Deep Learning

introtodeeplearning.com - Resource for Software Labs

Set of Domains within Artificial Intelligence

Introduction to Deep Learning:

• The inputs are known as a perceptron
• The inputs and weights are multiplied and added by w0 which is 1 (bias)
• The non-linearity activation function is to account for non-linear data points. These non-linear activation functions classify two binary groups or categories.

Classification & Regression Models:

• Classification is a specific task within supervised learning, where the goal is to assign data points to predefined classes or categories.
• Regression is another task within supervised learning, focusing on predicting continuous numerical values. Learning rates are a tool used for optimization to make sure the training data is not being overfitted.
• Supervised learning encompasses both classification and regression, as they involve learning from labeled data.
• Other types of types of learning include unsupervised learning and reinforcement learning

3 steps to get out into deep learning:

• Compute multiplication with corresponding weights & inputs
• Add them all together (Summation)
• Compute a non-linearity activation function
• A dot product is the summation of all inputs & weights