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Neural Network Optimizers Made Simple - Core algorithms and why they are needed
A Gentle Guide to fundamental techniques used by gradient descent optimizers like SGD, Momentum, RMSProp, Adam, and others, in plain English.
In Optimizer, tutorial, Apr 07, 2021Foundations of NLP Explained Visually - Beam Search, How It Works
A gentle guide to how Beam Search enhances predictions, in plain English
In NLP, tutorial, Apr 01, 2021Audio Deep Learning Made Simple - Automatic Speech Recognition (ASR), How it Works
Speech-to-Text algorithm and architecture, including Mel Spectrograms, MFCCs, CTC Loss and Decoder, in Plain English.
In Audio, tutorial, Mar 26, 2021Audio Deep Learning Made Simple - Sound Classification, Step-by-Step
An end-to-end example and architecture for audio deep learning’s foundational application scenario, in plain English.
In Audio, tutorial, Mar 18, 2021Audio Deep Learning Made Simple - Data Preparation and Augmentation
A Gentle Guide to enhancing Spectrogram features for optimal performance. Also Data Augmentation, in Plain English.
In Audio, tutorial, Feb 24, 2021Audio Deep Learning Made Simple - Why Mel Spectrograms perform better
A Gentle Guide to processing audio in Python. What are Mel Spectrograms and how to generate them, in Plain English.
In Audio, tutorial, Feb 19, 2021Audio Deep Learning Made Simple - State-of-the-Art Techniques
A Gentle Guide to the world of disruptive deep learning audio applications and architectures. And why we all need to know about Spectrograms, in Plain English.
In Audio, tutorial, Feb 12, 2021Transformers Explained Visually - Multi-head Attention, deep dive
A Gentle Guide to the inner workings of Self-Attention, Encoder-Decoder Attention, Attention Score and Masking, in Plain English.
In Transformers, tutorial, Jan 17, 2021Transformers Explained Visually - How it works, step-by-step
A Gentle Guide to the Transformer under the hood, and its end-to-end operation.
In Transformers, tutorial, Jan 02, 2021Reinforcement Learning Explained Visually - Policy Gradients, step-by-step
A Gentle Guide to the REINFORCE algorithm, in Plain English.
In Reinforcement Learning, tutorial, Dec 30, 2020Featured
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Enterprise ML - Why putting your model in production takes longer than building it
In Neural, tutorial, -
Enterprise ML - Why building and training a "real-world" model is hard
In Enterprise, -
Transformers Explained Visually - Not just how, but Why they work so well
In Transformers, tutorial, -
Differential and Adaptive Learning Rates - Neural Network Optimizers and Schedulers demystified
In Neural, tutorial, -
Foundations of NLP Explained — Bleu Score and WER Metrics
In NLP, tutorial, -
Image Captions with Attention in Tensorflow, Step-by-step
In Vision, tutorial, -
Leveraging GeoLocation Data with Machine Learning - Essential Techniques
In GeoLocation, tutorial, -
Foundations of NLP Explained Visually - Beam Search, How It Works
In NLP, tutorial, -
Audio Deep Learning Made Simple - Automatic Speech Recognition (ASR), How it Works
In Audio, tutorial, -
Audio Deep Learning Made Simple - Sound Classification, Step-by-Step
In Audio, tutorial, -
Audio Deep Learning Made Simple - Why Mel Spectrograms perform better
In Audio, tutorial, -
Audio Deep Learning Made Simple - State-of-the-Art Techniques
In Audio, tutorial, -
Transformers Explained Visually - How it works, step-by-step
In Transformers, tutorial, -
Transformers Explained Visually - Overview of Functionality
In Transformers, tutorial, -
Reinforcement Learning Made Simple - Intro to Basic Concepts and Terminology
In Reinforcement Learning, tutorial,