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 […]
Feature extraction and PCA A common problem when working with data, particularly when it comes to ML, is having an overwhelming number of columns and not enough rows to handle such a quantity of columns. A great example of this is when we were looking at the send cash now example in our naïve Bayes […]
TL with BERT and GPT Having grasped the fundamental concepts of pre-trained models and TL, it’s time to put theory into practice. It’s one thing to know the ingredients; it’s another to know how to mix them into a delicious dish with them. In this section, we will take some models that have already learned […]
Types of bias Understanding bias in ML requires us to categorize it into different types based on its source and manifestation. This helps us to identify the root cause and implement targeted mitigation strategies. Here are the key types of bias to consider: Each of these types of bias has different implications for fairness in […]
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 […]
Uncovering bias in GPT-2 LLMs such as GPT-2 have revolutionized natural language processing (NLP) by producing high-quality, human-like text. While these models can be valuable tools, they are not without their challenges. One significant concern is the potential for bias in their outputs, which can result from biases in the training data. To understand this […]
Model drift Now, there are several types of model drift we should be aware of. Each tells a different tale of how our models can falter: To summarize, model drift is a challenge that reminds us of the ever-changing nature of data and user behavior. As we navigate these currents, understanding the types of drift […]
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 […]
Mastering data governance For countless organizations, data stands as a priceless treasure. Yet, it’s data governance that serves as the compass guiding one to extract the true worth of data. Envision data governance as a holistic amalgamation of principles, methodologies, and tools designed to oversee the entire life cycle of your data, ensuring it’s in […]
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 […]
