A blog of Machine Learning and AI

  • RunPod’s Serverless.

    RunPod’s Serverless platform allows for the creation of API endpoints that automatically scale to meet demand. The tutorial guides you through creating a basic worker and turning it into an API endpoint on the RunPod serverless platform. For this tutorial, we will create an API endpoint that helps us accomplish the tedious task of telling…

  • Word Representation and Vectorization

    Introduction to Vectorization We can see that in one-hot encoding, every single word in ourtext corresponds to a vector element.For ex: Here “Queen” is [0,1,0,0,0] and “room” is [0,0,0,0,1] One disadvantage of working with sparse matrices is that they end up consuming a lot of memory, especially as the size of the corpus increases, making…

  • Transfer learning and fine-tuning

    Transfer learning and fine-tuning

    Transfer learning and fine-tuning In this tutorial, you will learn how to classify images of cats and dogs by using transfer learning from a pre-trained network. A pre-trained model is a saved network that was previously trained on a large dataset, typically on a large-scale image-classification task. You either use the pretrained model as is…

  • What is VGGNet?

    What is VGG? VGG stands for Visual Geometry Group; it is a standard deep Convolutional Neural Network (CNN) architecture with multiple layers. The “deep” refers to the number of layers with VGG-16 or VGG-19 consisting of 16 and 19 convolutional layers. The VGG architecture is the basis of ground-breaking object recognition models. Developed as a…

  • Regularization in CNNs

    Regularization in CNNs

    The Overfitting problemfor CNNs Techniques to address Overfitting 1. Data Augmentation 2. Batch Normalization 3. Spatial Dropout In Spatial Dropout, whole feature maps themselves are randomly dropped. Dropping a feature map, means making all the cells of that feature map 0, which would be as good as not using it.Let’s suppose we obtained 8 feature…

  • What are Artificial Neural Networks?

    What are Artificial Neural Networks?

    What are Artificial Neural Networks (ANNs)? Input Layer – Takes Inputs Hidden Layer – Responsible for processing information Output Layer – Gives Output How Does A Neural Network Get Trained? Here, we can see that wa1,wa2,wa3, and wa4 are weights assigned to the connections of the 1st node of the input layer, wb1,wb2,wb3,wb4 are weights assigned to the…

  • Difference between fit, fit_transform, and transform?

    Difference between fit, fit_transform, and transform?

    1. What is the difference between fit, fit_transform, and transform? The way we use fit and predict in regression, similarly for functions that transform the data – we have fit and transform fit – is used to fit parameters of the function transform – transforming the data using parameters fitted with the fit function fit_transform – to first…

  • Introduction to Computer Vision

    Introduction to Computer Vision

    CNNs are a special type of neural network designed to work with image data. CNNs use convolutional layers – hidden layers which perform convolution operations. They have some different characteristics to ANNs: The first part in this architecture is the convolutional layer followed by the pooling layer and the second part is the fully connected…

  • Terraform là gì ?

    Terraform là gì ?

    Terraform là một công cụ xây dựng hạ tầng bằng ngôn ngữ lập trình ( infrastructure as code ). Infrastructure as Code. Multi-cloud provisioning. Manage Kubernetes Manage network infrastructure. Manage virtual images. Integrate with existing workflows Enforce policy as code. Inject secrets into Terraform. Reference : https://developer.hashicorp.com/terraform/intro

  • What is a pipeline in machine learning ?

    What is a pipeline in machine learning ?

    Pipelines Build a pipeline Note: A pipeline can be constructed purely for data transformation alone. Which means there it is not mandatory to have an estimator make_pipeline Reference : What are Azure Machine Learning pipelines? at : https://learn.microsoft.com/en-us/azure/machine-learning/concept-ml-pipelines?view=azureml-api-2 https://www.databricks.com/glossary/what-are-ml-pipelines

Got any book recommendations?