How do artificial intelligence, machine learning, deep learning and neural networks relate to each other?
Author(s): AlishaS Originally published on Towards AI. Machine Learning vs. AI vs. Deep Learning vs. Neural Networks: Whatβs the Difference? The rapid evolution of technology is molding our everyday existence as businesses turn more and more to sophisticated algorithms for efficiency. Amidst …
GANs (Generative Adversarial Networks:)
Author(s): Data Science meets Cyber Security Originally published on Towards AI. We Saw the Generative AI, Now Letβs See How Actually It Is Generated.! IMAGE SOURCE: BY AUTHOR INTRODUCTION: GANs, Generative Adversarial networks, letβs first understand what GANs are. So I have …
Graph Attention Networks Paper Explained With Illustration and PyTorch Implementation
Author(s): Ebrahim Pichka Originally published on Towards AI. A detailed and illustrated walkthrough of the βGraph Attention Networksβ paper by VeliΔkoviΔ et al. with the PyTorch implementation of the proposed model. Illustration of the message-passing layer in a Graph Attention Network sβ …
1Cycle Learning Rate Scheduling with TensorFlow and Keras
Author(s): Jonathan Quijas Originally published on Towards AI. Machine Learning Engineering A Practical Methodology to Set the Most Important Hyperparameter in Deep Learning Photo by David Everett Strickler on Unsplash Problem Statement Training a Deep Neural Network can be a challenging task. …
Why Tensorflow is a great choice for building projects powered by Computer Vision
Author(s): Nour Islam Mokhtari Originally published on Towards AI. Explained by an AI engineer who works on Computer Vision applications Join hundreds of subscribers to my weekly newsletter if youβre interested in learning and staying up-to-date with whatβs happening in the field …
Letβs Learn: Neural Nets #3 β Activation Functions
Author(s): Bradley Stephen Shaw Originally published on Towards AI. A beginnerβs guide to activation functions in neural nets. Photo by Antoine Dautry on Unsplash Today weβll be looking at activation functions in neural nets β the who, what, where, and why of …
How to Train MAML(Model-Agnostic Meta-Learning)
Author(s): Sherwin Chen Originally published on Towards AI. An elaborate explanation for MAML and more Top highlight Source: Pixabay Model-Agnostic Meta-Learning(MAML) has been growing more and more popular in the field of meta-learning since itβs first introduced by Finn et al. in …
Stop Calling Automation AI⦠and the Natural Progression of Intelligent Machines
Author(s): Junis Alico Originally published on Towards AI. Defining Automation and AI U+007C Towards AI Photo credit: Image by Gerd Altmann In the last few years, Iβve noticed a lot of C-level executives use AI in their keynote speeches and television appearances. …
DNC: Differential Neural Network
Author(s): Sherwin Chen Originally published on Towards AI. A detailed walk-through of DNC Photo by Philipp Katzenberger on Unsplash In the previous article, we discussed Neural Turing Machines(NTMs), which introduced an external memory to maintain information for later retrieval. In this post, …
A Surefire Way to Building a Neural Network Using Python
Author(s): Abhishek Kumar Originally published on Towards AI. Do you ever wonder how the browse feeds of your Netflix, YouTube, or Instagram Accounts can be so riveting so as to kill hours of your time against your will? Or how there are …
Why Perceptron Neurons Need Bias Input?
Author(s): Caio Davi Originally published on Towards AI. How to avoid bias input in Neural Networks? Please, just donβt. Everybody knows what is the neural network bias input. And everybody uses it since the very first Perceptron implementation. But why we use …
How do Twins and Triplet Neural Network work
Author(s): Edward Ma Originally published on Towards AI. Photo by Ralph Mayhew on Unsplash As a practitioner, we usually build a multiclass classification model which is a predefined number of categories. Indeed, we do not need to know the total number of …
The Advanced Era of Artificial Intelligence(AI): AI Will Now Transform Your Thoughts Into Texts
Author(s): Anushkad Originally published on Towards AI. Recently a system was developed by Joseph Makin and researchers from the University of California, San Francisco, who revealed their findings in the journal Nature Neuroscience. An immensely impressive and astonishing breakthrough may just be …
LSTM for Time-series: Chaos in the AI Industry
Author(s): Michelangiolo Mazzeschi Originally published on Towards AI. Machine Learning LSTM is one of the most common neural networks for forecasting time-series. Unfortunately, this kind of neural network is complex (data preparation in particular), not only because time-series data requires a specific …
Reading: MegDet β A Large Mini-Batch Object Detector, 1st Place of COCO 2017 Detection Challenge (Object Detection)
Author(s): Sik-Ho Tsang Originally published on Towards AI. Computer Vision Large Mini-Batch Size of 256 With Warmup Learning Rate Policy and Cross-GPU Batch Normalization, the training time is reduced From 33 hrs To 4 hrs mmAP increases much faster with 256-batch In …
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