Data Science & AIDeep LearningIntermediateTrending
Deep Learning with PyTorch
Build neural networks from tensors upward, then train them properly.
4.8(347 ratings) 68 lessons · 9 modules 24h2.0k students
PN
What you'll learn
- Understand autograd rather than treating it as magic
- Write custom training loops you can debug
- Diagnose overfitting, underfitting and unstable loss curves
- Train and evaluate vision and text models end to end
About this course
Start from tensors and gradients, build a network by hand, then move to training loops, regularisation, learning-rate schedules and debugging. You finish by training a vision model and a text model and evaluating both honestly.
Curriculum
9 modules · 68 lessons · 24h
The detailed curriculum for this course is being finalised. The outline covers 68 lessons across 9 modules.
Requirements
- Python proficiency
- Undergraduate-level linear algebra
Who this course is for
- Data scientists moving into deep learning
- ML engineers formalising their practice
Student reviews
No written reviews yet. The course has 347 ratings with an average of 4.8 out of 5.
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