1. Scikit-Learn: The Classic

Scikit-learn remains the popular for traditional machine learning (SVMs, decision trees, random forests, etc.). Its strengths:

• Simple API: You can train a model in a few lines of code.
• Rock-solid community: It’s been around for years and is well-maintained.
• Great for small-to-mid datasets: If you’re not doing deep learning, this is still a top choice.

When NOT to Use Scikit-Learn

• Deep learning: It lacks native support for neural networks.
• Big Data: It struggles with datasets that don’t fit in memory.

2. TensorFlow 2.0: Now Actually Usable

TensorFlow was once the deep learning cool-kid but had a horrible API. In 2020, the TensorFlow 2.0 made a nice jump:

• Eager execution by default: No more annoying computational graphs.
• Integrated Keras: Finally, deep learning without 50 lines of boilerplate.
• Better debugging: Debugging TensorFlow used to be a nightmare; now it’s much more intuitive.

When to Use TensorFlow

• Production-ready deep learning (Google-scale stuff).
• Massive datasets and distributed training.
• TensorFlow Extended (TFX) for ML pipelines.

3. PyTorch: The Deep Learning Darling

PyTorch is very popular for deep learning research, and for good reasons:

• Super intuitive API: It feels like native Python.
• Dynamic computation graphs: No need to define everything before execution.
• Perfect for researchers: Most ML papers use PyTorch.

When NOT to Use PyTorch

• Production deployment: TensorFlow still dominates here.
• Automated ML Pipelines: PyTorch lacks mature tools like TFX.

4. JAX: The Rising Star

JAX, from Google, is a NumPy on steroids:

• Auto-differentiation: Like TensorFlow, but cleaner.
• Insanely fast on GPUs/TPUs.
• Great for research: Many cutting-edge projects now use JAX over TensorFlow.

Should You Use JAX?

• If you’re into deep learning research, YES.
• If you need production-ready ML, stick with TensorFlow or PyTorch.

5. FastAI: Deep Learning for Humans

Built on PyTorch, FastAI makes deep learning easier:

• Great for beginners.
• Fewer lines of code to build complex models.
• Comes with pre-trained models.

Who Should Use FastAI?

• Beginners who don’t want to deal with TensorFlow/PyTorch directly.
• Developers looking for rapid prototyping.

6. H2O.ai: AutoML Made Easy

H2O.ai focuses on automating machine learning:

• H2O AutoML: Automated feature engineering + model selection.
• Handles big data.
• Great for enterprises.

When to Choose H2O.ai?

• If you need Automated ML without much coding.
• If you work with big datasets that won’t fit in RAM.

Final Verdict: Which One Should You Use?

References

1. Scikit-Learn Official Docs
2. TensorFlow 2.0 Guide
3. PyTorch Documentation
4. JAX Documentation
5. FastAI Course
6. H2O.ai AutoML