Module 6 — Modern Deep Learning Overview

Deep Learning Tutorial Series

• Module 0 — Environment Setup (Beginner Guide)
• Module 1 — Machine Learning & Neural Network Basics
• Module 2 — Training & Learning Process
• Module 3 — Practical Deep Learning with PyTorch
• Module 4 — CNNs and Computer Vision
• Module 5 — Deep Learning Architectures
• Module 6 — Modern Deep Learning Overview

This module introduces models for sequential data and gives a high-level overview of modern deep learning systems, including attention and transformers.

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Modern Deep Learning Pipeline

Typical workflow today:

1. Choose pretrained model
2. Replace final layer
3. Fine-tune on your dataset
4. Evaluate and deploy

Pretrained Models

Modern deep learning rarely trains from scratch.

Instead, we use pretrained models:

Examples:

• ImageNet models (vision)
• BERT / GPT (text)
• Whisper (speech)

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Transfer Learning

Transfer learning means:

• Start with a pretrained model
• Adapt it to your task

Benefits:

• Requires less data
• Faster training
• Better performance

Example:

• Use ImageNet CNN → classify medical images

• Representation Learning Breakthroughs Every ML Engineer Should Know: What is Representation Learning? (Medium, 2023)
• What is self-supervised learning? (IBM blog, 2023)

Autoencoder

• Autoencoders in Deep Learning: Tutorial & Use Cases [2023] (V7labs, 2023)

Contrastive Learning

• The Beginner’s Guide to Contrastive Learning (V7labs, 2022)
• Contractive Autoencoders: An Insight into Enhanced Feature Learning (Medium, 2023)

Few Shot Learning

• An Introduction to Few-Shot Learning
• Understanding Few-Shot Learning in Computer Vision – What You Need to Know

Acknowledgement

Some content on this page was prepared or edited with assistance from ChatGPT.

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