Supervised learning in machine learning can be described in terms of function approximation. Given a dataset comprised of inputs and…

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## CNN vs. RNN vs. ANN – Analyzing 3 Types of Neural Networks in Deep Learning

Overview Check out 3 different types of neural networks in deep learning Understand when to use which type of neural…

Continue Reading## 9 Books on Generative Adversarial Networks (GANs)

Generative Adversarial Networks, or GANs for short, were first described in the 2014 paper by Ian Goodfellow, et al. titled…

Continue Reading## A Tour of Generative Adversarial Network Models

Generative Adversarial Networks, or GANs, are deep learning architecture generative models that have seen wide success. There are thousands of…

Continue Reading## Simple Guide to Hyperparameter Tuning in Neural Networks

This is a good question, and the answer to that is using callbacks. Callbacks: taking a peek into our model…

Continue Reading## Gaining insights on transfer learning with FlashTorch

If you’ve done the maths already… I would be better of randomly guessing it myself. Intuitively, this perhaps makes sense.…

Continue Reading## Deep Learning for NLP: ANNs, RNNs and LSTMs explained!

Well, these weights are also included in any edge that joins two different neurons. This means that in the image…

Continue Reading## Neural Network Optimization

Neural Network OptimizationCovering optimizers, momentum, adaptive learning rates, batch normalization, and more. Matthew Stewart, PhD ResearcherBlockedUnblockFollowFollowingJun 27“The goal is to hit…

Continue Reading## Comprehensive Introduction to Neural Network Architecture

Comprehensive Introduction to Neural Network ArchitectureA detailed overview of neural architecture, activation functions, loss functions, output units. Matthew Stewart, PhD ResearcherBlockedUnblockFollowFollowingJun…

Continue Reading## Evolution of Graph Neural Networks for Recommender Systems

Evolution of Graph Neural Networks for Recommender SystemsIntroduction to the three progressions of Graph Neural Networks in the context of…

Continue Reading## A Gentle Introduction to Writing Gentle Introductions

Your motivation certainly isn’t that you want people to know that you know about this thing — of course not. Your motivation…

Continue Reading## Neural Networks — A Solid Practical Guide

Neural Networks — A Solid Practical GuideExplaining How Neural Networks Work With Practical ExamplesFarhad MalikBlockedUnblockFollowFollowingMay 16This article aims to present a transparent…

Continue Reading## Research of Influence in Offline and Online Social Networks

Research of Influence in Offline and Online Social NetworksThe role of tie strength and network degrees in determining the power of…

Continue Reading## Security Vulnerabilities of Neural Networks

Or one of each?In terms of a classification algorithm which has decision boundaries, here is an illustration of how the…

Continue Reading## Bewildering Brain

Not unless you mail them from Desolation Row. Like its obvious, the lyrics, even though they have a clear Dylanesque…

Continue Reading## Two rival AI approaches combine to let machines learn about the world like a child

Over the decades since the inception of artificial intelligence, research in the field has fallen into two main camps. The…

Continue Reading## Hitchhiker’s Guide to Residual Networks (ResNet) in Keras

Hitchhiker’s Guide to Residual Networks (ResNet) in KerasLearn the foundations of residual networks and build a ResNet in KerasMarco PeixeiroBlockedUnblockFollowFollowingApr 8Photo by…

Continue Reading## Google Knows What You Are Saying With Only 80 MBs

HMMs, RNN-Ts, CTC, DNNs, LSTMs, CNNs, a brief history and fun with letters!Traditionally, voice diction has used Hidden Markov Models…

Continue Reading## Trained neural nets perform much like humans on classic psychological tests

Today we get an answer thanks to the work of Been Kim and colleagues at Google Brain, the company’s AI…

Continue Reading## Machine Learning for Beginners: An Introduction to Neural Networks

A quick recap of what we did:Introduced neurons, the building blocks of neural networks. Used the sigmoid activation function in…

Continue Reading## Why Training a Neural Network Is Hard

Fitting a neural network involves using a training dataset to update the model weights to create a good mapping of…

Continue Reading## What are the limits of deep learning?

What are the limits of deep learning?Proceedings of the National Academy of SciencesBlockedUnblockFollowFollowingJan 21by M. Mitchell WaldropThe much-ballyhooed artificial intelligence…

Continue Reading## Building a WiFi spots Map of networks around you with WiGLE and R

Building a WiFi spots Map of networks around you with WiGLE and RAMRBlockedUnblockFollowFollowingFeb 19It’s always fun to explore the world around…

Continue Reading## Neural Networks: Tricks of the Trade Review

Deep learning neural networks are challenging to configure and train. There are decades of tips and tricks spread across hundreds…

Continue Reading## Review: Highway Networks — Gating Function To Highway (Image Classification)

Review: Highway Networks — Gating Function To Highway (Image Classification)Highway Networks, Inspired By LSTM, Using Gating Function, More Than 1000 Layers. SH TsangBlockedUnblockFollowFollowingFeb…

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