Table of Contents.

1. Transformers

A deep dive into the Transformer architecture and Coding a BERT model from scratch

1.1 Introduction to Large Language Models

1.2 Anatomy of the Transformer Block

1.3 Tokenization

1.4 Byte Pair Encoding

1.5 Word Embedding

1.6 Positional Embedding

1.7 Transformer Block

1.8 The Need for Attention Mechanism

2. Vision Models

An exploration into the architecture, concepts, and breakthroughs of Vision models.

Segment Anything Model

Embeddings in NLP

Swin Transformer

Detection Transformer

3. Audio DeepLearning

A comprehensive guide to understanding audio signals and their application in deep learning.

Introduction

Sound as a Physical Signal

Fundamental Waveform Parameters

Periodic and Aperiodic Sounds

Phase in Audio Signals

Timbre and the Character of Sound

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