LSTM volume

Long Short-Term Memory

How machines learn to remember — and forget — on purpose

A story-driven, from-first-principles walk through Long Short-Term Memory networks — starting with why sequences defeat ordinary neural networks and ending with complete, real-world projects, advanced architectures, and the interview questions that test it all.

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Chapters: 18
Parts: 8
Study time: ~6h

Part 01

Foundations

2 chapters · 30 min

01
Introduction
The memory problem that gave birth to LSTM
12m
02
Foundations of AI, Machine Learning, and Deep Learning
The ladder from rule-based systems to deep networks
18m

Part 02

Sequence Learning

1 chapters · 15 min

03
Sequence Learning
Why order matters and how machines can exploit it
15m

Part 03

Mathematical Toolkit

1 chapters · 25 min

04
The Mathematical Toolkit
Vectors, matrices, derivatives, and gradient descent — everything LSTM needs
25m

Part 04

From RNN to LSTM

3 chapters · 73 min

Part 05

Building LSTM in Code

2 chapters · 52 min

Part 06

Data, Evaluation & Tuning

3 chapters · 52 min

Part 07

Advanced Architectures & Projects

3 chapters · 87 min

Part 08

Reference

3 chapters · 39 min

Begin with Introduction

The memory problem that gave birth to LSTM

Open chapter 01 Other volumes

Long Short-Term Memory

Table of contents

Foundations

Introduction

Foundations of AI, Machine Learning, and Deep Learning

Sequence Learning

Sequence Learning

Mathematical Toolkit

The Mathematical Toolkit

From RNN to LSTM

Recurrent Neural Networks

Long Short-Term Memory

The Mathematics of LSTM

Building LSTM in Code

Building LSTM from Scratch with NumPy

Building LSTM with TensorFlow/Keras

Data, Evaluation & Tuning

Data Preparation

Evaluation Metrics

Hyperparameter Optimization

Advanced Architectures & Projects

Advanced Architectures

Real-World Projects

Best Practices and Common Problems

Reference

Research Foundations

Interview and Viva Preparation

Python & NumPy Refresher

Begin with Introduction

Long Short-Term Memory

Table of contents

Foundations

Introduction

Foundations of AI, Machine Learning, and Deep Learning

Sequence Learning

Sequence Learning

Mathematical Toolkit

The Mathematical Toolkit

From RNN to LSTM

Recurrent Neural Networks

Long Short-Term Memory

The Mathematics of LSTM

Building LSTM in Code

Building LSTM from Scratch with NumPy

Building LSTM with TensorFlow/Keras

Data, Evaluation & Tuning

Data Preparation

Evaluation Metrics

Hyperparameter Optimization

Advanced Architectures & Projects

Advanced Architectures

Real-World Projects

Best Practices and Common Problems

Reference

Research Foundations

Interview and Viva Preparation

Python & NumPy Refresher

Begin with Introduction