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A Study of Malicious Code in PyPI Ecosystem

Hacker News

We preliminarily classified these malicious code into five categories based on malicious behaviour characteristics. Our analysis revealed that 74.81% of all malicious packages successfully entered end-user projects through source code installation, thereby increasing security risks.

Python 130
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Know this before using iOS alternative app stores

Dataconomy

Interestingly, it has also been revealed that any alternative app marketplaces previously installed on a device will cease to function if the user spends an extended period outside the European Union. These alternative marketplaces offer users the ability to install apps on their iPhones, functioning alongside the traditional Apple App Store.

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Real-Time Sentiment Analysis with Kafka and PySpark

Towards AI

Install Java and Download Kafka: Install Java on the EC2 instance and download the Kafka binary: 4. This analysis helps us understand the common words linked to various sentiment categories. Launch EC2 instance: Create an EC2 instance with appropriate security group settings allowing inbound traffic on Kafka ports.

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US Army Physical Fitness Test: Normative Data on 6022 Soldiers (1994)

Hacker News

Subjects were 5346 male soldiers and 676 female soldiers from 14 Army installations in the United States. Data were separated into the age and gender categories on which the current APFT standards are based (17-21, 22-26, 27-31, 32-36, 37-41, 42-46, 47-51, >52 years).

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Create Your Own Data Analyst Assistant With Langchain Agents

Towards AI

Now, LangChain is the most advanced library for creating Agents, but Hugging Face has also joined the party with their Transformers Agents & tools, and even the ChatGPT plugins could fit into this category. Install and load required libraries. As always, we need to install libraries that are not available in the Colab environment.

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Image Classification Using R, Keras, and Comet ML

Heartbeat

Image classification is a computer vision task that allows algorithms to understand an image’s contents and assign one or more categories to the image. In this tutorial, you will use the Fashion MNIST dataset that contains 72K grayscale images of clothes belonging to 10 categories. Sign up for a free account to use Comet ML’s platform.

ML 52
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Navigating Transfer Learning with Comet

Heartbeat

train_gen = train_datagen.flow_from_dataframe(train_df, train_dir, x_col='filename', y_col='category', target_size=img_size, class_mode='binary', batch_size=batch_size) validation_datagen = ImageDataGenerator(rescale=1./255) Now, for this task, you will need a few things: A Comet account. Sign up here. A Python 3.9+