Balancing the Regularization Effect of Data Augmentation

The Effect of Data Augmentation

When training neural networks, data augmentation is one of the most commonly used pre-processing techniques. The word **“augmentation” **which literally means _“the action or process of making or becoming greater in size or amount”, _summarizes the outcome of this technique. But another important effect is that _it increases or augments the diversity of the data. _The increased diversity means, at each training stage the model comes across a different version of the original data.

By Author

**_Why do we need this ‘increased diversity’ in data? _**The answer lies in the core tenet of machine learning — The Bias-Variance tradeoff. More complex models like deep neural networks have low bias but suffer from high variance. This implies that, these models overfit the training data and would show poor perform on test data or the data, they haven’t seen before. This would lead to higher prediction errors. Thus, the increased diversity from data augmentation reduces the variance of the model by making it better at generalizing.

For images, some common methods of data augmentation are taking cropped portions, zooming in/out, rotating along the axis, vertical/horizontal flips, adjusting the brightness and sheer intensity. Data augmentation for audio data involves adding noise, changing speed and pitch.

While data augmentation prevents the model from overfitting, some augmentation combinations can actually lead to underfitting. This slows down training which leads to a huge strain on resources like available processing time, GPU quotas, etc. Moreover, the model isn’t able to learn as much information to give accurate predictions which, again leads to high prediction errors. In this blog post we take the example of semantic segmentation on satellite images, to see the impact of different combinations of data augmentations on training.

About the Data set

This Kaggle data set gives the** satellite images from Sentinel 2 and their corresponding masks which segment the water bodies**. The masks have been calculated using the Normalized Difference Water Index or NDWI. Out of a total 2841 images in the data set, 2560 were extracted for the train set, 256 for the validation set and 25 for the test set respectively. The entire analysis and modeling was done on Google Colab with GPU support.

Structure of U-NET

Simply put, a U-NET is an autoencoder with residual or skip connections from each convolutional block in the encoder to its counterpart in the decoder. This results in a symmetric ‘U’ like structure. This article gives a comprehensive line by line explanation of the structure of a U-NET from the original paper.

We use a slightly modified version of the U-NET as shown below.

Snapshot of a block of the UNET used (By Author)

A Look at Different Cases of Data Augmentation

We explore 5 different cases of data augmentation with the help of Keras ImageDataGenerator. We want to see how augmentation can lead to overfitting or underfitting during training. Thus, for comparison of the 5 cases, Accuracy and _Loss _during training & validation were used; where binary cross-entropy was taken as the loss function.

When dealing with semantic segmentation, an important point to remember is to apply the _same _augmentations to the images and their corresponding masks!

#deep-learning #deep learning

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Your Data Architecture: Simple Best Practices for Your Data Strategy

If you accumulate data on which you base your decision-making as an organization, you should probably think about your data architecture and possible best practices.

If you accumulate data on which you base your decision-making as an organization, you most probably need to think about your data architecture and consider possible best practices. Gaining a competitive edge, remaining customer-centric to the greatest extent possible, and streamlining processes to get on-the-button outcomes can all be traced back to an organization’s capacity to build a future-ready data architecture.

In what follows, we offer a short overview of the overarching capabilities of data architecture. These include user-centricity, elasticity, robustness, and the capacity to ensure the seamless flow of data at all times. Added to these are automation enablement, plus security and data governance considerations. These points from our checklist for what we perceive to be an anticipatory analytics ecosystem.

#big data #data science #big data analytics #data analysis #data architecture #data transformation #data platform #data strategy #cloud data platform #data acquisition

Getting Started With Data Lakes

Frameworks for Efficient Enterprise Analytics

The opportunities big data offers also come with very real challenges that many organizations are facing today. Often, it’s finding the most cost-effective, scalable way to store and process boundless volumes of data in multiple formats that come from a growing number of sources. Then organizations need the analytical capabilities and flexibility to turn this data into insights that can meet their specific business objectives.

This Refcard dives into how a data lake helps tackle these challenges at both ends — from its enhanced architecture that’s designed for efficient data ingestion, storage, and management to its advanced analytics functionality and performance flexibility. You’ll also explore key benefits and common use cases.

Introduction

As technology continues to evolve with new data sources, such as IoT sensors and social media churning out large volumes of data, there has never been a better time to discuss the possibilities and challenges of managing such data for varying analytical insights. In this Refcard, we dig deep into how data lakes solve the problem of storing and processing enormous amounts of data. While doing so, we also explore the benefits of data lakes, their use cases, and how they differ from data warehouses (DWHs).

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This is a preview of the Getting Started With Data Lakes Refcard. To read the entire Refcard, please download the PDF from the link above.

#big data #data analytics #data analysis #business analytics #data warehouse #data storage #data lake #data lake architecture #data lake governance #data lake management

How Has COVID-19 Impacted Data Science?

The COVID-19 pandemic disrupted supply chains and brought economies around the world to a standstill. In turn, businesses need access to accurate, timely data more than ever before. As a result, the demand for data analytics is skyrocketing as businesses try to navigate an uncertain future. However, the sudden surge in demand comes with its own set of challenges.

Here is how the COVID-19 pandemic is affecting the data industry and how enterprises can prepare for the data challenges to come in 2021 and beyond.

#big data #data #data analysis #data security #data integration #etl #data warehouse #data breach #elt

Applications Of Data Science On 3D Imagery Data

CVDC 2020, the Computer Vision conference of the year, is scheduled for 13th and 14th of August to bring together the leading experts on Computer Vision from around the world. Organised by the Association of Data Scientists (ADaSCi), the premier global professional body of data science and machine learning professionals, it is a first-of-its-kind virtual conference on Computer Vision.

The second day of the conference started with quite an informative talk on the current pandemic situation. Speaking of talks, the second session “Application of Data Science Algorithms on 3D Imagery Data” was presented by Ramana M, who is the Principal Data Scientist in Analytics at Cyient Ltd.

Ramana talked about one of the most important assets of organisations, data and how the digital world is moving from using 2D data to 3D data for highly accurate information along with realistic user experiences.

The agenda of the talk included an introduction to 3D data, its applications and case studies, 3D data alignment, 3D data for object detection and two general case studies, which are-

• Industrial metrology for quality assurance.
• 3d object detection and its volumetric analysis.

This talk discussed the recent advances in 3D data processing, feature extraction methods, object type detection, object segmentation, and object measurements in different body cross-sections. It also covered the 3D imagery concepts, the various algorithms for faster data processing on the GPU environment, and the application of deep learning techniques for object detection and segmentation.

#developers corner #3d data #3d data alignment #applications of data science on 3d imagery data #computer vision #cvdc 2020 #deep learning techniques for 3d data #mesh data #point cloud data #uav data

What Is ETLT? Merging the Best of ETL and ELT Into a Single ETLT Data Integration Strategy

Data integration solutions typically advocate that one approach – either ETL or ELT – is better than the other. In reality, both ETL (extract, transform, load) and ELT (extract, load, transform) serve indispensable roles in the data integration space:

• ETL is valuable when it comes to data quality, data security, and data compliance. It can also save money on data warehousing costs. However, ETL is slow when ingesting unstructured data, and it can lack flexibility.
• ELT is fast when ingesting large amounts of raw, unstructured data. It also brings flexibility to your data integration and data analytics strategies. However, ELT sacrifices data quality, security, and compliance in many cases.

Because ETL and ELT present different strengths and weaknesses, many organizations are using a hybrid “ETLT” approach to get the best of both worlds. In this guide, we’ll help you understand the “why, what, and how” of ETLT, so you can determine if it’s right for your use-case.

#data science #data #data security #data integration #etl #data warehouse #data breach #elt #bid data