10 Deep Learning Best Practices
As projects move from small-scale research to large-scale deployment, there are some universal best practices to achieve successful deep learning model rollout for a company of any size and means.
How to use Deep Learning when you have Limited Data
Often the data needed to build a model is impossible to find. Models trained for one task can be reused for another with Transfer Learning
Topic Modeling with LSA, PLSA, LDA & lda2Vec
We will explore topic modeling through 4 of the most popular techniques today: LSA, pLSA, LDA, and the newer, deep learning-based lda2vec.
How to do Semantic Segmentation using Deep learning
semantic segmentation is one of the key problems in the field of computer vision. This article is a comprehensive overview including a step-by-step guide to implement a deep learning image segmentation model.
How to use Named-Entity Recognition (NER) for Information Extraction
The problem requires us to create a pipeline that will convert OCR outputs of different kinds of documents to a key-value like structure where keys are all the important fields one might need from, for example, an invoice like - invoice number, name of vendor ...
Information Extraction from Receipts with Graph Convolutional Networks
Automated information extraction is making business processes faster and more efficient. Graph Convolutional Networks can extract fields and values from visually rich documents better than traditional deep learning approaches like NER.
Data Augmentation: How to Use Deep Learning with Limited Data?
This article is a comprehensive review of Data Augmentation techniques for Deep Learning, specific to images.
How to use deep learning & OCR for data extraction from meter readings
Submeter reading has been traditionally a manual task. However, it can be easily automated using Deep Learning. It saves time, money and man hours.
