A complete machine learning real world application walk-through using LSTM neural networks

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The popularity of cryptocurrencies skyrocketed in 2017 due to several consecutive months of exponential growth of their market capitalization. The prices peaked at more than $800 billion in January 2018.

Although machine learning has been successful in predicting stock market prices through a host of different time series models, its application in predicting cryptocurrency prices has been quite restrictive. The reason behind this is obvious as prices of cryptocurrencies depend on a lot of factors like technological progress, internal competition, pressure on the markets to…


And take your models from jupyter notebook to production

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When a data scientist/machine learning engineer develops a machine learning model using Scikit-Learn, TensorFlow, Keras, PyTorch etc, the ultimate goal is to make it available in production. Often times when working on a machine learning project, we focus a lot on Exploratory Data Analysis(EDA), Feature Engineering, tweaking with hyper-parameters etc. But we tend to forget our main goal, which is to extract real value from the model predictions.

Deployment of machine learning models or putting models into production means making your models available to the…


Deep Learning for solving the most commonly diagnosed cancer in women

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Breast cancer is the second most common cancer in women and men worldwide. In 2012, it represented about 12 percent of all new cancer cases and 25 percent of all cancers in women.

Breast cancer starts when cells in the breast begin to grow out of control. These cells usually form a tumor that can often be seen on an x-ray or felt as a lump. …


An end to end guide to program your self driving car to steer using deep learning

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Every year, traffic accidents account for 2.2% of global deaths. That stacks up to roughly 1.3 million a year — 3,287 a day. On top of this, some 20–50 million people are seriously injured in auto-related accidents each year. The root of these accidents? Human error.

From distracted driving to drunk driving to reckless driving to careless driving, one poor or inattentive decision could be the difference between a typical drive and a life-threatening situation. …


To better understand your customers

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Customer Segmentation is the subdivision of a market into discrete customer groups that share similar characteristics. Customer Segmentation can be a powerful means to identify unsatisfied customer needs. Using the above data companies can then outperform the competition by developing uniquely appealing products and services.

The most common ways in which businesses segment their customer base are:

  1. Demographic information, such as gender, age, familial and marital status, income, education, and occupation.
  2. Geographical information, which differs depending on the scope of the company. For localized businesses…


Computer vision for friction-less store experience.

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Retail has never been a stagnant industry. Retailers just can not afford to stand still if they want to succeed. They must adapt and innovate or risk being left behind.

The application of computer vision in retail is set to fundamentally change the shopping experience for customers and retailers. In this blog I will be making a computer vision based multi class object classification model for retail products. This project has been inspired from the famous Amazon Go store. …


An end to end pipeline for pneumonia detection from X-ray images

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The risk of pneumonia is immense for many, especially in developing nations where billions face energy poverty and rely on polluting forms of energy. The WHO estimates that over 4 million premature deaths occur annually from household air pollution-related diseases including pneumonia. Over 150 million people get infected with pneumonia on an annual basis especially children under 5 years old. In such regions, the problem can be further aggravated due to the dearth of medical resources and personnel. For example, in Africa’s 57 nations, a…


An end to end pipeline for deep learning on satellite imagery

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Ship detection from remote sensing imagery is a crucial application for maritime security which includes among others traffic surveillance, protection against illegal fisheries, oil discharge control and sea pollution monitoring. This is typically done through the use of an Automated Identification System (AIS), which uses VHF radio frequencies to wirelessly broadcast the ships location, destination and identity to nearby receiver devices on other ships and land-based systems.

AIS are very effective at monitoring ships which are legally required to install a VHF transponder, but fail…


Deep Learning for satellite imagery

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The main objective of this blog is to develop methods for detecting icebergs using satellite radar data and high spatial resolution images in the visible spectral range. The methods of satellite monitoring of dangerous ice formations, like icebergs in the Arctic seas represent a threat to the safety of navigation and economic activity on the Arctic shelf.

The developed method of iceberg detection is based on statistical criteria for finding gradient zones in the analysis of two-dimensional fields of satellite images. The approaches proposed to…


How can deep learning be used for segmenting medical images ?

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Automatic segmentation of microscopy images is an important task in medical image processing and analysis. Nucleus detection is an important example of this task. Imagine speeding up research for almost every disease, from lung cancer and heart disease to rare disorders. The 2018 Data Science Bowl offers our most ambitious mission yet: create an algorithm to automate nucleus detection. We’ve all seen people suffer from diseases like cancer, heart disease, chronic obstructive pulmonary disease, Alzheimer’s, and diabetes. Think how many lives would be transformed if…

Abhinav Sagar

Co — Founder at Stealth Startup, Previous Deep learning researcher at VIT Vellore. https://abhinavsagar.github.io

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