Use Yolov5 architecture to train model with pytorch backend for different dataset and convert dataset from one format to other for training of yolov5 object detection models.

Tesseract is a open-source OCR engine owened by Google for performing OCR operations on different kind of images. Use pytesseract to process different type of images and localize text location with confidence.

Tensorflow callbacks are very important to customize behaviour of Keras Tensorflow models in training or evaluation. We can either use predefined callbacks from tensorflow or can write our own callbacks to do some process.

We have different triditional and Machine Learning based methods for increasing our training data by changing data shape, adding noise or creating nearly similar data using current dataset. NLPAug is an open source python package for data augmentation using different methods and pretrained Deep Learning models.

AzureML is a machine learning cloud-based service from Azure cloud used for development, training and deployment of Machine Learning models and solutions

Emotion Classification is a Natural Language Processing task in Machine Learning where we can process text data and classify into different classes and can detect sentiment of given text.

Use Azure Cognitive face service to detect faces in images and also match faces to recognize person in images. We can also use it to group faces and face recognition.

Connect a custom domain from any domain provider to Google cloud compute engine

Train a Deep learning image classification model with no or very less code and serve using an endpoint using azure cognitive services.

Image classification is a very basic example in computer vision yet used widely for a lot of tasks. In image classification, we can input and image to deep learning model and get related label for class which image belongs to. We can use image classification for classifying an object from different classes, checking quality of a manufactured objects(good or not),

Optimize tensorflow deep learning models using TensorRT by nvidia to speed up inference of models with good accuracy.

For any model, we train for a specific purpose, we need to deploy it on some cloud or local device to use it for new data, so here we will learn about cloud deployment as API

Face Detection and Recognition is a very used part in Deep Learning. We use face detection and recognition for different tasks like login on applications, person recognition and different attendence systems.