Faster Training of Large Language Models with Parallelization
Implementing Faster Training of Large Language Models with Parallelization. Learn about key parallelism techniques for GPUs and TPUs to accelerate training.
Tag Archives: Optimization
Optimizers explained for training Neural Networks
Overview Training a Deep Learning model (or any machine learning model in fact) is all about bringing the model predictions (model output) close to the real output(Ground truth) for a given set of input-output pairs. Once the model’s results are close to the real results our job is done. To understand how close model predictions are with respect… Read More »
Optimizing TensorFlow models with Quantization Techniques
Deep Learning models are great at solving extremely complex tasks efficiently but this superpower comes at a cost. Due to a large number of parameters, these models are typically big in size(memory footprint) and also slow in the inference (during predictions). Slow and heavy models are not much appreciated when it comes to the deployment part. As we… Read More »