Uriah  Dietrich

Uriah Dietrich

1615979880

Convolutional AutoEncoders (CAE) with Tensorflow

Autoencoders has been in the deep learning literature for a long time now, most popular for data compression tasks. With their easy structure and not so complicated underlying mathematics, they became one of the first choices when it comes to dimensionality reduction in simple data. However, using basic fully connected layers fail to capture the patterns in pixel-data since they do not hold the neighboring information. For a good capturing of the image data in latent variables, convolutional layers are usually used in autoencoders.
Introduction
Autoencoders are unsupervised neural network models that summarize the general properties of data in fewer parameters while learning how to reconstruct it after compression[1]. In order to extract the textural features of images, convolutional neural networks provide a better architecture. Moreover, CAEs can be stacked in such a way that each CAE takes the latent representation of the previous CAE for higher-level representations[2]. Nevertheless, in this article, a simple CAE will be implemented having 3 convolutional layers and 3 subsampling layers in between.
The tricky part of CAEs is at the decoder side of the model. During encoding, the image sizes get shrunk by subsampling with either average pooling or max-pooling. Both operations result in information loss which is hard to re-obtain while decoding.

#convolutional-network #tensorflow #deep-learning #artificial-intelligence #convolutional-autoencoder

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Convolutional AutoEncoders (CAE) with Tensorflow
Uriah  Dietrich

Uriah Dietrich

1615979880

Convolutional AutoEncoders (CAE) with Tensorflow

Autoencoders has been in the deep learning literature for a long time now, most popular for data compression tasks. With their easy structure and not so complicated underlying mathematics, they became one of the first choices when it comes to dimensionality reduction in simple data. However, using basic fully connected layers fail to capture the patterns in pixel-data since they do not hold the neighboring information. For a good capturing of the image data in latent variables, convolutional layers are usually used in autoencoders.
Introduction
Autoencoders are unsupervised neural network models that summarize the general properties of data in fewer parameters while learning how to reconstruct it after compression[1]. In order to extract the textural features of images, convolutional neural networks provide a better architecture. Moreover, CAEs can be stacked in such a way that each CAE takes the latent representation of the previous CAE for higher-level representations[2]. Nevertheless, in this article, a simple CAE will be implemented having 3 convolutional layers and 3 subsampling layers in between.
The tricky part of CAEs is at the decoder side of the model. During encoding, the image sizes get shrunk by subsampling with either average pooling or max-pooling. Both operations result in information loss which is hard to re-obtain while decoding.

#convolutional-network #tensorflow #deep-learning #artificial-intelligence #convolutional-autoencoder

Hudson  Kunde

Hudson Kunde

1590891900

Building Convolutional Autoencoder using TensorFlow 2.0

We are going to continue our journey on the autoencoders. In this article, we are going to build a convolutional autoencoder using the convolutional neural network (CNN) in TensorFlow 2.0.

#keras-autoencoder #tensorflow

5 Steps to Passing the TensorFlow Developer Certificate

Deep Learning is one of the most in demand skills on the market and TensorFlow is the most popular DL Framework. One of the best ways in my opinion to show that you are comfortable with DL fundaments is taking this TensorFlow Developer Certificate. I completed mine last week and now I am giving tips to those who want to validate your DL skills and I hope you love Memes!

  1. Do the DeepLearning.AI TensorFlow Developer Professional Certificate Course on Coursera Laurence Moroney and by Andrew Ng.

2. Do the course questions in parallel in PyCharm.

#tensorflow #steps to passing the tensorflow developer certificate #tensorflow developer certificate #certificate #5 steps to passing the tensorflow developer certificate #passing

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

Mckenzie  Osiki

Mckenzie Osiki

1622078340

Understanding Convolutions by hand vs TensorFlow

Do you think we can match TensorFlow by hand? You bet!

1. Purpose

TensorFlow and various other open source libraries for machine learning like SciPy, provide these nice built in functions for performing convolutions. However, as nice as these functions are, it is worth opening the hood to discover the power behind the code. In my opinion, without the convolutional layer, computer vision would be as blind as a bat. So I hope you enjoy this article because we will dig into the convolutions that make up convolutional layers and see the big picture together.

The Jupyter Notebooks I made for this are on my GitHub.

#computer-vision #deep-learning #convolutional-network #machine-learning #tensorflow