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 […]
We have gathered 1,288 images of people’s faces, and each one has 1,850 features (pixels) that identify that person. Here’s the code we used – an example of one of our faces can be seen in Figure 11.22:plt.imshow(X[100].reshape((h, w)), cmap=plt.cm.gray) lfw_people.target_names[y[100]] ‘George W Bush’ Figure 11.22 – A face from our dataset: George W. Bush […]
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 […]
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 […]
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 […]
Example – TL for image classification We could take a pre-trained model such as ResNet or the Vision Transformer (shown in Figure 12.10), initially trained on a large-scale image dataset such as ImageNet. This model has already learned to detect various features from images, from simple shapes to complex objects. We can take advantage of […]
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 […]
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 […]
