Step by Step
F
Frameworks — the software layer for building models
PyTorch is the dominant framework in both research and industry; TensorFlow/Keras (built by Google) is common in production deployment pipelines.
Example: a research paper releasing new model code will almost always release it in PyTorch first.
H
Hardware — what the models actually run on
NVIDIA GPUs (using CUDA) are the industry standard for training, Google has its own TPUs (Tensor Processing Units), and Apple Silicon chips increasingly run smaller models locally on consumer devices.
Example: training a large language model from scratch typically requires thousands of NVIDIA GPUs running for weeks.
O
Open-source models — pretrained models anyone can use
The Hugging Face Hub hosts 500,000+ pretrained models, including well-known ones like BERT, Llama, and Stable Diffusion, that developers can download and fine-tune instead of training from scratch.
Example: instead of training an image generator from zero, a developer can download a pretrained Stable Diffusion checkpoint from Hugging Face and fine-tune it on their own images.
T
Tools / Libraries — supporting the ML workflow
scikit-learn handles classical (non-deep-learning) ML algorithms, NumPy and Pandas handle data manipulation, and Matplotlib handles visualization — the everyday supporting cast around any ML project.
Example: a data scientist might use Pandas to clean a dataset, scikit-learn to train a simple classifier, and Matplotlib to plot the results — all before ever touching a neural network.
Applied Walkthrough
1
A beginner wants to learn core ML concepts like classification and regression without deep learning complexity.
2
The right starting tool is scikit-learn — it's built specifically for classical ML fundamentals with a simple, consistent API.
3
Once they're ready to move into deep learning and want access to existing pretrained models rather than training from scratch, PyTorch plus Hugging Face becomes the natural next step.
4
This progression — scikit-learn first, then PyTorch + Hugging Face — is the standard recommended learning path in the field today.
Exam Application
Exams testing this topic usually ask you to match a tool to its category (framework vs. hardware vs. model repository vs. library) or to identify the appropriate tool for a described beginner vs. advanced use case. Know that PyTorch and TensorFlow are frameworks (not model repositories), Hugging Face is a model repository (not a framework), and scikit-learn is for classical ML, not deep learning.
⚠ Common Trap
The most common trap is confusing a framework (PyTorch, TensorFlow — used to build and train models) with a model repository (Hugging Face Hub — used to download other people's pretrained models). You can use PyTorch to load a model downloaded from Hugging Face; they serve different purposes and are not competitors or substitutes for each other.
✓ Quick Self-Check
1. Which framework is considered dominant in AI research today?
PyTorch.
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2. What company built TensorFlow, and where is it most commonly used?
Google — it's especially common in production deployment pipelines.
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3. What does the Hugging Face Hub provide?
Access to 500,000+ pretrained open-source models, such as BERT, Llama, and Stable Diffusion.
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4. Which library is best suited for classical machine learning, as opposed to deep learning?
scikit-learn.
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5. Is Hugging Face a framework or a model repository?
A model repository — it hosts pretrained models, while PyTorch and TensorFlow are the frameworks used to build and run them.
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