A Gentle Guide to the lifecycle of a Machine Learning project in the Enterprise, the roles involved and the challenges of building models, in Plain English — What is Enterprise ML? What does it take to deliver a machine learning (ML) application that provides real business value to your company? Once you’ve done that and proved the substantial benefit that ML can bring to the company, how do you expand that effort to additional use cases, and really start to fulfill…

A Gentle Guide to how the Attention Score calculations capture relationships between words in a sequence, in Plain English. — Transformers have taken the world of NLP by storm in the last few years. Now they are being used with success in applications beyond NLP as well. The Transformer gets its powers because of the Attention module. …

A Gentle Guide to the reasons for the Batch Norm layer’s success in making training converge faster, in Plain English — The Batch Norm layer is frequently used in deep learning models in association with a Convolutional or Linear layer. Many state-of-the-art Computer Vision architectures such as Inception and Resnet rely on it to create deeper networks that can be trained faster. In this article, we will explore why Batch Norm…

A Gentle Guide to boosting model training and hyperparameter tuning with Optimizers and Schedulers, in Plain English — Optimizers are a critical component of neural network architecture. And Schedulers are a vital part of your deep learning toolkit. During training, they play a key role in helping the network learn to make better predictions. But what ‘knobs’ do they have to control their behavior? And how can you…

A Gentle Guide to an all-important Deep Learning layer, in Plain English — Batch Norm is an essential part of the toolkit of the modern deep learning practitioner. Soon after it was introduced in the Batch Normalization paper, it was recognized as being transformational in creating deeper neural networks that could be trained faster. Batch Norm is…

A Gentle Guide to two essential metrics (Bleu Score and Word Error Rate) for NLP models, in Plain English — Most NLP applications such as machine translation, chatbots, text summarization, and language models generate some text as their output. In addition applications like image captioning or automatic speech recognition (ie. Speech-to-Text) output text, even though they may not be considered pure NLP applications.

An end-to-end example using Encoder-Decoder with Attention in Keras and Tensorflow 2.0, in Plain English — Generating Image Captions using deep learning has produced remarkable results in recent years. One of the most widely-used architectures was presented in the Show, Attend and Tell paper. The innovation that it introduced was to apply Attention, which has seen much success in the world of NLP, to the Image…

A Gentle Guide to Image Feature Encoders, Sequence Decoders, Attention, and Multi-modal Architectures, in plain English — Image Captioning is a fascinating application of deep learning that has made tremendous progress in recent years. What makes it even more interesting is that it brings together both Computer Vision and NLP. What is Image Captioning? It takes an image as input and produces a short textual summary describing the content of the…

A Gentle Guide to Feature Engineering and Visualization with Geospatial data, in Plain English — Location data is an important category of data that you frequently have to deal with in many machine learning applications. Location data typically provides a lot of extra context to your application’s data. For instance, you might want to predict e-commerce sales projections based on your customer data. The machine…