Deep Learning 101: How to Train Your AI to Slay ๐ค
Learn how to speed up training of language models with the latest techniques and optimizers. From Adam to sequence length scheduling, we've got you covered.
Deep Learning 101: How to Train Your AI to Slay ๐ค
Optimizers for Training Language Models: Donโt Be a Noob, Use the Basics ๐ค
When it comes to training language models, optimizers are like the secret sauce that makes your AI slay. And, lowkey, itโs giving me major feels for Adam, the OG optimizer. But, letโs get real, there are other optimizers out there, like Adagrad, RMSProp, and Nadam.
Each has its own strengths and weaknesses, so you gotta choose the one thatโs right for your model.
The Main Character Energy: Adam Optimizer
Adam is still the most popular optimizer for training deep learning models. Itโs like the Beyoncรฉ of optimizers โ itโs been around for ages, but it still slays. With its ability to adapt to changing learning rates, Adam is the go-to choice for many researchers and developers.
Learning Rate Schedulers: Donโt Be Afraid to Reduce the Noise ๐ง
Learning rate schedulers are like the volume controllers of your AIโs learning process. They help you adjust the learning rate over time to prevent overfitting and underfitting. And, letโs be real, nobody likes a noisy AI.
The Best Kept Secret: Learning Rate Schedulers
Learning rate schedulers are not as widely discussed as optimizers, but theyโre just as important. With the right scheduler, you can prevent overfitting and underfitting, which means your AI will be way more accurate.
Sequence Length Scheduling: Donโt Be a Stranger to Context ๐ค
Sequence length scheduling is like the context menu of your AIโs learning process. It helps you adjust the sequence length over time to prevent overfitting and underfitting. And, letโs be real, context is key.
The Context is Everything: Sequence Length Scheduling
Sequence length scheduling is not as popular as other techniques, but itโs just as effective. By adjusting the sequence length over time, you can prevent overfitting and underfitting, which means your AI will be way more accurate.
Other Techniques to Help Training Deep Learning Models: Donโt Worry, We Got You Covered ๐ค
Other techniques like weight decay, gradient clipping, and early stopping can also help you train your AI more efficiently. And, letโs be real, nobody likes a stuck AI.
The Ultimate Cheat Code: Weight Decay
Weight decay is like the cheat code of deep learning. It helps you prevent overfitting by adding a penalty term to the loss function. And, letโs be real, who doesnโt love a good cheat code?
Originally reported by ML Mastery
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