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 […]
Our goal is to predict whether or not a person gave a 5- or 1-star review based on the words they used in the review. Let’s set a baseline with logistic regression and see how well we can predict this binary category:from sklearn.linear_model import LogisticRegression lr = LogisticRegression()X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=100) […]
Decoding BERT’s pre-training One of the most impressive feats of TL can be observed in BERT, a pre-trained model that revolutionized the NLP landscape. Two fundamental training tasks drive BERT’s robust understanding of language semantics and relationships: masked language modeling (MLM) and next sentence prediction (NSP). Let’s break them down and see how each one […]
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 […]
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 […]
Measuring bias To successfully combat bias, we must first measure its existence and understand its impact on our ML models. Several statistical methods and techniques have been developed for this purpose, each offering a different perspective on bias and fairness. Here are a few essential methods: These methods offer diverse perspectives on measuring bias and […]
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, […]
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 […]
Mitigating drift The world of ML is ever-evolving, making it crucial for us to remain adaptable. We’ve seen how the concept of drift is integral to understanding changes in our data or model over time. But what can we do when faced with these shifting sands? Are we merely left to witness the disintegration of […]
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 […]
