I'm Kibrom (KB), a Project Lead Data Scientist in Medical Imaging AI, where a single algorithm can mean life or death, and a Tech Manager in Banking. My mission is to build human-centric AI that solves real problems. This blog is where I get real about the messy, brilliant, and frustrating journey of getting it right. Join me in making sense of the revolution.
Learn how coding AI has become the new literacy in the digital age. Master Python, machine learning, deep learning, and AI frameworks like TensorFlow and PyTorch. This comprehensive guide covers essential skills for becoming an AI programmer, from programming fundamentals to advanced neural network architectures including CNNs, RNNs, and GANs. Perfect for anyone looking to enter the field of artificial intelligence and machine learning...
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.....