CRNNs combine both convolutional and recurrent architectures and is widely used in text detection and optical character recognition (OCR). In this project, we are going to use a CRNN architecture to detect text in sample images. The data we are going to use is TRSynth100k from Kaggle. Given an image containing some text, the goal here is to correctly identify the text using the CRNN architecture. We are going to train the model end-to-end from scratch.
In this deep learning project, you will find similar images (lookalikes) using deep learning and locality sensitive hashing to find customers who are most likely to click on an ad.
Build your own image similarity application using Python to search and find images of products that are similar to any given product. You will implement the K-Nearest Neighbor algorithm to find products with maximum similarity.
In this machine learning project, you will use the video clip of an IPL match played between CSK and RCB to forecast key performance indicators like the number of appearances of a brand logo, the frames, and the shortest and longest area percentage in the video.
Want to search images of clothes which have text on them? Then this project talks through how we can classify an image whether it has text on it or not. For this we use state of the model called as inception and try and deepdive into how it works on our dataset
In this Deep Learning Project on Image Segmentation Python, you will learn how to implement the Mask R-CNN model for early fire detection.
Text data requires special preparation before you can start using it for any machine learning project.In this ML project, you will learn about applying Machine Learning models to create classifiers and learn how to make sense of textual data.
In this time series project, you will forecast Walmart sales over time using the powerful, fast, and flexible time series forecasting library Greykite that helps automate time series problems.
PySpark Project-Get a handle on using Python with Spark through this hands-on data processing spark python tutorial.
In this project, we will cover in detail the architecture of a transformer used in natural language processing use cases. We will go through the key nlp areas in the pre-transformer stage like bow, word2vec...and then the origin and gradual refinement of transformers. Finally, we will study one of the most popular state of the art transformer models, called BERT and use it for text classification on a large dataset.