This Week in AI
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AI Legislation in the EU, Drug Discovery, and Robotic Pizza Deliveries - This Week in AI
Welcome to our This Week in AI roundup. This week we have stories about new AI rules in the EU, AI for drug discovery, and robotic pizza deliveries.…
Generative Models
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Introduction to Conditional GANs (cGANs) & Controllable Generation
In this guide, we discuss two types of GANs that allow you to control the output of the model: conditional GANs (cGANs) and controllable generation.…
This Week in AI
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Enterprise AI Trends, Top 100 AI Companies, and Deep Learning Papers - This Week in AI
Welcome to our This Week in AI roundup. This week we have stories about enterprise AI trends, the top 100 AI companies, deep learning papers, and more.…
Generative Models
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Introduction to Wasserstein GANs with Gradient Penalty
In this article, we discuss the Wasserstein loss function for Generative Adversarial Networks (GANs), which solves a common issue that arises during the training process.…
Python for Finance
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Python for Finance: Portfolio Optimization
In this guide, we discuss portfolio optimization with Python. Topics covered include the Sharpe ratio, portfolio allocation, and portfolio optimization.…
This Week in AI
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Universal Basic AI Income, GPT-3, and Nanoparticles - This Week in AI
Welcome to our first edition This Week in AI roundup. This week we have stories about universal basic AI income, GPT-3, synthetic data, and nanoparticles.…
Generative Models
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Introduction to Deep Convolutional GANs (DCGANs)
In this article, we discuss the key components of building a DCGAN for the purpose of image generation. This includes activation functions, batch normalization, convolutions, pooling and upsampling, and transposed convolutions.…
Generative Models
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Introduction to Generative Adversarial Networks (GANs): Intuition & Theory
Generative Adversarial Networks, or GANs, are an emergent class of deep learning that have been used for everything from creating deep fakes, synthetic data, creating NFT art, and more.…
SQL
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SQL for Data Science: Subqueries and Joins
In this article on SQL for data science, we discuss how to merge and combine data from multiple sources using subqueries and joins.…
SQL
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SQL for Data Science: Filtering, Sorting, and Calculating Data
In this article, we discuss how to filter, sort, aggregate, calculate, and group data with SQL.…