What is Deep Learning

Deep learning, a subset of machine learning, involves the development of multi-layered neural networks to model complex patterns and structures in data. By emulating the human brain's structure and function, deep learning algorithms can automatically learn features from data, eliminating the need for manual feature extraction.

Deep Learning Spreads

Modeling a Single Neuron

A neuron can be depicted as follows:

Artificial Neural Networks And its Intuition

In the neuron model, the concepts of "weights," "bias," and "activation function" are crucial components:

• Weights
  Representing the efficiency of synaptic transmission, weights determine how strongly each neuron influences others.

• Bias
  The neuron's sensitivity, bias adjusts the neuron's excitability.

• Activation function
  A function describing a neuron's rule for processing information.

The output of a neuron is determined by the following equation:

y = f({\sum\limits_{k=1}^{n}{x_k}{w_k} + b})

• y: The output of the neuron.
• f: The activation function, which determines the output of the neuron based on the input and weights.
• \sum\limits_{k=1}^{n}{x_k}{w_k}: The weighted sum of the inputs (x_k) and their corresponding weights (w_k). The summation symbol (\sum) indicates that we sum the product of each input and its corresponding weight across all inputs (k=1 to k=n).
• b: The bias term, which adjusts the neuron's excitability.

In essence, this formula describes the process by which a neuron in an artificial neural network processes the input data. The neuron takes the weighted sum of its inputs, adds the bias term, and then applies the activation function to produce the final output (y). This output can then be passed on to other neurons in subsequent layers of the neural network.

Neural Networks

Neural networks are a collection of interconnected neurons, typically arranged in layers. Each neuron in one layer connects to another neuron in the subsequent layer.

What is a neural network?

Neural networks consist of an input layer, one or more hidden layers, and an output layer:

• Input layer
  Neurons in the input layer initially receive information.

• Hidden layers
  These layers process complex data received by the input layer, transforming it into simpler data that can be handled by the output layer. The number of hidden layers can vary depending on the complexity of the information being handled. However, increasing the number of neurons and hidden layers can also increase the required data, memory, and operations.

• Output layer
  The output layer produces values processed by the activation function and weighted by the input and hidden layers.

In a neural network, each node (neuron) connects to other nodes, with associated weights and thresholds. When a node's output exceeds a specific threshold, the node is activated, and data is sent to the next layer of the network. Otherwise, no data is forwarded.

Applications of Deep Learning

Deep learning, with its ability to model complex patterns, has a vast array of applications across various domains. These applications extend far beyond the realms of image processing, speech recognition, and natural language processing, touching upon a multitude of other industries and sectors. Some examples of deep learning applications include:

Image Processing

• Image classification
  Categorizing images into different classes or labels.
• Object detection
  Identifying and locating objects within images.
• Key point extraction
  Extracting features such as human skeleton detection and posture estimation.
• Image segmentation
  Dividing an image into multiple segments or regions.
• Image synthesis
  Generating new images by combining or altering existing images.
• Style transfer
  Applying the artistic style of one image to another.

Speech Recognition

• Text transcription
  Converting spoken language into written text
• Speaker identification
  Recognizing the identity of a speaker based on their voice characteristics.
• Voice command recognition
  Interpreting and executing spoken commands.
• Language identification
  Detecting the language being spoken in an audio clip.

Natural Language Processing

• Emotion analysis
  Identifying and categorizing emotions expressed in text.
• Sentence summarization
  Generating a concise summary of a given text.
• Machine translation
  Translating text from one language to another.
• Sentiment analysis
  Determining the sentiment or tone of a piece of text (e.g., positive, negative, or neutral).
• Named entity recognition
  Identifying and classifying entities such as people, organizations, and locations within a text.
• Chatbots and conversational AI
  Creating AI systems capable of engaging in human-like conversations.

Healthcare

• Medical image analysis
  Analyzing medical images for diagnostic purposes or to monitor disease progression.
• Drug discovery
  Identifying potential drug candidates by analyzing chemical structures and properties.
• Personalized medicine
  Developing customized treatment plans based on an individual's genetic makeup and other factors.

Finance

• Fraud detection
  Identifying suspicious transactions or activities that may indicate fraud.
• Algorithmic trading
  Automating trading decisions based on market data and predefined strategies.
• Credit scoring
  Assessing the creditworthiness of individuals or businesses.

Autonomous Vehicles

• Environmental perception
  Enabling vehicles to perceive and understand their surroundings through sensor data.
• Path planning
  Determining the optimal route for a vehicle to navigate from one point to another.
• Control and decision-making
  Facilitating real-time decision-making for vehicle control and maneuvering.

References

https://www.ibm.com/cloud/learn/neural-networks
https://semiengineering.com/deep-learning-spreads/
https://laptrinhx.com/artificial-neural-networks-and-its-intuition-2081057101/