A Beginners Guide to Artificial Neural Network using Tensor Flow & Keras. Building a fraud detection model using Artificial Neural Network & fine-tuning Hyperparameters using RandomizedSearchCV
ANNs (Artificial Neural Network) is at the very core of Deep Learning an advanced version of Machine Learning techniques. Artificial Neural Networks involve the following concepts. The input & the output layer, the hidden layers, neurons under hidden layers, forward propagation, and backward propagation. In a nutshell, the input layer is the set of independent variables, the output layer represents the final output (the dependent variable), the hidden layers consist of neurons where equations are developed and activation functions are applied. The forward propagation talks about how equations are developed to achieve the final output, whereas the backward propagation calculates the gradient descent to updates the learning rates accordingly. More about the operational process can be found in the article below.
When an ANN contains a deep stack of hidden layers, it is called a deep neural network (DNN). A DNN works with multiple weights and bias terms, each of which needs to be trained. In just two passes through the network, the algorithm can compute the Gradient Descent automatically. In other words, it can identify how each weight and each bias term across all the neurons should be tweaked to reduce the error. The process repeats unless the network converges to a minimum error.
Let’s run through the algorithm step by step:
The past few decades have witnessed a massive boom in the penetration as well as the power of computation, and amidst this information.
Most popular Data Science and Machine Learning courses — August 2020. This list was last updated in August 2020 — and will be updated regularly so as to keep it relevant
Cheat Sheets for AI, Neural Networks, Machine Learning, Deep Learning & Big Data
Artificial Neural Networks — Recurrent Neural Networks. Remembering the history and predicting the future with neural networks. A intuition behind Recurrent neural networks.
Fundamentals of Neural Network in Machine Learning. What is a Neuron? What is the Activation Function? How do Neural Network Works? How do Neural Networks Learn?