Jamison  Fisher

Jamison Fisher

1619355780

A Practical Guide to TFRecords

TensorFlow’s custom data format TFRecord is really useful. The files are supported natively by the blazing fast tf.data API, support distributed datasets, leverage parallel I/O. But they are somewhat overwhelming at first. This post serves as a practical introduction.

Overview

We will first go over the concept behind TFRecord files. With this in mind, we can then go on to work with image data; we will use both a small and a large dataset. Expanding our knowledge, we then work with audio data. The last large domain is the text domain, which we’ll cover as well. To combine all this, we create an artificial multi-data-type dataset and, you guess it, write it to TFRecords as well.

TFRecord’s layout

When I started my deep learning research, I naively stored my data scattered over the disk. To make things worse, I polluted my directories with thousands of small files, in the order of a few KB. The cluster I was then working on was not amused. And it took quite some time to get all these files loaded.

This is where TFRecords (or large numpy arrays, for that matter) come in handy: Instead of storing the data scattered around, forcing the disks to jump between blocks, we simply store the data in a sequential layout. We can visualize this concept in the following way:

Visualization created by the author

The TFRecord file can be seen as a wrapper around all the single data samples. Every single data sample is called an Example, and is essentially a dictionary storing the mapping between a key and our actual data.

Now, the seemingly complicated part is this: When you want to write your data to TFRecords, you first have to convert your data to a Feature. These features are then the inner components of one Example:

Visualization created by the author

So far, so good. But what is now the difference to storing your data in a compressed numpy array, or a pickle file? Two things: The TFRecord file is stored sequentially, enabling fast streaming due to low access times. And secondly, the TFRecord files are natively integrated into TensorFlows tf.data API, easily enabling batching, shuffling, caching, and the like.

As a bonus, if you ever have the chance and the computing resources to do multi-worker training, you can distribute the dataset across your machines.

On a code level, the feature creation happens with these convenient methods, which we will talk about later on:

To write data to TFRecord files, you first create a dictionary that says

I want to store this data point under this key

When reading from TFRecord files, you invert this process by creating a dictionary that says

I have this keys, fill this placeholder with the value stored at this key

Let us see how this looks in action.

#tensorflow #python #data-science #deep-learning

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A Practical Guide to TFRecords
Murray  Beatty

Murray Beatty

1596344940

Help Your Data Science Career By Publishing Your Work!

This guide aims to cover everything that a data science learner may need to write and publish articles on the internet. It covers why you should write, writing advice for new writers, and a list of places that invite contributions from new writers.

Let’s get to it!

Why you should write:

Writing isn’t just for “writers”. The art of writing well is for everyone to learn - programmers, marketers, managers and leaders, alike. And yes, data scientists and analysts too!

You should write articles because when you do:

You learn:

Writing teaches you the art of writing. It’s kind of circular but it’s true.

Make no mistake, the art of writing isn’t about grammar (although, that’s important) and flowery language (definitely not important). It’s about conveying your thoughts with clarity in simple language.

And learning this art is important even if you absolutely know that you don’t want to write blogs/articles for a living. It’s important because all the jobs have some form of writing involved - messages, emails, memos and the whole spectrum. So basically, writing is a medium for almost any job you can have.

Apart from that, when you write you learn the things that you thought you knew but didn’t really know. So, writing is an opportunity to learn better.

#data science career tips #guide #guides #publishing work #writing guide

Lenora  Hauck

Lenora Hauck

1598028720

K-Means Clustering in Python: A Practical Guide

The k-means clustering method is an unsupervised machine learning technique used to identify clusters of data objects in a dataset. There are many different types of clustering methods, but k-means is one of the oldest and most approachable. These traits make implementing k-means clustering in Python reasonably straightforward, even for novice programmers and data scientists.

If you’re interested in learning how and when to implement k-means clustering in Python, then this is the right place. You’ll walk through an end-to-end example of k-means clustering using Python, from preprocessing the data to evaluating results.

In this tutorial, you’ll learn:

  • What k-means clustering is
  • When to use k-means clustering to analyze your data
  • How to implement k-means clustering in Python with scikit-learn
  • How to select a meaningful number of clusters

Click the link below to download the code you’ll use to follow along with the examples in this tutorial and implement your own k-means clustering pipeline:

Download the sample code: Click here to get the code you’ll use to learn how to write a k-means clustering pipeline in this tutorial.

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What Is Clustering?

Clustering is a set of techniques used to partition data into groups, or clusters. Clusters are loosely defined as groups of data objects that are more similar to other objects in their cluster than they are to data objects in other clusters. In practice, clustering helps identify two qualities of data:

  1. Meaningfulness
  2. Usefulness

Meaningful clusters expand domain knowledge. For example, in the medical field, researchers applied clustering to gene expression experiments. The clustering results identified groups of patients who respond differently to medical treatments.

Useful clusters, on the other hand, serve as an intermediate step in a data pipeline. For example, businesses use clustering for customer segmentation. The clustering results segment customers into groups with similar purchase histories, which businesses can then use to create targeted advertising campaigns.

Note: You’ll learn about unsupervised machine learning techniques in this tutorial. If you’re interested in learning more about supervised machine learning techniques, then check out Logistic Regression in Python.

There are many other applications of clustering, such as document clustering and social network analysis. These applications are relevant in nearly every industry, making clustering a valuable skill for professionals working with data in any field.

#a practical guide #python #k-means clustering

Biju Augustian

Biju Augustian

1576048394

A Practical Guide to Database Design and Access SQL|Simpliv

Description
In this course you will design and develop normalized tables, analyse table designs to avoid redundancy and anomalies, write SQL statements to extract and manipulate requests and resolve many inherent problems associated with poor database design. As you develop these skills, you will be exposed to LucidArt for diagramming ERD’s; querySQL and QBE programming, to execute query statements; MS Access Workbench, for implementing business solutions.

Who this course is for:

IT staff and business employees who would like an understanding of how to organize, access and report data effectively and efficiently
Basic knowledge
An interest in computer information systems. Copy of MS Access 2016 software is optional

What will you learn
Build relational database solutions for home and business, including lots of practical examples, “best practice” guidelines, Access SQL design and implementation, templates that work
To continue:

#sql #practical-guide #database #design-and-access-sql

Best Practice Guide For Rest API Security

What is the Rest API?

RESTful programming provides stateless and a uniform interface, Rest API is HTTP-based URLs that hide the back-end infrastructure from the user. Rest APIs provide the back end for modern web and mobile applications.

Why is API security important?

Rest APIs are the most important layer in the back-end infrastructure for most modern applications. Cybercriminals are increasingly targeting APIs. Ensuring web API security is the most important and crucial. Let’s see what you can do to ensure REST API security.

Common Security Vulnerabilities & Mistakes and Best Practices to Secure REST APIs

Always Use HTTPS

API security start with Http Connection. All requests from clients to your API should be encrypted (HTTPS). Unfortunately, many client HTTP do not enable HTTPS/secure connections by default it’s necessary to enforce that from the server. When Clients who attempt to connect via HTTP should forcefully be redirected to secure HTTPS connections.

You can get a free certificate with Let’s Encrypt. SSL provides security from basic API vulnerabilities with almost minimal effort

Distributed Denial of Service Attacks (DDoS)

A Distributed Denial of Service (DDoS) is a targeted cyber attack on a web site or device where a malicious attacker flood of traffic is sent from single or multiple sources. the main purpose of DDos is to make a machine or network resource unavailable to its genuine users by temporarily or disrupting services of a host connected to the Internet. if we are not using appropriate security practice or tools then it makes RESTful API into a non-functional situation.

####### How to Prevent or Stop DDoS Attacks

API DoS attacks are more common these days. Rest APIs utilizations also increasing day-by-day. The organization’s dependency is increasing day-by-day because of business needed a unified platform. An attacker can use multiple ways for the DDoS attack so as developer or security engineer you need to implement long-term solution not a temporary

Rate Limit

Attackers can make so many repeated calls on the APIs. it can make resources unavailable to its genuine users. A rate limit is the number of API calls an app or user can make within a given period. When this limit is exceeded, block API access temporarily and return the 429 (too many requests) HTTP error code.

#restapi #rest api #rest api security #best practice #rest api developer guide #security

6 Best Practices for Implementing DevOps: Step by Step Guide

Introduction

Any enterprise aiming to collaborate with IT outsourcing service providers for the development of software should understand the importance of DevOps Best Practices to scale up the effectiveness of the project execution.

DevOps Best Practices not only support the efficient completion of projects but also takes the collaboration between companies and software service providers to the next level. The following article will guide you through some of the DevOps Best Practices that you can use for implementing DevOps.

#best practices #devops #devops best practices #devops implementation