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

What is GEEK

Buddha Community

Understanding Convolutions by hand vs TensorFlow

Hand Sanitizer in bulk - Get your effective hand sanitizer here

With the spread of various harmful virus globally causing immense distress and fatalities to human mankind, it has become absolutely essential for people to ensure proper and acute hygiene and cleanliness is maintained. To further add to the perennial hardship to save lives of people the recent pandemic of Covid-19 affected globally created the worst nightmare for people of all walks of life. Looking at the present crisis, it has become imperative for human beings to be encouraged to tackle this challenge with an everlasting strength to help protect oneself and their loved ones against the devastating effects of the virus. One thing that stands up between keeping all safe and vulnerable is by making sure that everybody attentively Hand wash periodically to help physically remove germs from the skin and getting rid of the live microbes.

The essence of apposite handwashing is based around time invested in washing and the amount of soap and water used. Technically, washing hands without soap is much less effective anyway. But incase a proper handwashing support system doesn’t become possible around, the usage of Effective Hand Sanitizer will certainly help fight to reduce the number of microbes on the surface of hands efficiently, eliminating most variants of harmful bacteria to settle.

The need has come about for Hand Sanitizer in bulk to save your daily life aptly maintaining a minimum of 60% alcohol - as per the CDC recommendations and approved by USFDA for its greater effectiveness. With the growing demand of people on the move the demand for easy to carry, small, and travel size worthy pouches that are also refillable once the product runs out is the need of the hour. To further make sure that human lives are well protected from these external viruses, it is mandatory for producer of effective Hand Sanitizer to evolve products circumspectly with ingredients that produce not just saving lives but with multiple benefits for people of all ages.

#hand sanitizer #hand sanitizer in bulk #hand sanitizer ingredient #hand sanitizer to alcohol #hand sanitizer travel size #hand sanitizer wholesale

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

Autumn  Blick

Autumn Blick

1598839687

How native is React Native? | React Native vs Native App Development

If you are undertaking a mobile app development for your start-up or enterprise, you are likely wondering whether to use React Native. As a popular development framework, React Native helps you to develop near-native mobile apps. However, you are probably also wondering how close you can get to a native app by using React Native. How native is React Native?

In the article, we discuss the similarities between native mobile development and development using React Native. We also touch upon where they differ and how to bridge the gaps. Read on.

A brief introduction to React Native

Let’s briefly set the context first. We will briefly touch upon what React Native is and how it differs from earlier hybrid frameworks.

React Native is a popular JavaScript framework that Facebook has created. You can use this open-source framework to code natively rendering Android and iOS mobile apps. You can use it to develop web apps too.

Facebook has developed React Native based on React, its JavaScript library. The first release of React Native came in March 2015. At the time of writing this article, the latest stable release of React Native is 0.62.0, and it was released in March 2020.

Although relatively new, React Native has acquired a high degree of popularity. The “Stack Overflow Developer Survey 2019” report identifies it as the 8th most loved framework. Facebook, Walmart, and Bloomberg are some of the top companies that use React Native.

The popularity of React Native comes from its advantages. Some of its advantages are as follows:

  • Performance: It delivers optimal performance.
  • Cross-platform development: You can develop both Android and iOS apps with it. The reuse of code expedites development and reduces costs.
  • UI design: React Native enables you to design simple and responsive UI for your mobile app.
  • 3rd party plugins: This framework supports 3rd party plugins.
  • Developer community: A vibrant community of developers support React Native.

Why React Native is fundamentally different from earlier hybrid frameworks

Are you wondering whether React Native is just another of those hybrid frameworks like Ionic or Cordova? It’s not! React Native is fundamentally different from these earlier hybrid frameworks.

React Native is very close to native. Consider the following aspects as described on the React Native website:

  • Access to many native platforms features: The primitives of React Native render to native platform UI. This means that your React Native app will use many native platform APIs as native apps would do.
  • Near-native user experience: React Native provides several native components, and these are platform agnostic.
  • The ease of accessing native APIs: React Native uses a declarative UI paradigm. This enables React Native to interact easily with native platform APIs since React Native wraps existing native code.

Due to these factors, React Native offers many more advantages compared to those earlier hybrid frameworks. We now review them.

#android app #frontend #ios app #mobile app development #benefits of react native #is react native good for mobile app development #native vs #pros and cons of react native #react mobile development #react native development #react native experience #react native framework #react native ios vs android #react native pros and cons #react native vs android #react native vs native #react native vs native performance #react vs native #why react native #why use react native

Hello Jay

Hello Jay

1594525380

Keras vs. Tensorflow - Difference Between Tensorflow and Keras

Keras and Tensorflow are two very popular deep learning frameworks. Deep Learning practitioners most widely use Keras and Tensorflow. Both of these frameworks have large community support. Both of these frameworks capture a major fraction of deep learning production.

Which framework is better for us then?

This blog will be focusing on Keras Vs Tensorflow. There are some differences between Keras and Tensorflow, which will help you choose between the two. We will provide you better insights on both these frameworks.

What is Keras?

Keras is a high-level API built on the top of a backend engine. The backend engine may be either TensorFlow, theano, or CNTK. It provides the ease to build neural networks without worrying about the backend implementation of tensors and optimization methods.

Fast prototyping allows for more experiments. Using Keras developers can convert their algorithms into results in less time. It provides an abstraction overs lower level computations.

Major Applications of Keras

  • The performance of Keras is smooth on both CPU and GPU.
  • Keras provides modularity, flexibility to code, extensibility, and has an adaptation for innovation and research.
  • The pythonic nature of Keras makes it easy to explore and debug the code.

What is Tensorflow?

Tensorflow is a tool designed by Google for the deep learning developer community. The aim of TensorFlow was to make deep learning applications accessible to the people. It is an open-source library available on Github. It is one of the most famous libraries to experiment with deep learning. The popularity of TensorFlow is because of the ease of building and deployment of neural net models.

Major area of focus here is numerical computation. It was built keeping the processing computation power in mind. Therefore we can run TensorFlow applications on almost kind of computer.

Major applications of Tensorflow

  • From mobiles to embedded devices and distributed servers Tensorflow runs on all the platforms.
  • Tensorflow is the enterprise of solving real-world and real-time problems like image analysis, robotics, generating data, and NLP.
  • Developers are implementing tools for translation languages and the detection of skin cancers using Tensorflow.
  • Major projects using TensorFlow are Google translate, video detection, image recognition.

#keras tutorials #keras vs tensorflow #keras #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