ENTERPRISE MACHINE LEARNING SERIES

A Gentle Guide to the complexities of model deployment, and integrating with the enterprise application and data pipeline. What the Data Scientist, Data Engineer, ML Engineer, and ML Ops do, in Plain English.

Let’s say we’ve identified a high-impact business problem at our company, built an ML (machine learning) model to tackle it, trained it, and are happy with the prediction results. This was a hard problem to crack that required much research and experimentation. …

Notes from Industry, ENTERPRISE MACHINE LEARNING SERIES

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…

HANDS-ON TUTORIALS, INTUITIVE TRANSFORMERS SERIES NLP

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. …

HANDS-ON TUTORIALS, INTUITIVE DEEP LEARNING SERIES

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…

Hands-on Tutorials, INTUITIVE DEEP LEARNING SERIES

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…

Hands-on Tutorials, INTUITIVE DEEP LEARNING SERIES

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 neural network layer that is…

INTUITIVE NLP SERIES

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.

How good is the predicted output?

The common problem when training these applications…

INTUITIVE IMAGE CAPTIONS SERIES

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…

Hands-on Tutorials, INTUITIVE IMAGE CAPTIONS SERIES

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…

Hands-on Tutorials, INTUITIVE GEO-LOCATION SERIES

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…

Ketan Doshi

Machine Learning and Big Data

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