Ever found yourself scratching your head, trying to figure out why someone would invest in ether (ETH) instead of just using it on the Ethereum network? Let’s look at Fidelity’s recent report on Ethereum’s Investment Thesis. Ethereum vs Ether Ethereum vs. Ether: Picture Ethereum as a bustling digital city, and ether (ETH) as the currency … Read more
When working with Python, you might occasionally need to convert your Python script to an executable file to run on machines without Python installed. While PyInstaller is a popular choice for creating .exe files, you may be looking for alternative solutions for your project. One such alternative is py2exe, a Python Distutils extension that can … Read more
An autoencoder is a neural network that learns to compress and reconstruct unlabeled data. It has two parts: an encoder that processes the input, and a decoder that reproduces it. While the original transformer model was an autoencoder with both encoder and decoder, OpenAI’s GPT series uses only a decoder. In a way, transformers are … Read more
Price is a function of supply and demand. Increase demand and price goes up. Increase supply and price goes down. Bitcoin has a fixed supply of 21 million coins forever. So we don’t need to worry about supply, it is 100% predictable and limited to 21,000,000 BTC in the year 2140. With fixed supply, the … Read more
Deep learning has revolutionized various fields, including image recognition and natural language processing. Two prominent architectures have emerged and are widely adopted: Convolutional Neural Networks (CNNs) and Transformers. CNNs and Transformers differ in their architecture, focus domains, and coding strategies. CNNs excel in computer vision, while Transformers show exceptional performance in NLP; although, with the … Read more
TL;DR: Transformers process input sequences in parallel, making them computationally efficient compared to RNNs which operate sequentially. Both handle sequential data like natural language, but Transformers don’t require data to be processed in order. They avoid recursion, capturing word relationships through multi-head attention and positional embeddings. However, traditional Transformers can only capture dependencies within their … Read more
The multiplication operator (*) allows you to repeat a given string a certain number of times. However, if you want to repeat a string to a specific length, you might need to employ a different approach, such as string slicing or a while loop. For example, while manipulating strings in Python, you may need to … Read more
I just realized that the link to my doctoral thesis doesn’t work, so I decided to host it on the Finxter blog as a backup. Find the thesis here: 🔗 PDF Download link: https://blog.finxter.com/wp-content/uploads/2023/09/dissertation_christian_mayer_distributed_graph_processing_DIS-2019-03.pdf Here’s the abstract: 💡 Abstract: Distributed graph processing systems such as Pregel, PowerGraph, or GraphX have gained popularity due to their … Read more
Metcalfe’s law states that the value of a network (V) is proportional to the square of the number of connected users of the system (N²). In this article, I’ll develop a Bitcoin price prediction (V) in the year 2030 based on the average growth rate of the number of Bitcoin nodes (N). General Intuition Metcalfe’s … Read more
As you dive into the crypto ecosystem, you may come across Polygon (MATIC) and Ethereum (ETH), two popular and interconnected projects. What’s the relationship of those two projects and tokens? Polygon, formerly known as Matic Network, is an interoperability and scaling framework designed for building Ethereum-compatible blockchains. Its native token, MATIC, serves multiple purposes, including … Read more
I’ve read the expression of large language models (LLMs) being “Money Laundering for Copyrighted Data” on Simon Willison’s blog. In today’s article, I’ll show you which exact training data sets open-source LLMs use, so we can gain some more insights into this new alien technology and, hopefully, get smarter and more effective prompters. Let’s get … Read more
Python Multiprocessing Fundamentals 🚀 Python’s multiprocessing module provides a simple and efficient way of using parallel programming to distribute the execution of your code across multiple CPU cores, enabling you to achieve faster processing times. By using this module, you can harness the full power of your computer’s resources, thereby improving your code’s efficiency. To … Read more