TensorFlow vs PyTorch
Two frameworks dominate deep learning: TensorFlow (Google) and PyTorch (Meta). Both are powerful, but they have different design philosophies. This chapter helps you choose the right one.
TensorFlow (2.x + Keras)
- Production‑oriented: TensorFlow Serving, TF Lite for mobile, TF.js for browser.
- Keras as high‑level API (easy for beginners).
- Static graph by default (but eager execution available).
- Larger ecosystem and industry adoption.
PyTorch
- Research‑oriented: dynamic computation graphs (define‑by‑run).
- More Pythonic, easier to debug.
- Preferred in academia and many research labs.
- Strong support for custom layers and dynamic architectures.
Which One to Learn?
- For production and deployment at scale: TensorFlow.
- For research, prototyping, and flexibility: PyTorch.
- Both are valuable; many companies use both.
- Recommendation: start with PyTorch for learning (easier debugging), then learn TensorFlow if needed.
Code Comparison
PyTorch:
import torch
import torch.nn as nn
model = nn.Linear(10, 2)
x = torch.randn(5, 10)
y = model(x)import tensorflow as tf
model = tf.keras.layers.Dense(2, input_shape=(10,))
x = tf.random.normal((5,10))
y = model(x)Two Minute Drill
- TensorFlow: production, Keras, static graphs.
- PyTorch: research, dynamic graphs, Pythonic.
- Both are valid; choose based on your needs.
- Start with PyTorch for learning.
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