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Enhancing Autoencoders with memory modules for Anomaly Detection.
In this article, we'll go through how to detect anomalies in video feeds and how contemporary work use Memory modules to improve performance. 1
As we noted, a wel-annotated and comprehensive dataset is extremely hard to build for anomaly detection. 1
This is why unsupervised learning is used for Anomaly Detection, i.e., Deep Autoencoders. 1
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Automate your Text Processing workflow in a single line of Python Code
Various steps in the pipeline include text cleaning, tokenization, stemming, encoding to numerical vector, etc followed by model training. 1
The dataset derived for NLP task is textual data, mainly derived from the internet. 1
Most of the time, the textual data used for NLP modeling is dirty and needs to be cleaned in the early stage of data processing. 1
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Teaching a Neural Network to Play Cards
I started by implementing the framework for the game in Python, where four simulated players can play against each other. 1
Initially, lacking a trained model, the players select cards randomly, out of the choices that are permitted by the rules. 1
Each game has 8 rounds, but in the last round, with only one card left, there is really no decision to be made. 1
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Man is to Computer Programmer as Woman is to Homemaker
Thus, there is implicit gender bias in the Google news articles that word embedding model identifies and potentially exaggerates. 1
The paper discusses how the gender bias in the trained data was evaluated and proposed methods to remove or minimize the gender bias. 1
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Three Good Books to Teach You Data Skepticism
This approach rests on some key questions: 1) Are the data trustworthy in the first place? 1
2) Are the findings you collect meaningful? 1
3) What are the consequences if you come to the wrong conclusions? 1
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The 7 Tasks in Data Science Management
by Martin Schmidt and Marcel Hebing An ever increasing number of use cases for data science are evolving in most companies from nearly every sector.
4h ago 
from Medium
How Data is Fueling & Accelerating Digital Transformation
A recent report from Twilio found that the global pandemic has sped up digital transformation strategies at 97% of companies.
Stats essentials for data science
Photo by Artem Maltsev on Unsplash Whether you're spending all day in spreadsheets or TensorFlow, being an effective data scientist requires a solid understanding of statistics fundamentals.
A Complete Free Course on Inferential Statistics for Data Scientists in R
Photo by Morgan Sessions on Unsplash It is very important to learn statistics well for data scientists.
Learning visualization tools and data manipulation tools are great!
5h ago 
from IT PRO
How to become a machine learning engineer | IT PRO
Machine learning engineers are some of the most highly sought after professionals in the IT industry, and this will only continue to grow as more companies adopt artificial intelligence (AI) technologies.