Let’s look at one more interesting thing. I mentioned before that one of the purposes of this example was to examine and visualize our eigenfaces, as they are called: our super columns. I will not disappoint. Let’s write some code that will show us our super columns as they would look to us humans: def […]
Put another way, the COD states that as we increase the number of feature columns, we need exponentially more data to maintain the same level of model performance. This is because, in high-dimensional spaces, even the nearest neighbors can be very far away from a given data point, making it difficult to create good predictions. […]
Different types of TL Let’s take a moment to get familiar with the diverse landscape of TL. It’s not a single monolithic idea, but rather a collection of varied strategies that fall under one umbrella term. There’s a type of TL for just about every scenario you might come across. Inductive TL First up, we […]
Understanding algorithmic bias Algorithmic bias is a pivotal issue in the world of ML. It occurs when a system, intentionally or not, generates outputs that are unfair or systematically prejudiced toward certain individuals or groups. This prejudice often originates from the fact that these systems learn from existing data, which itself can be riddled with […]
Mitigating algorithmic bias Even after understanding and measuring bias in ML, the job is only half done. The next logical step is to implement strategies for mitigating bias. Various techniques exist, each with its strengths and weaknesses, and a combination of these strategies can often yield the best results. Here are some of the most […]
Mitigation during model in-processing So, we’ve done our best to clean up our data, but what about when we’re training our model? Can we do something there too? Absolutely! The model in-processing stage allows us to bake fairness right into the training process. It’s a bit like adding spices to a dish as it cooks, […]
Emerging techniques in bias and fairness in ML When it comes to the world of tech, one thing is certain – it never stands still. And ML is no exception. The quest for fairness and the need to tackle bias has given rise to some innovative and game-changing techniques. So, put on your techie hats, […]
Implementing feedback systems Feedback isn’t just for Amazon reviews or post-workshop surveys. In the world of ML, feedback systems can act as our reality checks. They can help us understand whether our model’s predictions align with the evolving real world. Feedback can come from various sources, such as users flagging incorrect predictions or automated checks […]
Data management: crafting the bedrock At the heart of a compelling data governance strategy lies proficient data management. This sphere encompasses the meticulous aggregation, fusion, orchestration, and retention of trustworthy datasets, acting as a catalyst for businesses to harness maximum value. With the contemporary business landscape rapidly evolving, an organization’s merit hinges significantly on its […]
Data integration – from collection to delivery Data, in its raw form, originates from a myriad of sources, demanding integration to unlock its full potential. The integration might unfurl in batch sequences or be streamed in real time, aiming for prompt insight derivation. But integration isn’t a standalone task—it demands orchestrated actions to curate, ingest, […]
