The Future Performance Analysis and Training
AI (Artificial Intelligence) is a branch of computer science that focuses on the development of intelligent machines that can perform tasks that typically require human…
Tag: Artificialneuralnetworks
A Gentle 101 Talk on Artificial Neural Networks
What is this post about? In this super gentle 101 talk given at the Insight Centre for Data Analytics at University College Dublin , I…
The Backpropagation Algorithm-PART(1): MLP and Sigmoid
What is this post about? The training process of deep Artificial Neural Networks (ANNs) is based on the backpropagation algorithm. Starting with this post, and…
Dissecting Relu: A desceptively simple activation function
What is this post about? This is what you will be able to generate and understand by the end of this post. This is the…
Reconciling modern machine learning practice and the bias-variance trade-off
What is This post about? The interview with the lead author of the paper: Prof. Mikhail Belkin Together we will analyze an amazing paper entitled:…
Stochastic Approximation to Gradient Descent
What will you learn? The video below, delivers the main points of this blog post on Stochastic Gradient Descent (SGD): (GitHub code available in here) …
Back-propagation with Cross-Entropy and Softmax
What will you learn? This post is also available to you in this video, should you be interested 😉 https://www.youtube.com/watch?v=znqbtL0fRA0&feature=youtu.be In our previous post, we…
Deriving the Gradient Descent Rule (PART-2)
What Will You Learn? In our previous post, we have talked about the meaning of gradient descent and how it can help us update the…
Deriving the Gradient Descent Rule (PART-1)
The Gradient Descent Rule https://www.youtube.com/watch?v=gYqG4OT2Kj4 When training a model, we strive to minimize a certain error function (). This error function gives us an indication…
What is the Delta Rule? (Part-2)
What We Have Learned So Far … So far, we have learned that the Delta rule guarantees to converge to a model that fits our…