PyTorch
What it is: Open source deep learning framework from Meta with dynamic computation graphs and Pythonic API favored by researchers.
What It Does Best
Research and experimentation. Dynamic computation graphs mean models can change during runtime. Debug with standard Python tools - breakpoints, print statements work naturally.
Pythonic and intuitive. Feels like NumPy with GPU support. If you know Python, PyTorch feels natural. Clean, readable code that's easy to understand and modify.
Cutting-edge research. Most new research papers use PyTorch. Latest architectures available in community implementations immediately. The de facto standard for academic research.
Key Features
Dynamic graphs: Computation graph built on-the-fly during forward pass
Autograd: Automatic differentiation for any Python code
TorchScript: Compile models for production deployment
Distributed training: Built-in multi-GPU and multi-node support
Rich ecosystem: PyTorch Lightning, Hugging Face, fast.ai integration
Pricing
Free: Open source (BSD license)
Commercial: No licensing costs for any use
Cloud: Free software, supported on all major clouds
When to Use It
✅ Research and prototyping deep learning models
✅ Learning deep learning from scratch
✅ Custom model architectures and experiments
✅ Following and implementing research papers
✅ Need flexibility and fine-grained control
When NOT to Use It
❌ Classical ML only (scikit-learn simpler)
❌ Need enterprise production serving (TensorFlow ecosystem more mature)
❌ Primary target is mobile/edge deployment
❌ Want high-level API only (Keras easier)
❌ Team has deep TensorFlow expertise already
Common Use Cases
Computer vision: CNNs for image classification, detection, segmentation
NLP models: Transformers, BERT, GPT implementations
Generative models: GANs, diffusion models, VAEs
Reinforcement learning: Deep RL algorithms and agents
Research prototypes: Novel architecture experimentation
PyTorch vs Alternatives
vs TensorFlow: PyTorch more intuitive, TensorFlow better production tools
vs Keras: PyTorch more flexible, Keras simpler for beginners
vs JAX: PyTorch easier to learn, JAX faster for some workloads
Unique Strengths
Dynamic graphs: Most flexible computation graph system
Research dominance: 70%+ of research papers use PyTorch
Pythonic design: Most natural Python deep learning API
Meta backing: Well-maintained with massive company support
Bottom line: The standard for deep learning research and the best framework for learning. More intuitive than TensorFlow with vibrant ecosystem. Choose PyTorch for flexibility, research, and modern deep learning. Production deployment has improved dramatically in recent years.