An introduction to deep learning
Published: 2023-06-12 22:22:32

Among the many subfields of Artificial Intelligence(AI) , machine learning is the field that has produced the most successful applications. Within machine learning, the biggest advance is “deep learning”—so much so that the terms “AI,” “machine learning,” and “deep learning” are sometimes used interchangeably”. 
Deep learning has emerged has a groundbreaking subfield of AI that has transformed various sectors:
Computer vision
Natural Language Processing(NLP)
Speech recognition 
This article serves as an introduction to deep learning, providing and overview of its fundamental concepts, architectures and applications. It won’t delve into other subfields of AI. 
By gaining a solid understanding of the basics of deep learning, readers will discover the remarkable capabilities and potential of this technology.
This article is aimed at beginners in the field of Artificial Intelligence. 

What is deep learning?
Deep learning is a subfield of machine learning that focuses on training artificial neural networks with multiple layers to learn and extract high-level representations from complex data.
Deep learning is inspired by the mechanism of the  neurons in the human brain. Data is fed into the input layer of the neural network, and a result emerges from the output layer of the network. In between the input and output layers may be up to thousands of other layers, hence the name “deep” learning.

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Neural networks 
According to Wikipedia, Artificial neural networks, usually simply called neural networks or neural nets, are computing systems inspired by the biological neural networks that constitute animal brains.
Neural networks consist of interconnected artificial neurons, referred to as nodes or units, organized into layers. These layers typically include an input layer, one or more hidden layers, and an output layer. Each node within a layer receives input signals, performs computations, and passes the output to the next layer. Through the process of training, neural networks adjust the weights and biases associated with each node, enabling them to capture intricate patterns and relationships within the data.

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Deep learning architectures
Deep learning architectures harness the power of neural networks by increasing their depth, allowing them to learn more complex and abstract representations. The following are the two popular deep learning architectures:
Convolutional Neural Networks (CNNs) 
Recurrent Neural Networks (RNNs).

Convolutional Neural Network (CNN)
Utilizing deep learning on a standard neural network is a challenge in tasks involving image data. This is because an image has tens of millions of pixels, and teaching deep learning to find subtle hints and features from a massive number of images is painstaking. 
The observation of how humans see and identify images inspired the invention of CNNs. The key components of a CNN include:
convolutional layers
pooling layers
fully connected layers
Convolutional layers apply filters to the input data, extracting hierarchical representations. 
Pooling layers downsample the feature maps, reducing computational complexity and enhancing translation invariance. 
Fully connected layers combine the extracted features and make predictions based on the learned representations.

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Recurrent Neural Networks (RNN)
RNNs are designed to process sequential data with temporal dependencies, making them well-suited for tasks like natural language processing and speech recognition. 
The key feature of RNNs is their ability to maintain an internal memory, enabling them to capture context and sequential information. The recurrent connections within RNNs allow them to process sequences of varying lengths. 
Popular variants of RNNs, such as Long Short-Term Memory (LSTM) and Gated Recurrent Units (GRUs), address the vanishing gradient problem and capture long-term dependencies.

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The following are some applications of deep learning:
Self-driving cars
News Aggregation and Fraud News Detection
Natural Language Processing
Virtual assistants 
Entertainment 
Computer vision
Healthcare, etc.