I’m Kibrom, and this blog is my learning diary, driven by a passion for using AI to solve real-world problems. As a Lead Data Scientist on a Chest X-ray project and a Tech Manager in banking, I optimize data solutions to improve outcomes. Join me on this adventure, where we become masters of AI and make a real difference!
In today's digital age, computer literacy is a basic requirement for most people. Centuries ago, people relied on oral communication to pass on information from one generation to the next. The ability to read and write was a privilege reserved for the elite few because language literacy was not considered an essential skill. Those who were literate were often looked up to and held in high regard. This created a stark division between the literate and the illiterate, with the latter relying on the former for even the most basic communication needs. It wasn't until several centuries later that society began to see the value in literacy for all... ....
Autoencoders are a type of neural network that can be used to learn a compressed representation of input data. They work by training the network to reconstruct the input data from a lower-dimensional latent representation, which is typically obtained using an encoder. Autoencoders are versatile and can be used for a variety of tasks, including data compression, anomaly detection, and feature learning.. ....
Are you tired of making predictions based on correlation rather than causation? Introducing Causality in Machine Learning, a cutting-edge approach to understanding the underlying causes of complex data patterns. By incorporating causal inference techniques, we can gain a deeper understanding of how different variables interact and affect each other, leading to more accurate predictions and informed decision-making.
As we enter the digital age, one thing is certain: technology is not just a trend, it's a game-changer. With each passing day, technology is making its way into more and more industries, bringing about a variety of benefits for society such as increased efficiency, improved communication, and access to information. But, with change comes challenges, and one of the biggest concerns is the displacement of jobs. Neural tangent kernel (NTK) (Jacot et al. 2018) is a kernel to explain the evolution of neural networks during training via gradient descent....
Overfitting is often perceived as a major challenge in DNNs, leading to a lack of confidence in their ability to generalize to new data. As Neal Shusterman, the author of "Unwind", once wrote: "But remember that good intentions pave many roads. Not all of them lead to hell." However, the reality is that the severity of overfitting in DNNs is often overstated and can be effectively mitigated through various techniques.....