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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How Machine Learning Will Shape the Ecommerce Industry

With machine learning becoming ever more integrated into our daily lives, it’s natural to wonder, “How will machine learning affect ecommerce?”It’s a great question.

Machine Learning: The Future of EcommerceBefore we dig any deeper, it’s important to make a distinction between artificial intelligence and machine learning.

Let’s take a look at where ecommerce is at and how machine learning will affect ecommerce in the not-so-distant future.

Machine Learning and the Customer ExperienceMachine learning allows ecommerce businesses to create a more personalized customer experience.

A lot has changed in ecommerce over the last few decades and machine learning promises to change things even more.

How have you seen machine learning used in ecommerce?

ConclusionAs you can see, there are a lot of exciting opportunities for machine learning in ecommerce.

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