Choosing Hyperparameters: Learning Rate, Batch Size, Steps, and LR Schedulers
About this lesson
Are your models failing to converge or stuck in local minima? The difference between a good model and a state-of-the-art one often comes down to your hyperparameter choices. In this deep dive, we break down how to effectively tune the most impactful settings in neural network training. What you’ll learn in this technical guide: Learning Rate Mastery: Understand why it is the "master knob" of training, how to use a learning rate finder to identify the sweet spot, and typical starting ranges for Adam and SGD. Batch Size Trade-offs: Learn the relationship between batch size, GPU utilization, and generalization, and how to apply the "square root of 2" rule when scaling up. Schedulers & Warmup: Discover why "Cosine" and "Linear" schedulers are essential for modern transformers, and how adding a warmup phase can stabilize early training. Gradient Stability: Learn how to implement Gradient Clipping to prevent exploding gradients from crashing your training runs. Tuning Strategies: We provide a systematic approach to tuning—from starting configurations to using automated tools like Optuna for large-scale projects. Whether you are fine-tuning a transformer or training a CNN from scratch, this video provides the practical framework you need to get the best performance out of your hardware. Resources: Check the description for the full training loop code, reference tables for starting configurations, and links to the foundational research papers mentioned in this video! #DeepLearning #MachineLearning #HyperparameterTuning #AIEngineering #NeuralNetworks #PyTorch #DataScience #LLM #AIAcademy #ArtificialIntelligence #TechTutorial
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