First, we need to get an outline from an image. I decided to use Canny edge detection to extract the edges from an image (in the example above, a drawing of Sherlock Holmes). This will give us a 2D binary array indicating the edges of the image. Now, we need to represent these points as complex coordinates oriented about the origin. The x-axis will represent the real component, and the y-axis will represent the imaginary component.

1. Convert outline to a complex signal

Now, we need to order the points to make the progression as smooth as possible. In practice, I’ve found ordering to minimize the difference from one point to the next is a good approach!

2. Reorder to minimize distance between sequential points

We’ll now apply the Fourier transform to extract the magnitude and phase information associated with each frequency. As visualizing all the oscillators is slow, we’ll filter out all the frequency components above self._n_oscillators .

3. Call the Fourier transform

In other articles, I’ve written about the application of the Fourier transform to determine the frequency components of real or complex signals. We’ve discussed frequency, phase, and magnitude in…

problemsolving

Problem Solving

computerscience

Computer Science

artificialintelligence

Artificial Intelligence

Teach (and learn) the Fourier transform geometrically!

It is important to understand the <a href="https://towardsai.net/p/machine-learning/main-types-of-neural-networks-and-its-applications-tutorial-734480d7ec8e">topology </a>of event-driven architecture to deep dive into various sub-patterns of event-driven architecture. This architecture pattern is based on two main <a href="https://towardsai.net/p/machine-learning/main-types-of-neural-networks-and-its-applications-tutorial-734480d7ec8e">topologies </a>: <ul><li>Mediator </li><li>Broker </li> </ul>

Topology of Event-Driven Architecture

The central mediator plays an important role in mediator <a href="https://towardsai.net/p/machine-learning/main-types-of-neural-networks-and-its-applications-tutorial-734480d7ec8e">topology </a>. It is generally used when it is required to orchestrate various steps with an event through a central mediator. It is helpful for events that have multiple events and needs some level of orchestration to process the event.Example: Design GPS Programm in a Vehicle For overall vehicle safety, navigation, and cost estimation plans, GPS data is considered the starting point for designing a proper GPS for a vehicle. They are intended to provide proper business and safety goals.

Mediator Topology

The mediator component acts as an orchestrator for every initial step. It identifies the type of event or type of data and sends it to a specific Ch<a href="https://towardsai.net/p/machine-learning/building-neural-networks-from-scratch-with-python-code-and-math-in-detail-i-536fae5d7bbf">ann </a>el. Several tools are present to handle mediator topology. Example- jBPM, Mule ESB, Apache Camel, Spring Integration, etc.

Mediator

Event Queue is used to carry the event to the event mediator. It is a kind of <a href="https://sponsors.towardsai.net/">enterprise </a>messaging queue system. There are several messaging systems like RabbitMQ, Kafka, etc.

Queue

The event channel is utilized by the event mediator to pass particular events related to each step in the primary event to the event processors. It can be either message topic or message queue.

Channel

It is a component or service application that contains business logic for the execution of business needs on receiving of events. Nowadays these are microservice-based components. It can be a channel queue consumer as well.

Processor

In Broker topology, the message flow is distributed across the event processor on receiving of event. It is different from a mediator because it is not central. It is useful in chain types of processing. supExample: Design Online Food Ordering System Online food ordering is the process of ordering food from a website.<ul><li>User selects restaurant and orders food.</li><li>Pays for the ordered food.</li><li>Restaurant receives notification</li><li>Delivery partner receives notification</li><li>Restaurant handovers food to the delivery partner</li><li>Delivery partner delivers food to the user</li></ul>

Broker Topology

artsandculture

Society, Arts & Culture

Deep Dive into Event-Driven Architecture

<ul><li>Dynamic : pipelines are configuration as code, allowing for dynamic pipeline generation. This allows for writing code that instantiates pipelines dynamically.</li><li>Extensible : Easily define your operators, executors and extend the library so that it fits the level of abstraction that suits your environment.</li><li>Elegant : pipelines are lean and explicit. Parameterizing your scripts is built into the core of Airflow using the powerful Jinja templating engine.</li><li>Scalable : has a modular architecture and uses a message queue to orchestrate an arbitrary number of workers. Airflow is ready to scale to infinity.</li></ul>

What are the principles of Airflow?

"<a href="https://airflow.apache.org/docs/apache-airflow/stable/index.html">Airflow </a>is a platform to programmatically author, schedule and monitor workflows"

It is used for the scheduling and orchestration of data pipelines or workflows as directed acyclic graphs (DAGs) of tasks.

The Airflow scheduler executes your tasks on an array of workers while following the specified dependencies.

The rich user interface makes it easy to visualize pipelines running in production, monitor progress, and troubleshoot issues when needed.

"Airflow is a platform to programmatically author, schedule and monitor workflows"

It is used for the scheduling and orchestration of data pipelines 

softwareengineering

Software Engineering

Apache Airflow in Data Engineering

is the process of repeating items in a self-similar way. In programming languages, if a program allows you to call a function inside the same function, then it is called a recursive call of the function.

Recursion

<a href="https://en.wikipedia.org/wiki/Recursion">Recursion </a> occurs when a thing is defined in terms of itself or of its type. Recursion is used in a variety of disciplines ranging from linguistics to logic. The most common application of recursion is in mathematics and computer science, where a function being defined is applied within its own definition. While this apparently defines an infinite number of instances (function values), it is often done in such a way that no infinite loop or infinite chain of references can occur.

Recursive Functions is Inception, where function can be assumed of dream,where the function can call itself until the base result is obtained.

<ul><li><a href="https://towardsdatascience.com/pydantic-688e897cfd3a">pydantic </a>class allows custom data types to be defined or we can extend validation with methods on a model decorated with the validator decorator </li><li>Here, The datatype of name, grade, roll, email, phone, subjects, friends must be as mentioned in pydantic class else it will raise an error (pydantic validation error) </li> </ul>

Data Validator

<ul><li>Serialization means to convert an object into that string , and deserialization is its inverse operation (convert string -> object) .</li><li>When transmitting a student in a file, the student is required to be byte strings, but Pydntic(here Student) objects are seldom in this format. Serialization can convert these Pydantic objects into byte strings for such use. After the byte strings are transmitted, the receiver will have to recover the original object from the byte string. <a href="https://stackoverflow.com/questions/3316762/what-is-deserialize-and-serialize-in-json">ref </a></li><li>We need JSON Serializable objects to provide HTTP Response.</li></ul>

Seriali

Sanic and Data Validation using Pydantic

• Kaggle : Kaggle is a platform where professionals can learn, practice, and sharpen their data analytics and data science. The platform provides tons of data that are public and allows the users of the platform to share code so that they can learn the best practices within the data space.

• FiveThirtyEight : FiveThirtyEight is an interactive news and sports platform that has some incredible information for <a href="https://www.analyticsinsight.net/data-science-is-helping-the-world-recover-from-covid-loss/">data visualization </a>projects. The platform makes a lot of their data available to the public, which means they can download and use the information according to their own convenience.

• Google Dataset Search : Google Dataset Search is one of the most comprehensive dataset search engines that are available. It claims to hold more than 25 million online <a href="https://www.analyticsinsight.net/top-10-best-companies-in-india-to-join-as-data-scientists-in-2022/">datasets </a>and assists scientists and researchers in better locating datasets. It is armed with a function, which can sort data types, update dates, and so much more.

• Data.gov : Data.gov allows its users to download and explore data from multiple US government agencies. The information can range from government budgets to climate data. It is documented quite evidently so that it becomes easier for the users to navigate them.

• AWS Public Datasets: AWS Public Datasets allow the users to download the data and work with it on their individual devices. They can analyse the data in the cloud using EC2 and Hadoop via EMR. Amazon has a page that lists all the datasets for its users and also gives free access to all the new accounts.

• UCI Machine Learning Repository : UCI Machine Learning Repository is one of the oldest sources of datasets on the internet. The datasets are generally contributed by the users, and thus have varying levels of documentation and cleanliness. Users can download UCI Machine Learning Repository without any registration.

• Quandl : Quandl is a repository of economic and financial data. Most of this information is free, but some require purchasing. The platform is extremely useful for building models to predict economic indicators and stock prices.

• data.world : data.world describes itself as the social network for <a href="https://www.analyticsinsight.net/top-10-profitable-data-science-tools-used-in-business-in-2022/">data </a>professionals. It is a platform where they can search for copy, analyze, and download datasets. In addition to this, they can upload their data and use it to collaborate with others.

• Buzzfeed News : Buzzfeed provides datasets, analysis, libraries, tools, and guides that are used in the articles available on GitHub. It is a quite popular platform and is used by millions of data professionals.

• Academic Torrents : Academic Torrents is a new site that is geared around sharing the <a href="https://www.analyticsinsight.net/top-biggest-data-science-conferences-to-attend-in-2022/">datasets </a>from specific scientific papers. It is new to the dataset platform market and allows its users to browse data directly on the site.

As the need for data science increases, individuals are looking for authentic datasets platforms for their data science projects. This article lists the top datasets platforms that can be used in 2022.

10 Platforms to Get Datasets for Data Science Projects in 2022

Data scientist teams in financial services, health care, and manufacturing tell VentureBeat their enterprise’s DSML strategies are the most effective when they anticipate and plan for uneven initial results by business unit. The teams also say producing models at scale using MLOps is fundamentally different from producing mainstream internal apps with DevOps. They add that the more complex the operating model of a business unit, the steeper the MLOps learning curve. DSML’s contributions to business units vary by the availability of reliable data and how clearly defined problem statements are.g

DSML is delivering uneven value in enterprises today

<ul><li>Adaptive ML shows potential for improving cybersecurity, remote site security, quality management in manufacturing, and fine-tuning industrial robotics systems. </li> </ul>

<ul><li>Collaborative workflow support in DSML platforms becomes table stakes for competing in the market. </li> </ul>

<ul><li>MLOps will have a breakout year as organizations gain more experience scaling models for deployment faster while tracking business outcomes for greater results. </li> </ul>

<ul><li>Privacy concerns will force every organization creating sensor-connected products and the services supporting them to use synthetic data to build, test, and refine models. </li> </ul>

<ul><li>DSML platform providers need to scale up and automate the entire ML workflow at scale. </li> </ul>

<ul><li>Transfer learning will see rapid adoption across enterprises with DSML strategies operating at scale and in production today. </li> </ul>

<ul><li>Organizations need to focus on use cases and metrics first and realize that exceptional model accuracy may not deliver business value. </li> </ul>

Despite the growing enthusiasm for DSML's core technologies, getting results from its strategies is elusive for enterprises.

startup

Startups

How to build a data science and machine learning roadmap in 2022

🕐 <a href="https://hyperskill.org/learn/step/16430">Factory method </a>is a pattern that creates an interface or abstract class that also allows for modification of object creation in subclasses. It is best used when the developer doesn’t know beforehand the exact types and dependencies of the objects the code should work with.

🕐 <a href="https://hyperskill.org/learn/step/16469">Singleton </a>is a pattern that creates only one instance of an object while providing a point of global access to this instance. This pattern should be implemented when a class in the program has only a single instance available to all clients.

🕐 <a href="https://hyperskill.org/learn/step/16430">Prototype </a>is a pattern based on the concept of copying an existing object for creating a new one. It is used to reduce the number of subclasses that differ only in the way they initialize their objects.

🕐 <a href="https://hyperskill.org/learn/step/16735">Builder </a>is a pattern that lets developers design complex objects by using simple objects via a step-by-step approach. This allows creating different modified variations of the same object.

🕐 <a href="https://hyperskill.org/learn/step/17108">Abstract factory </a>is a pattern that produces sets of related objects. In its simplest form, it could be a set of factories that allows developers to produce parts of object sets.

Today marks the last day of Hour of Code at JetBrains Academy!

product

Product & Design

Learn in 1 Hour: Design Patterns | The JetBrains for Education Blog

Predicting the movement of stock prices is an alluring challenge with the promise of riches. Unfortunately, predicting future stock prices consistently and reliably is generally considered impossible. However, we can use models to make useful predictions, manage risk, and profit probalistically .

The options casino

Monthly simple and logarithmic returns of IBM stock with fitted normal distributions. We observe more returns at the extremities of the distributions than the normal distribution would predict. We call these 'fat tails' or 'excess kurtosis'. This image has been taken from 'Analysis of Financial Time Series, 3rd Ed.' by Ruey S. Tsay.

Normal Distribution

<a href="https://stats.libretexts.org/Bookshelves/Probability_Theory/Probability_Mathematical_Statistics_and_Stochastic_Processes_(Siegrist)/18%3A_Brownian_Motion/18.01%3A_Standard_Brownian_Motion">Brownian motion </a>describes the motion of a particle in a fluid or gas. Such a particle bounces around “randomly” within the fluid surrounding it. The path it creates appears quite noisy and random. It can essentially be considered a limiting form of the random walk where both the time between steps and the step length approach zero (with some caveats: namely, if the time step is, the step length must be- this produces a process whose variance scales exactly with time). Consider flipping a coin to determine whether to take a step forward or backward. Now let the time between steps approach zero.<warningtag> </warningtag>

Brownian motion

Monte-Carlo simulation is a statistical technique inspired by the casinos of Monaco. Much like gamblers resigning their fates to probability, we hand over the results of statistical analysis to chance. By running enough trials, we can make conclusions with statistical significance.Consider evaluating a call option with a strike prices of $105 for this stock. What would be the expected value of the option at expiry, given a geometric Brownian model for the stock’s movement?

Option Pricing

Monte-Carlo simulation of terminal values is a relatively simple simulation, and one can probably be completed analytically. The true power of Monte-Carlo simulation is unlocked when analysing scenarios that are more difficult to solve analytically, if not impossible. For example, if we wanted to analyse an American-style option, which can be exercised anytime, we might want to count the probability of a stock price exceeding the strike price at any time during the lifetime of that option. We could do this by counting how many trials cross the strike price boundry.

More Advanced Scenarios

moneyandinvestments

Money & Investments

Monte Carlo Options Pricing 

Do data scientists perform predictive analytics? Absolutely . Do non-data scientists also? Sure . However, if I were to put data scientist on one end of the predictive analytics see-saw, and on the other end, I would expect the data scientist would always hit the ground.

1. Predictive Analytics Mindset

<ul><li>"How can we increase sales?" </li><li>"Why is churn higher in some months than in others?" </li><li>"Why does this need to be done like that ?" </li><li>"What happens if we do X to Y?" </li><li>"How does X play into what happens over here?" </li><li>"Have we tried...?" </li><li>And so on... </li> </ul>

2. Curiosity

It is the data scientist's job to know as much about their relevant systems as possible, and leverage their curiosity and predictive analytical mindset to account for as much of these systems' operations and interactions as feasible, in order to keep them running smoothly even when being tweaked. If you aren't able to appreciate why no one person is able to fully explain how the economy works, data science is not for you.

3. Systems Thinking

Of course, we need the technical, statistical, and additional skills to be able to follow up on these questions, but these skills are useless if we don't have the creativity to think of interesting and non-obvious ways to be able to investigate and ultimately provide answers. This is why data scientists must inherently be creative.

4. Creativity

This storytelling is not like fictional storytelling; it's more like "fancy explaining," or providing an intuitive explanation tailored to the listener. You wouldn't tell a five year old a Stephen King story a bedtime, just like you wouldn't delve into a dry, long-winded narrative about supply chain metrics to someone working in research and development. Be aware of your audience.

5. Storytelling Sensibilities

I've put some thought into this, and come up with the 5 characteristics of a what I believe define a successful data scientist. Do you agree?

Teach (and learn) the Fourier transform geometrically!

Deep Dive into Event-Driven Architecture

Apache Airflow in Data Engineering

Recursion

Sanic and Data Validation using Pydantic

Mausam Adhikari