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Abstract cover artwork for Deep Learning with PyTorch: Fundamentals to Fine-Tuning, a Deep Learning course Deep Learning
Deep Learning intermediate

Deep Learning with PyTorch: Fundamentals to Fine-Tuning

Autograd from first principles, then real training loops: learning-rate schedules, mixed precision, gradient accumulation, distributed data parallel, and the debugging discipline for a loss that will not go down. Finishes by fine-tuning a pretrained model on a custom dataset with a proper evaluation harness.

HT Hiroshi Tanaka 4.9

$99.00

22 hours

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Abstract cover artwork for Distributed Training with FSDP and DeepSpeed, a Deep Learning course Deep Learning
Deep Learning advanced

Distributed Training with FSDP and DeepSpeed

When a model no longer fits on one GPU. Data, tensor and pipeline parallelism, fully sharded data parallel, activation checkpointing, and the communication patterns that decide whether adding GPUs actually makes training faster.

HT Hiroshi Tanaka 4.7

$129.00

15 hours

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Abstract cover artwork for MLOps: Shipping Models That Survive Contact With Users, a Machine Learning course Machine Learning
Machine Learning advanced

MLOps: Shipping Models That Survive Contact With Users

Experiment tracking, model registries, reproducible training pipelines, shadow deployments, drift detection and automated rollback. You will build a pipeline where a retrained model cannot reach production without passing evaluation gates, and where a bad deploy is detected in minutes rather than quarters.

AR Ahmed Rahal 4.7

$119.00

20 hours

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