Are you curious about how artificial intelligence algorithms can be used to solve complex problems? Look no further than simulated annealing! This powerful optimization technique is inspired by the physical process of heating and cooling metals and has been adapted for use in AI systems. In this blog post, we’ll dive into what simulated annealing…
Artificial intelligence has come a long way over the years. From the early days of “smart” appliances and digital assistants, we’ve seen AI grow more complex and sophisticated by the day. But what exactly is artificial intelligence, and why are neural networks so important in its development? In this blog post, we will explore these…
Neural networks are the brainchild of artificial intelligence (AI). They are a type of machine learning that can be used to make predictions based on data. In other words, they can learn and understand complex patterns in data without being explicitly programmed. This makes them incredibly useful for tasks such as image recognition, speech recognition,…
This release will deal with the implementation of the neural network using Tensorflow. Using the Titanic Dataset from kaggle Notebook to check and run code: https://www.kaggle.com/code/tanavbajaj/neural-network-basic/notebook Let’s start by importing the libraries import numpy as np import pandas as pd import tensorflow as tf Read the Data data= pd.read_csv(‘../input/titanic/train.csv’) Select the required data frames…
Activation functions get their names from you using it in neural networks as they decide whether a particular neuron should activate or not. In the context of this release, we will discuss the sigmoid function in detail as it is used in the logistic regression algorithm for binary classification. Sigmoid Activation function Sigmoid function’s main…
Lets us try to implement logistic regression from scratch in python. Recommended to be read after the Neural Networks release. Importing necessary libraries The dataset we will be using is Pima-Indians-diabetes-database Whose objective is to predict whether or not a patient has diabetes diagnostically. y = data.Outcome.values x_data = data.drop([“Outcome”],axis=1) After this, the data…
