AI and ML for Coders in PyTorch, Moroney L., 2026

AI and ML for Coders in PyTorch, Moroney L., 2026.
        
   Before we get into the ins and outs of ML, let’s consider how it evolved from traditional programming. We’ll start by examining what traditional programming is, then consider cases where it is limited. Then we’ll see how ML evolved to handle those cases, and as a result has opened up new opportunities to implement new scenarios, unlocking many of the concepts of artificial intelligence.




Recognizing Clothing Items.
For our first example, let’s consider what it takes to recognize items of clothing in an image. Consider, for example, the items in Figure 2-1.

There are a number of different clothing items here, and you can recognize them. You understand what is a shirt, or a coat, or a dress . But how would you explain this to somebody who has never seen clothing? How about a shoe? There are two shoes in this image, but how would you describe that to somebody? This is another area where the rules-based programming we spoke about in Chapter 1 can fall down. Sometimes it’s just infeasible to describe something with rules.

Of course, computer vision is no exception. But consider how you learned to recognize all these items—by seeing lots of different examples, and gaining experience with how they’re used. Can we do the same with a computer? The answer is yes, but with limitations. Let’s take a look at a first example of how to teach a computer to recognize items of clothing, using a well-known dataset called Fashion MNIST.

Contents.
Chapter 1: Introduction to PyTorch (available).
Chapter 2: Introduction to Computer Vision (available).
Chapter 3: Going Beyond the Basics: Detecting Features in Images (unavailable).
Chapter 4: Using Public Datasets with PyTorch (unavailable).
Chapter 5: Introduction to NLP (unavailable).
Chapter 6: Making Sentiment Programmable Using Embeddings (unavailable).
Chapter 7: RNNs for NLP (unavailable).
Chapter 8: Using ML to Create Text (unavailable).
Chapter 9: Understanding Sequence and Time Series Data (unavailable).
Chapter 10: Create Models to Predict Sequences (unavailable).
Chapter 11: Convolutional and Recurrent Method for Sequences (unavailable).
Chapter 12: Concepts of Inference: Inputs and Outputs to Models (unavailable).
Chapter 13: Hosting your PyTorch Models for Serving (unavailable).
Chapter 14: Using 3P Models: Registries and Hubs (unavailable).
Chapter 15: Transformers and Transformers (unavailable).
Chapter 16: Using LLMs with Custom Data (unavailable).
Chapter 17: Serving LLMs with Ollama (unavailable).
Chapter 18: Introduction to RAG (unavailable).
Chapter 19: Using Generative Image Models with HF Diffusers (unavailable).
Chapter 20: Tuning Generative Image Models with LoRA (unavailable).



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Теги: учебник по программированию :: программирование :: Moroney