Introduction to Neural Networks and Deep Learning
In this Artificial Intelligence online course, we start with the motive behind using the terms Neural network and look at the individual constituents of a neural network. Installation of and building familiarity with TensorFlow library, appreciate the simplicity of Keras and build a deep neural network model for a classification problem using Keras. We also learn how to tune a Deep Neural Network.
Gradient Descent is an iterative process that finds the minima of a function. It is an optimisation algorithm that finds the parameters or coefficients of a function’s minimum value. However, this function does not always guarantee to find a global minimum and can get stuck at a local minimum. In this module, you will learn everything you need to know about Gradient Descent.
Introduction to Perceptron & Neural Networks
Perceptron is an artificial neuron, or merely a mathematical model of a biological neuron. A Neural Network is a computing system based on the biological neural network that makes up the human brain. In this module, you will learn all the neural networks’ applications and go much deeper into the perceptron.
Normalisation is a technique to change the values of numeric columns in the dataset to a standard scale, without distorting differences in the ranges of values. In Deep Learning, rather than just performing normalisation once in the beginning, you’re doing it all over the network. This is called batch normalisation. The output from the activation function of a layer is normalised and passed as input to the next layer.
Activation and Loss functions
Activation Function is used for defining the output of a neural network from several inputs. Loss Function is a technique for prediction error of neural networks.
Hyper parameter tuning
This module will drive you through all the concepts involved in hyperparameter tuning, an automated model enhancer provided by AI training.
Deep Neural Networks
An Artificial Neural Network (ANN) having several layers between the input and output layers is known as a Deep Neural Network (DNN). You will learn everything about deep neural networks in this module.
Tensor Flow & Keras for Neural Networks & Deep Learning
TensorFlow is created by Google, which is an open-source library for numerical computation and wide-ranging machine learning. Keras is a powerful, open-source API designed to develop and evaluate deep learning models. This module will teach you how to implement TensorFlow and Keras from scratch. These libraries are widely used in Python for AIML.