Julie  Donnelly

Julie Donnelly

1595909700

Practicum: Under and Over-complete Autoencoders

Week 7 – Practicum: Under- and over-complete autoencoders

0:00:00 – Week 7 – Practicum

PRACTICUM: http://bit.ly/pDL-en-07-3
We discussed some applications of Autoencoders and talked about why we want to use them. Then we talked about different architectures of Autoencoders (under or over complete hidden layer), how to avoid overfitting issues and the loss functions we should use. Finally we implemented a standard Autoencoder and a denoising Autoencoder.
0:00:55 – Application of Autoencoders
0:14:39 – Architecture and loss function in Autoencoders
0:41:31 – Notebook example for different types of Autoencoders

#deep-learning #machine-learning #artificial-intelligence #developer #python

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Practicum: Under and Over-complete Autoencoders
Harry Patel

Harry Patel

1614145832

A Complete Process to Create an App in 2021

It’s 2021, everything is getting replaced by a technologically emerged ecosystem, and mobile apps are one of the best examples to convey this message.

Though bypassing times, the development structure of mobile app has also been changed, but if you still follow the same process to create a mobile app for your business, then you are losing a ton of opportunities by not giving top-notch mobile experience to your users, which your competitors are doing.

You are about to lose potential existing customers you have, so what’s the ideal solution to build a successful mobile app in 2021?

This article will discuss how to build a mobile app in 2021 to help out many small businesses, startups & entrepreneurs by simplifying the mobile app development process for their business.

The first thing is to EVALUATE your mobile app IDEA means how your mobile app will change your target audience’s life and why your mobile app only can be the solution to their problem.

Now you have proposed a solution to a specific audience group, now start to think about the mobile app functionalities, the features would be in it, and simple to understand user interface with impressive UI designs.

From designing to development, everything is covered at this point; now, focus on a prelaunch marketing plan to create hype for your mobile app’s targeted audience, which will help you score initial downloads.

Boom, you are about to cross a particular download to generate a specific revenue through your mobile app.

#create an app in 2021 #process to create an app in 2021 #a complete process to create an app in 2021 #complete process to create an app in 2021 #process to create an app #complete process to create an app

Julie  Donnelly

Julie Donnelly

1595909700

Practicum: Under and Over-complete Autoencoders

Week 7 – Practicum: Under- and over-complete autoencoders

0:00:00 – Week 7 – Practicum

PRACTICUM: http://bit.ly/pDL-en-07-3
We discussed some applications of Autoencoders and talked about why we want to use them. Then we talked about different architectures of Autoencoders (under or over complete hidden layer), how to avoid overfitting issues and the loss functions we should use. Finally we implemented a standard Autoencoder and a denoising Autoencoder.
0:00:55 – Application of Autoencoders
0:14:39 – Architecture and loss function in Autoencoders
0:41:31 – Notebook example for different types of Autoencoders

#deep-learning #machine-learning #artificial-intelligence #developer #python

Dicanio Rol

Dicanio Rol

1594449179

Complete Guide to build an AutoEncoder in Pytorch and Keras

This article is continuation of my previous article which is complete guide to build CNN using pytorch and keras.

Taking input from standard datasets or custom datasets is already mentioned in complete guide to CNN using pytorch and keras. So we can start with necessary introduction to AutoEncoders and then implement one.

AutoEncoders

Auto Encoder is a neural network that learns encoding data with minimal loss of information.

There are many variants of above network. Some of them are:

Sparse AutoEncoder

This auto-encoder reduces overfitting by regularizing activation function hidden nodes.

Denoising AutoEncoder

This auto-encoder is trained by adding noise to input. This will remove noise from input at evaluation.

#keras #variational-autoencoder #pytorch

Mckenzie  Osiki

Mckenzie Osiki

1621939380

Image Generation Using TensorFlow Keras - Analytics India Magazine

Computer Vision is a wide, deep learning field with enormous applications. Image Generation is one of the most curious applications in Computer Vision. Again, Image Generation has a great collection of tasks; to mention, a few can outperform humans. Most image generation tasks are common for videos, too, since a video is a sequence of images.

A few popular Image Generation tasks are:

  1. Image-to-Image translation (e.g. grayscale image to colour image)
  2. Text-to-Image translation
  3. Super-resolution
  4. Photo-to-Cartoon/Emoji translation
  5. Image inpainting
  6. Image dataset generation
  7. Medical Image generation
  8. Realistic photo generation
  9. Semantic-to-Photo translation
  10. Image blending
  11. Deepfake video generation
  12. 2D-to-3D image translation

One deep learning generative model can perform one or more tasks with a few configuration changes. Some famous image generative models are the original versions and the numerous variants of Variational Autoencoder (VAE), and Generative Adversarial Networks (GAN).

This article discusses the concepts behind image generation and the code implementation of Variational Autoencoder with a practical example using TensorFlow Keras. TensorFlow is one of the top preferred frameworks for deep learning processes. Keras is a high-level API built on top of TensorFlow, which is meant exclusively for deep learning.

The following articles may fulfil the prerequisites by giving an understanding of deep learning and computer vision.

  1. Getting Started With Deep Learning Using TensorFlow Keras
  2. Getting Started With Computer Vision Using TensorFlow Keras

#developers corner #autoencoders #beginner #decoder #deepfake #encoder #fashion mnist #gan #image generation #image processing #image synthesis #keras #super-resolution #tensorflow #vae #variational autoencoder

Unconventional Deep Learning Techniques for Tabular Data

In recent years, Deep Learning has made huge strides in the fields of Computer Vision and Natural Language Processing. And as a result, deep learning techniques have often been confined to image data or sequential (text) data. What about tabular data? The traditional method of information storage and retrieval in many organizations is arguably the most important when it comes to business use cases. But data in our tables/dataframes seem to be content with the use of simple Multi-layer feedforward networks in the Deep Learning arena. Although one can argue that Recurrent Neural Networks (RNNs) are often used on tabular time series data, the applications of these methodologies on data without a time series component in it is very limited. In this blog post, we’ll look at the application of some deep learning techniques, usually used on image or text data, on non-time series tabular data in the decreasing level of conventionality.

Autoencoders for Dimensionality Reduction

Conventionally, autoencoders have been used for non-linear dimensionality reduction. Say we have a dataset where the number of features is way more than what we’d prefer it to be, we can use autoencoders to bring the feature set to the desired feature size through complex non-linear functions that we needn’t have to worry about! This is a more effective technique compared to linear dimensionality reduction methods like PCA (Principal Component Analysis) or other conventional non-linear techniques like LLE (Locally Linear Embeddings).

Image for post

Autoencoder structure

Autoencoders are trained on the training feature set without any labels, i.e., they try to predict as output whatever the input was. This would be a simple task if the hidden layers were wide enough to capture all of our input data. Thereby, the requirement, for a neural network to be an autoencoder, is to have at least one layer, the bottleneck layer, of lower dimension compared to the input or the output dimension. This is usually the embedding layer or the reduced feature set we want to use. The metric when training the network can be the usual mean squared error or mean absolute error. If the original data is _x _and the reconstructed output generated by the autoencoder is _x_hat, _we try to minimize

#denoising-autoencoder #autoencoder #language-model #deep-learning #deep learning