Skin cancer is the most prevalent type of cancer. Melanoma, specifically, is responsible for 75% of skin cancer deaths, despite being the least common skin cancer. Melanoma is a deadly disease, but most cases of Melanoma can be cured with minor surgery if caught early. Dermatologists could enhance their diagnostic accuracy if detection algorithms consider “contextual” images within the same patient to determine which images represent a melanoma. If successful, classifiers would be more accurate and could better support dermatological clinic work.
The Kaggle dataset for SIIM-ISIC Melanoma Classification contains the medical images of skin lesions and patient data that…
If you are creating machine learning models using Keras, know all the available techniques to build a model, their strengths, and when to apply them along with the code.
Sequential is the most common and simple technique to build models in Keras.
Sequential groups a linear stack of layers into a Keras Model. The Sequential model is built by passing a list of layers to the Sequential constructor
When to build Sequential models in Keras?
Sequential Models are appropriate for a plain stack of layers with exactly one input tensor and one output tensor like VGGNet.
When to avoid building…
If you are an e-commerce retailer, your digital system’s performance, reliability, and availability are critical to your business. Digital systems include applications for managing Master data, supply chain, sales, and finance.
So how do you maintain the high availability and reliability of your systems?
DevOps collaborates development, quality assurance, and operations that involve people, processes, and technology to streamline the software development and release throughput using a cycle of Continuous Integration(CI) and Continuous Deployment(CD).
In DevOps, developers merge their code changes to a central repository like GitHub. These incremental code changes can be done frequently and reliably. Once the code…
You will learn
Data, model, hardware, or computation resources are the basic elements of a Machine Learning application.
here you will learn
Semi-Supervised learning is a combination of supervised and unsupervised learning.
Supervised learning employs labeled data for training to learn the relationship between the input data and the target variable; however, unsupervised learning utilizes unlabeled data to identify the hidden data pattern in the input data.
Unlabeled data is easier to acquire and less costly compared to the labeled data. …
This implementation is inspired and motivated by AN IMAGE IS WORTH 16X16 WORDS: TRANSFORMERS FOR IMAGE RECOGNITION AT SCALE.
Transformers are a big success in NLP, and Vision Transformers apply the standard Transformers used in NLP to the images.
A detailed explanation of the strengths and weaknesses of CNN and different variations are Vision Transformers can be read here.
The high-level steps to implement the Vision Transformer in Tensorflow 2.3 are outlined below.
Step 1: Split the image into fixed-size patches.
Step 2:Flatten the 2D image patches to 1D patch embedding and linearly embed them using a fully connected layer.
What knowledge will you gain here?
To train a deep learning model on input data, we have two different types of parameters.
You have a mix of images for different classes in a folder, how do you label these images when you are aware of the number of classes.
Here you will implement deep convolutional Autoencoders to label the Fashion MNIST dataset with just using the Fashion MNIST images.
Assumptions made for the labeling images using Autoencoders:
Autoencoders consist of an Encoder and a Decoder network. The Encoder encodes the high dimension input into a lower-dimensional latent representation also referred to as the bottleneck layer. The…
you will learn
SVM algorithm was proposed by Vladimir N. Vapnik and Alexey Ya. Chervonenkis in 1963
Support vector machine is a supervised machine learning algorithm used for classification as well as regression. SVM’s objective is to identify a hyperplane to separate data points into two classes by maximizing the margin between support vectors of the two classes
What is Kubernetes-K8s, why we need them and how Kubernetes work, what K3s and MicroK8s, and the similarities and differences between K3s and MicroK8s?
You want to run multiple instances of your deep learning solutions with several components like data. preprocessing, making inferences, and writing the information to the database or a file.
How can you easily scale and deploy your solutions?
Here are few things you want when you scale and deploy your solution