How will machine learning shape the future of writing?

Machine learning is a widely used application of AI that allows programmes to learn from extensive datasets without being programmed manually.

It can replace, as the paragraph itself implies, certain writing tasks being automated, leading to job loss for low-cost/low-skilled writers.

Imagine this: it takes almost half a lifetime for a human being to read enough to be able to pick up the art of writing and then actually write and get published, let alone be exceptionally adept in it.

Human labour has value, and that is why we still patronise such labour.

If you cannot differentiate the text written by a human author from that written by a machine, would you be willing to pay for it as much as you did before?

Human creativity, apart from following others and learning certain strategies, also requires raw feelings and emotions.

The only hope I see for the near future is collaboration between machines and human writers where, rather than competing with each other, both would complement each other’s skills and continue to produce great reads.

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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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