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Text Segmentation

Text Segmentation

Normalization, Tokenization, Sentence Segmentation + Useful Methods
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Jake Batsuuri
Inputting & PreProcessing Text

Inputting & PreProcessing Text

Input Methods, String & Unicode, Regular Expression Use Cases
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Jake Batsuuri
What are Context Free Languages?

What are Context Free Languages?

Grammars, Derivation, Expressiveness, Chomsky Hierarchy
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Jake Batsuuri
Language Generation with Recurrent Models

Language Generation with Recurrent Models

LSTM, Sampling, Smart Code Completion Tool
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Jake Batsuuri
Language Processing with Recurrent Models

Language Processing with Recurrent Models

Bidirectional RNNs, Encoding, Word Embedding and Tips
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Jake Batsuuri
Recurrent Models Overview

Recurrent Models Overview

Recurrent Layers: SimpleRNN, LSTM, GRU
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Jake Batsuuri
Convolutional Models for Sequential Data

Convolutional Models for Sequential Data

Easing Into Recurrent Neural Networks
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Jake Batsuuri
Working Understanding of Convolutional Models

Working Understanding of Convolutional Models

Creating, Preprocessing, Data Augmentation, Reusing, Visualizing
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Jake Batsuuri
Convolutional Models Overview

Convolutional Models Overview

Convolutions, Kernels, Downsampling & Properties
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Jake Batsuuri
What Are Regular Languages?

What Are Regular Languages?

Minimization, Finite State Transducers, Regular Relations
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Jake Batsuuri
Natural Language Tool Kit 3.5

Natural Language Tool Kit 3.5

Search Functions, Statistics, Pronoun Resolution
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Jake Batsuuri
Simple Regression Example

Simple Regression Example

K-Fold Validation, Normalization, MAE
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Jake Batsuuri
Back-Propagation Algorithm

Back-Propagation Algorithm

Definitions, Concepts, Algorithms with Visuals
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Jake Batsuuri
Notes on Formal Language Theory

Notes on Formal Language Theory

Objects, Operations, Regular Expressions and Finite State Automata
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Jake Batsuuri
Classifying Text Data into Multiple Categories

Classifying Text Data into Multiple Categories

Single-Label Multi-class Classification
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Jake Batsuuri
Context of Natural Language Processing

Context of Natural Language Processing

Motivations, Disciplines, Approaches, Outlook
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Jake Batsuuri
General Architectural Design Considerations for Neural Networks

General Architectural Design Considerations for Neural Networks

Universal Approximation Theorem, Depth, Connections
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Jake Batsuuri
The Common Approach to Binary Classification

The Common Approach to Binary Classification

The most generic way to setup your deep learning models to categorize movie reviews
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Jake Batsuuri
Hidden Units in Neural Networks

Hidden Units in Neural Networks

What are the hidden layers in deep neural networks? How are they constructed?
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Jake Batsuuri
Output Units For Deep Learning

Output Units For Deep Learning

What’s Output Units? Who’s Output Units? Why’s Output Units?
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Jake Batsuuri
Gradient Based Optimizations Under The Deep Learning Lens

Gradient Based Optimizations Under The Deep Learning Lens

How much gradients you got? A lot…
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Jake Batsuuri
Deep Feedforward Networks

Deep Feedforward Networks

Deep Feedforward Networks, DFF’s, are often called Feedforward Neural Nets, FNN’s, or Multi-layer Perceptrons, MLP’s
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Jake Batsuuri
Learning a XOR Function with Feedforward Neural Networks

Learning a XOR Function with Feedforward Neural Networks

MSE, Normal Equations, Linear Regression
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Jake Batsuuri
Principal Component Analysis Breakdown

Principal Component Analysis Breakdown

From derivation to algorithm and use
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Jake Batsuuri
Machine Learning Necessary for Deep Learning

Machine Learning Necessary for Deep Learning

Generalization, Capacity, Parameters, HyperParameters & Bayesian Statistics
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Jake Batsuuri
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