Tips to Register for an AWS Certification Exam with Pearson VUE from Pakistan
This blog post provides essential tips for individuals in Pakistan facing challenges with AWS certification exam registration through Pearson VUE. It outlines workarounds for the payment limitations, guides readers through the registration process, and offers insights to successfully navigate the system, ensuring a smooth experience.
Creating an Object Detection Model using Amazon Rekognition Custom Labels
Amazon Rekognition Custom Labels is a feature of Amazon Rekognition that enables customers to build specialized image analysis capabilities to detect unique objects and scenes integral to their specific use case. In this post, we will use this service to train a custom model with a small set of labeled images and use it to analyze new images via an API. This service uses AutoML to train models to find objects, scenes, concepts, object locations, and brand locations in images.
Implementing AutoEncoder with PyTorch
This is a practice notebook to implement AutoEncoder in PyTorch. An autoencoder takes an image as input, stores it in a lower dimension (term encoder), and tries to reproduce the same image as output, hence the term auto. Autoencoders come in handy to identify and group similar images.
Detect Vanishing Gradients in Deep Neural Networks by Plotting Gradient Distributions
In this notebook, we will explore how vanishing gradients can affect the training of a deep neural network. We will visualize the gradient flow from the deeper to starting layers during the backpropagation for two popular activation functions, Sigmoid and ReLU.
Convolutional Neural Networks Filters and Feature Maps with PyTorch
This is a practice notebook for implementing a convolutional neural network (CNN) on the MNIST dataset with PyTorch. We will implement the now famous LeNet-5 from Yann LeCun, a 7-layer CNN from 1989. Then we will explore and visualize the layers learned by our network including filters, feature maps, and output layers.
Two Class (Binary) Logistic Regression in Pytorch
This is a practice notebook for implementing a two class logistic regression model in PyTorch. We will start by generating some synthetic data and then build an end-to-end pipeline to train a model. We will also see two ways to implement logistic regression models.
Linear Regression with PyTorch
This is a practice notebook for implementing a linear regression model in PyTorch. We will start by generating some synthetic linear data and then load it into DataLoader class for creating mini-batches. Then build the complete pipeline to train the model and visualize its loss progress in TensorBoard.
Data Preparation with SageMaker Data Wrangler (Part 5)
A detailed guide on AWS SageMaker Data Wrangler to prepare data for machine learning models. This is a five parts series where we will prepare, import, explore, process, and export data using AWS Data Wrangler. You are reading Part 5:Export data for ML training.
Data Preparation with SageMaker Data Wrangler (Part 4)
A detailed guide on AWS SageMaker Data Wrangler to prepare data for machine learning models. This is a five parts series where we will prepare, import, explore, process, and export data using AWS Data Wrangler. You are reading Part 4:Preprocess data using Data Wrangler.
Data Preparation with SageMaker Data Wrangler (Part 3)
A detailed guide on AWS SageMaker Data Wrangler to prepare data for machine learning models. This is a five parts series where we will prepare, import, explore, process, and export data using AWS Data Wrangler. You are reading Part 3:Explore data with Data Wrangler visualizations.
Data Preparation with SageMaker Data Wrangler (Part 2)
A detailed guide on AWS SageMaker Data Wrangler to prepare data for machine learning models. This is a five parts series where we will prepare, import, explore, process, and export data using AWS Data Wrangler. You are reading Part 2:Import data from multiple sources using Data Wrangler.
Data Preparation with SageMaker Data Wrangler (Part 1)
A detailed guide on AWS SageMaker Data Wrangler to prepare data for machine learning models. This is a five parts series where we will prepare, import, explore, process, and export data using AWS Data Wrangler. You are reading Part 1:Prepare synthetic data and place it on multiple sources.
Docker - Send Container Logs to AWS CloudWatch
A tutorial on sending docker application logs to aws cloudwatch.
AWS EFS Sync to S3 Using DataSync
A tutorial to synchronize EFS with S3 bucket using DataSync service.
AWS EFS Sync to S3 Using Lambda
A tutorial on synchronizing EFS with S3 bucket using a Lambda function.
