriculum](https://online.nyit.edu/ms-data-science/curriculum). * [Careers](https://online.nyit.edu/ms-data-science/careers). * [Study at New York Tech](https://online.nyit.edu/ms-data-science/new-york). * [Apply Now](https://online.nyit.edu/blog/deep-learning-and-neural-networks#apply-now). [Home](https://online.nyit.edu/)[Online Degrees Blog at New York Tech](https://online.nyit.edu/blog)Deep Learning and Neural Networks: The Future of Machine Learning. . In contrast, deep learning programs use thousands of layers to train a model.2. An [Online Master’s in Data Science](https://online.nyit.edu/ms-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. 2. Retrieved on May 9, 2025, from [ibm.com/think/topics/deep-learning](https://www.ibm.com/think/topics/deep-learning). 8. Retrieved on May 9, 2025, from [neurond.com/blog/10-applications-of-deep-learning-in-artificial-intelligence](https://www.neurond.com/blog/10-applications-of-deep-learning-in-artificial-intelligence). New York Institute of Technology has engaged [Everspring](https://online.nyit.edu/privacy-policy), a leading provider of education and technology services, to support select aspects of program delivery. [](https://online.nyit.edu/).
N
news.mit.edu
research
https://news.mit.edu/2017/explained-neural-networks-deep-learning-0414
Deep learning is in fact a new name for an approach to artificial intelligence called neural networks, which have been going in and out of fashion for more than 70 years. Neural networks were first proposed in 1944 by Warren McCullough and Walter Pitts, two University of Chicago researchers who moved to MIT in 1952 as founding members of what’s sometimes called the first cognitive science department. Neural nets were a major area of research in both neuroscience and computer science until 1969, when, according to computer science lore, they were killed off by the MIT mathematicians Marvin Minsky and Seymour Papert, who a year later would become co-directors of the new MIT Artificial Intelligence Laboratory. By the 1980s, however, researchers had developed algorithms for modifying neural nets’ weights and thresholds that were efficient enough for networks with more than one layer, removing many of the limitations identified by Minsky and Papert.