Daniel Bourke: PyTorch 101 Crash Course (2025)

• A comprehensive, chaptered YouTube course spanning fundamentals, data pipelines, modular training loops, CNNs, metrics, GPU usage, saving/loading, and production‑style structure; the timestamps cover end‑to‑end workflows from tensors to image datasets, TinyVGG, and modular scripts, ideal for "scratch to pro" progression.
• Strengths: very detailed code walkthroughs, device‑agnostic patterns, repeated exercises, and realistic projects that mirror real ML work; extensive timestamp index makes it easy to navigate and revisit topics during practice.

freeCodeCamp: Learn PyTorch in 5 Projects

• Project‑based, 5.8‑hour video guided by Omar M. Atef that builds intuition by shipping tabular classification, image classification with custom and pretrained models, audio classification, and BERT text classification, giving a broad modality tour early on.
• Strengths: fast ramp from syntax to applied tasks across CV, audio, and NLP; great if learning is best via shipping multiple small, real projects that expose core PyTorch APIs and common training/evaluation loops.

fast.ai: Practical Deep Learning for Coders (PyTorch‑first)

• A practical series that uses PyTorch as the foundation beneath the fastai library to build state‑of‑the‑art models quickly, while progressively exposing lower‑level PyTorch concepts; covers CV, NLP, tabular, collaborative filtering, deployment, and transfer learning.
• Strengths: production‑minded best practices and rapid prototyping; explicit discussion of PyTorch vs TensorFlow, optimizers like AdamW, and using cloud GPUs; perfect for moving from beginner to practitioner able to ship models end‑to‑end.

How to use them together

• Start with Daniel Bourke's fundamentals to master tensors, DataLoaders, training/eval loops, metrics, GPU/device‑agnostic code, and modular scripts; complete the exercises chapter by chapter for depth.
• Build breadth with the freeCodeCamp 5‑project course to apply the core skills across tabular, CV, audio, and BERT NLP, reinforcing API fluency and problem‑specific tips.
• Level up to practitioner by following fast.ai lessons for transfer learning, modern architectures, and deployment patterns, while peeking under the hood to the PyTorch layers as suggested in the course notes and lectures.

Related

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Best 3 Video Courses for Learning PyTorch (Scratch to Pro)

1. Daniel Bourke: PyTorch 101 Crash Course (2025)

• Link: PyTorch 101 Crash Course (YouTube)
• Description: Comprehensive, chaptered course covering:
• Fundamentals, tensors, DataLoaders
• Data pipelines, modular training loops, CNNs, metrics
• GPU usage, saving/loading, production-style structure
• Timestamps let you easily review key topics
• Strengths: Detailed code walkthroughs, repeated practice exercises, device-agnostic patterns, realistic ML projects, excellent for revisiting concepts

2. freeCodeCamp: Learn PyTorch in 5 Projects

• Link: Learn PyTorch in 5 Projects (YouTube)
• Description: 5.8-hour, project-based video by Omar M. Atef
• Tabular classification, image classification (custom/pretrained), audio classification, BERT NLP
• Covers multiple real-world modalities early
• Strengths: Rapid transition from syntax to applied tasks in CV, audio, NLP; emphasizes practical projects and API fluency

3. fast.ai: Practical Deep Learning for Coders

• Link: fast.ai Course | fast.ai YouTube
• Description: Series using PyTorch as the backbone for fastai library to build state-of-the-art models:
• Covers CV, NLP, tabular, collaborative filtering, deployment, transfer learning
• Explains lower-level PyTorch concepts and rapid prototyping
• Strengths: Industry-oriented best practices, PyTorch vs TensorFlow, optimizer details (AdamW), cloud GPU usage, everything for moving from beginner to deployable practitioner