Neural Network - Paperspace Blog

Neural Architecture Search Part 1: An Overview

The hyperparameter optimization problem has been solved in many different ways for classical machine learning algorithms. Some examples include the use of grid search, random search, Bayesian optimization, meta-learning, and

Keras

Working With The Lambda Layer in Keras

In this tutorial we'll cover how to use the Lambda layer in Keras to build, save, and load models which perform custom operations on your data.

Deep Learning

Image Compression Using Autoencoders in Keras

Autoencoders are a deep learning model for transforming data from a high-dimensional space to a lower-dimensional space. They work by encoding the data, whatever its size, to a 1-D vector.

Tutorial

Generating an interactive Pix2Pix model with Gradient° and ml5.js

This post will go through the process of training a generative image model using Gradient° and then porting the model to ml5.js, so you can interact with it in

Series: Optimization

Intro to Optimization in Deep Learning: Vanishing Gradients and Choosing the Right Activation Function

An look into how various activation functions like ReLU, PReLU, RReLU and ELU are used to address the vanishing gradient problem, and how to chose one amongst them for your network.

Computer Vision

Style Transfer Part 2: Face Swap!

Face Transfer Using Deep Neural Nets Introduction In the previous article we looked at what style transfer was and how to use it to create your own art. Now, we will focus on how to extend the capabilities of style transfer beyond the art

Generative Models

Recurrent Language Models with RNNs

Building an intuition There is a vast amount of data which is inherently sequential, such as speech, time series (weather, financial, etc.), sensor data, video, and text, just to mention some. Recurrent Neural Networks (RNNs) are a family of neural networks designed specifically for