REDX WeSchool

Wonderful session on Cyber Security conducted by Mr. Tamaghna Basu yesterday! ✨

Missed it? Watch it soon on our YouTube channel - REDX WeSchool!

Here are 4 podcasts we recommend personally to help you learn something new today!

The wait is over! 🤩
Mark your calendars for our next session on Innovations and Machine Learning Algorithms in Cyber Security with Mr. Tamaghna Basu, Co-Founder & CEO, DeTaSECURE.

Catch the event Live on Welingkar institute’s official YouTube channel!
(WeSchool students can join in live through Zoom and also get a chance to interact with our speaker!)✨

See you there!

All that’s been happening in the Tech world! ✨

This World Environment Day let’s pledge to build a better tomorrow! 🌏🌱

#2022

Although, Stranger Things may seem all fiction but is it really?

Read about the Montauk’s Camp Hero and how it may actually have influenced the famous Netflix series! 😮

A company has a data moat if its access to data makes it difficult for competitors to enter its business. Moat is a common business term used to evoke the water-filled moats built around castles to make them easier to defend against attackers. For example, if a self-driving car company can acquire far more data than its competitors to train and test its system, and if this data makes a material difference in the system’s performance, then its business will be more defensible.

🟠 A data moat may not do much to protect an AI business if:

● System performance plateaus with more data. Say you're building a general-purpose speech recognizer, and human-level performance is 95 percent accurate. Collecting enough data to achieve 94 percent accuracy is hard, and getting incrementally more

data will have diminishing returns. In fact, it’s much easier for a competitor to improve
from 90 to 91 percent accuracy than for you to improve from 94 to 95 percent.

● Data doesn’t change over time. If the mapping from input x to output y remains the
same (as in speech recognition, where the input spoken words “The Batch” will continue to map to their text equivalents for a long time), competitors will have time to accumulate data and catch up.

● The application can be built with a smaller dataset thanks to new data-centric AI development technologies, including the ability to generate synthetic data, and tools that systematically improve data quality.

🟢 Given these circumstances, data can make an AI business more defensible if:

● Performance keeps improving materially within the range of dataset size that a company and its competitors can reasonably amass.

● The data distribution varies significantly over time. In this case, access to an ongoing stream of fresh data is critical for keeping the machine learning model current, which in turn earns further access to the data stream.

● The market has winner-take-all dynamics, and users have low switching costs. When a market supports only one leader, access to data that delivers even marginally better performance can be a major advantage.

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What is Web 3.0? 🤔

Here’s what you need to know about it. 🔝

Let us know your thoughts in the comments below👇🏼

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Transfer learning will be the next driver of machine learning’s commercial success after supervised learning. 💡

Using State-of-the-Art Pre-trained Neural Network Models we can tackle Computer Vision Problems with Transfer Learning.🧬

Here are a few pre-trained networks you can use for computer vision tasks such as ranging from image generation, neural style transfer, image classification, image captioning, anomaly detection, and so on. 🤩

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All that you need to know about the latest news in Tech! 🔥

Types of Neural Networks, its uses and advantages & disadvantages curated all in one place for you! 🫶🏽🤩

Source: geeksforgeeks.org
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