Tutorials in ML, AWS, R and Java
CLIP introduces a model that enables zero shot learning for a new dataset (in addition to a new example) by using NLP to supervise pre-training. i.e., To identify an object, you can provide the name or description of a new object that the model has not seen before. Traditionally a computer vision model was trained … Read more
Before we describe what Batch Normalisation is, here are a few introductory terms Internal covariate shift Stochastic Gradient Descent uses a minibatch of input to train the parameters of a layer. The input to a layer is the output from the previous layer. A change in the parameters of the previous layer causes a change … Read more
Why Distributed Machine Learning? In the previous article we looked at how GPGPU, ASICS, AWS’s Inferentia, the new NVidia A100 chip and other advances in hardware have tremendously improved the performance of Machine Learning training and inference. However the increase in the volume of data and the increasing complexity of the machine learning models require … Read more
Machine learning algorithms vary in size from a few parameters to a few billion parameters (e.g. GPT-3). The training data ranges from a few hundred training row to millions of rows. Training a model on a single CPU is not always efficient and so people started using GPU. GPU vs CPU vs GPGPU Wait a … Read more
Amazon SageMaker helps data scientists and Machine Learning developers build, train and deploy machine learning models. It includes Jupyter notebook to build and train model as well SageMaker API to train and deploy model with a few lines of code. Amazon CloudFormation helps in provisioning AWS resources using code. It automates provisioning and configuring resources … Read more