Why do some people avoid news? Because they don’t trust us — or because they don’t think we add value to their lives? Nieman Journalism Lab

In 2017, 29 percent of those surveyed worldwide said they “Often or sometimes avoid the news,” including 38 percent in the United States and 24 percent in the U.K. By 2019, those numbers had increased to 32 percent worldwide, 41 percent in the U.S., and 35 percent in the U.K. Why do people avoid news? In the 2017 data, the leading causes for Americans were “It can have a negative effect on my mood” and “I can’t rely on news to be true”.

LinkedIn senior editor-at-large Isabelle Roughol wrote a short piece Saturday summarizing this year’s Digital News Report, highlighted the news avoidance data in the headline, and asked readers about their own experience with news avoidance.

Mainstream news is a waste of time and energy – so yes, I avoid the news.

News organizations have become dependent on sensationalism and shocking news.

My question to you is why would I waste my energy and psychological wellbeing looking at grotesque pictures or reading depressing draining news? I would much rather see a magazine full of ads and no news.

Regular news consumption can engender a kind of learned helplessness that make clear the appeal of ideologically slanted news – which offers up a clear cast of good guys and bad guys with no moral gray – and just avoiding news entirely.

News consumption used to be about daily habits – reading the paper every morning, watching the 6 o’clock news every night.

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How I used Deep Learning to Optimize an Ecommerce Business Process with Keras

Nowadays in the era of deep learning and computer vision, checking manually web content is considered as a flaw and very time consuming, furthermore it can lead to many mistakes such as this one below, where moderators had accepted a laptop ad in phone category which is wrong and affect search engine quality, while this work could be done in a second by a Deep Learning model.

In this blog post I will cover how I optimized this process by building a simple Convolutional Neural Network using Keras framework, that can classify if an uploaded image is for a phone or a laptop and tell us if the image is matching the ad category or not.

2.2 Image resizingThis step is absolutely depending on the adopted Deep Learning architecture, for example when using Alexnet model to classify images, the input shape should be 227 x 227, while for VGG-19 the input shape is 224 x 224.Since we are not going to adopt any pre-built architecture, we will build our own Convolutional Neural Network model, where the input size is 64 x 64, like shown in the code snapshot below.

For this model, we will discuss each component how it was implemented using Keras and its own parameters starting from convolutions to fully connected layer, but first of all, let’s discover the full architecture of the built-in model.

We have to compile the network that we have just built by calling compile function, it is a mandatory step for every model built using Keras.

Analyzing Model with TensorBoardIn this step, we will see how we can analyse our model behavior using TensorBoard.

ConclusionTo conclude with, this blog post shows a complete computer vision pipeline by building a Deep Learning model that can predict the class of an uploaded image applied on eCommerce context, starting from the Data Collecting to the Data Modeling and finishing by Model Deployment as a web app.

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Deep Learning Algorithms in eCommerce

What is deep learning?Deep learning is a branch of machine learning which has been developed to help us discover and trace user behaviour online at a more complex level than ordinary machine learning.

Other names for deep learning include deep structured learning, hierarchical learning or deep machine learning.

Deep learning is based on a set of algorithms that try to mimic high level abstractions in data.

How does deep learning work?Although deep learning is a highly complicated and technical process, it can be summed up with a comparison to the human brain.

“Deep learning algorithms transform their inputs through more layers than shallow learning algorithms. At each layer, the signal is transformed by a processing unit, like an artificial neuron, whose parameters are ‘learned’ through training. A chain of transformations from input to output is a credit assignment path. CAPs describe potentially causal connections between input and output and may vary in length.”

This data has allowed deep learning algorithms to trace the buyer journey and by doing that we now have a fairly clear picture of what kind of product information buyers search for when they are making purchase decisions for different things.

Fortunately for Mary the site uses deep learning algorithms.

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