Deep Learning vs Neural Learning.
In this current digital age, where Artificial Intelligence is now a talk of the town. The word “Deep Learning" and “Neural Learning" should have come across you. Whether on Facebook Timeline, on a Blog Post, YouTube Video, Ad Banner or even at a coffee shop, you would probably have heard about them. Esp “Deep Learning", I’ll say.
Unfortunately, many confuse these two words as Synonyms. Sure, they both are training techniques for Artificial Intelligence but it is important to note that they are two different training and learning models. The similarities are quite obvious but the differences are there. In this article, I am going to be sharing with you, the 5 differences between Deep Learning and Neural Learning which you probably didn’t know about. But before we proceed, why don’t we take a little bit of our time to understand what Deep Learning and Neural Learning actually is??
Definition:
Deep Learning is a subset of machine learning, which is itself a subset of artificial intelligence (AI). Deep learning algorithms attempt to draw similar conclusions as humans would by continually analyzing data with a given logical structure. To achieve this, deep learning uses a layered structure of algorithms called an Artificial Neural Network (ANN), designed to mimic the human brain’s way of thinking.
These layers of algorithms, or 'nodes', are also known as 'hidden layers'. As the amount of hidden layers increases, the artificial neural network is able to understand and process data in increasingly complex ways, hence the term 'deep' learning. Each layer of nodes learns to recognize different features, with the complexity of features increasing the deeper into the network you go. For example, in image recognition, the first layer might recognize brightness, the next layer shapes, and so on, until the final layer recognizes the overall object.
Deep learning requires large amounts of labeled data to train on and substantial amounts of computational power. However, it's extremely effective at carrying out predictive analytics, such as speech recognition, image recognition, and natural language processing tasks.
Neural Learning, on the other hand, is a broader term that refers to various techniques used in training neural networks. These techniques encompass both shallow and deep learning, depending on the complexity of the network and the task at hand. Shallow neural networks typically have only one or two layers of nodes, while deep networks have three or more.
Neural learning is generally faster and requires less computational power and data than deep learning. It can be effective for simpler tasks and smaller datasets. However, for more complex tasks that require abstract representations of data, deep learning is often more suitable.
Both deep learning and neural learning involve using artificial neural networks to process information. The main difference lies in the complexity of the tasks they are most suited for and the amount of data and computational power they require.
Differences between Deep Learning and Neural Learning
1. Definition
Deep Learning: It is a subset of machine learning, focusing on the use of deep neural networks. These networks mimic the human brain’s functionality, learning from large amounts of data.
Neural Learning: This is a broader term encompassing various techniques used in neural networks. It refers to the learning method based on the neural network structure.
2. Architecture
Deep Learning: Deep Learning involves a deep architecture with multiple hidden layers. These layers allow for more complex modeling and abstraction of data.
Neural Learning: Neural Learning can be both shallow and deep, depending on the application’s needs. The design can be tweaked to suit the requirements of the task.
3. Data Processing
Deep Learning: Deep Learning models learn hierarchical representation through a layered structure. Each layer of nodes extracts and processes a different level of features.
Neural Learning: The focus of Neural Learning is on using neural networks for processing information, regardless of the complexity of the network.
4. Task Suitability
Deep Learning: Deep Learning is well-suited for complex tasks. However, it requires a substantial amount of labeled data to perform effectively.
Neural Learning: Neural Learning is adaptable to simpler tasks and proves to be effective even with smaller datasets.
5. Training Time
Deep Learning: Deep Learning models require a longer training time, especially for complex networks. This is due to their intricate structure and the large amounts of data they process.
Neural Learning: Neural Learning offers generally quicker training, making it suitable for real-time applications.
Conclusion
Deep Learning and Neural Learning, both integral parts of machine learning, have their unique strengths. While Deep Learning excels at handling complex tasks with large datasets, Neural Learning shines with simpler tasks and smaller datasets, providing faster results.
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