Q-learning is a popular reinforcement learning algorithm used to make decisions in an environment. It enables an agent to learn optimal actions by iteratively updating its Q-values, which represent the expected rewards for taking certain actions in specific states. Here is a step-by-step implementation of Q-learning using Python: Image by Author 1. Import the necessary libraries: import numpy as np import random 2. Define the environment: # Define the environment env = np.array([[ 0 , 0 , 0 , 0 , 0 , 0 , 0 , 0 , 0 , 0 ], [ 0 , 0 , 0 , 0 , 0 , 0 , 0 , 0 , 0 , 0 ], [ 0 , 0 , 0 , 0 , 0 , 0 , 0 , 0 , 0 , 0 ], [ 0 , 0 , 0 , 0 , 0 , 0 , 0 , 0 , 0 , 0 ], [ 0 , 0 , 0 , 0 , 0 , 0 , 0 , 0 , 0 , 0 ], [ 0 , 0 , 0 , 0 , 0 , 0 , 0 , 0 , 0 , 0 ], [ 0 , 0 , 0 , 0 , 0 , 0 , 0 , 0 , 0 , 0 ], [ 0 , 0 , 0 , 0 , 0 , 0 , 0 , 0 , 0 , 0 ], [ 0 , 0 , 0 , 0 , 0 , 0 , 0 , 0 , 0
Deep learning is a branch of machine learning that uses artificial neural networks to perform complex calculations on large datasets. It mimics the structure and function of the human brain and trains machines by learning from examples. Deep learning is widely used by industries that deal with complex problems, such as health care, eCommerce, entertainment, and advertising. This post explores the basic types of artificial neural networks and how they work to enable deep learning algorithms. Defining Neural Networks A neural network is modeled after the human brain and has artificial neurons or nodes. These nodes are arranged in three layers: The input layer The hidden layer(s) The output layer The input layer feeds each node with data as inputs. The node then applies random weights, a bias, and a nonlinear function, also called an activation function, to the inputs. The activation function decides which neuron to activate. How Deep Learning Works! Deep learning is a type of machin