Neural Networks Overview:
Neural networks are a subset of ML algorithms modeled loosely after the human brain. They are designed to recognize patterns and interpret sensory data through a kind of machine perception, labeling, or raw input clustering. The patterns they recognize are numerical, contained in vectors into which all real-world data, be it images, sound, text, or time series, must be translated.

Key Components of Neural Networks:

• Neurons (Nodes): Basic units of neural networks that receive input and pass on their signal to the next layer if a certain threshold is reached.
• Layers: Neural networks consist of layers:
  • Input Layer: Receives the initial data.
  • Hidden Layers: Intermediate layers that process inputs received from the previous layer using weights and biases.
  • Output Layer: Produces the final output.
• Weights and Biases: Parameters within neural network models that transform input data within the network’s internal layers.
• Activation Functions: Functions like ReLU (Rectified Linear Unit), Sigmoid, or Tanh that help decide whether a neuron should be activated by calculating weighted sum and further adding bias with it.

Types of Neural Networks:

• Feedforward Neural Networks (FNN): The simplest type where connections between the nodes do not form a cycle.
• Convolutional Neural Network (CNN): Particularly well-suited for image recognition tasks; they use convolutional layers that apply convolutional filters to detect features such as edges in images.
• Recurrent Neural Networks (RNN): Designed for sequence prediction problems; they have connections forming cycles along with time steps which allow them to maintain information about previous states.
• Long Short-Term Memory Networks (LSTM): A special kind of RNN capable of learning long-term dependencies; particularly useful for tasks involving sequences such as time-series analysis or natural language processing.

Training Neural Networks:
Training involves adjusting weights and biases within the network to minimize the difference between actual output and predicted output. This is typically done using backpropagation combined with an optimization algorithm like gradient descent.

Challenges with Neural Networks:

• Require large amounts of training data.
• Computationally intensive.
• Prone to overfitting on training data if not properly regularized.
• Interpretability can be difficult due to their “black box” nature.