AI Models Deep Learning Intermediate Edition

Course 2 of 3 — Deep Learning in Practice

Intermediate Edition — PyTorch · Computer Vision · NLP · Generative AI

🔵 Intermediate Level
📦 30 Lessons
🧪 Hands-On Apps
🔥 PyTorch Primary Framework
🖥️ CPU-First (No GPU Required)

Graduate from NumPy to PyTorch, from toy networks to real models. Thirty lessons. Thirty apps.

Why This Course?

Part 1 gave you the foundations. This course gives you the firepower.

The jump from NumPy to PyTorch is not just syntax — it is an architectural shift.

Most courses:

• Re-teach basics

• Jump to fine-tuning

This course:

• Builds systems from scratch

• Makes training decisions explicit

Milestones

• Lesson 3 → Custom nn.Module

• Lesson 8 → Fine-tuning on real data

• Lesson 30 → ONNX + REST pipeline

Great engineers don’t just use models — they understand execution.

Core Principles

1. PyTorch becomes transparent

• Dataset, DataLoader, training loop

• Full control over .backward()

2. Four domains

• Computer Vision

• NLP

• Generative AI

• Audio

3. Training science

• BatchNorm, Dropout

• LR schedulers

• Optimizers

4. 30 real apps

• GAN, VAE, SHAP, ONNX

5. Production mindset

• Gradient clipping

• NaN detection

• Checkpoints

What You Will Build

Project Structure

lesson_XX/
├── app.py
├── model.py
├── train.py
└── README.md

Who Should Take This Course?

What Makes This Course Different?

1. Train on your own data early

2. Training science is core

3. Multi-domain learning

4. Explainability included

5. Real production capstone

6. CPU-first

Key Topics

PyTorch

• Autograd internals

• DataLoader performance

• Training loops

Computer Vision

• CNNs

• Transfer learning

• Detection & segmentation

NLP

• RNN, LSTM

• Word2Vec

• Attention

Training Science

• BatchNorm, Dropout

• LR schedulers

• Optimizers

Generative AI

• Autoencoders

• VAE, GAN

• SimCLR

Production

• SHAP, LIME

• Optuna

• ONNX

Prerequisites

If you can implement gradient descent in NumPy — you're ready.

Learning Outcomes

You will be able to:

• Build full training pipelines

• Design CNNs

• Fine-tune models

• Train LSTMs

• Build VAEs & GANs

• Use SHAP & LIME

• Tune with Optuna

• Profile & optimize

• Deploy via ONNX

Course Structure

Full Curriculum (Condensed)

Section 1 — PyTorch

1. Tensor Ops Lab

2. Gradient Tracer

3. Net Architect

4. Data Pipeline Builder

5. Training Loop Lab

Section 2 — CV

1. CIFAR Classifier

2. Filter Inspector

3. Transfer Learning

4. Object Detector

5. Segmentation

Section 3 — NLP

1. RNN

2. LSTM

3. Word2Vec

4. Classification

5. Seq2Seq

Section 4 — Training

1. BatchNorm

2. Dropout

3. LR Scheduling

4. Optimizers

5. Custom Loss

Section 5 — Generative

1. Autoencoder

2. VAE

3. GAN

4. Audio

5. SimCLR

Section 6 — Production

1. Explainability

2. Optuna

3. AMP

4. Profiling

5. Capstone

Section Deep Dives

PyTorch

• Dynamic computation graph

• DataLoader bottlenecks

• Gradient clipping necessity

Computer Vision

• Feature hierarchy

• Catastrophic forgetting

• NMS tradeoffs

NLP

• Vanishing gradients

• Embedding geometry

• Attention alignment

Training Science

• BatchNorm pitfalls

• OneCycleLR advantage

• Focal loss impact

Generative

• VAE vs GAN latent space

• Mode collapse detection

• SimCLR evaluation

Production

• SHAP bias detection

• Optuna pruning

• ONNX validation

Tool Stack

Quick Start

pip install torch torchvision --index-url https://download.pytorch.org/whl/cpu
cd lesson_01
pip install -r requirements.txt
streamlit run app.py

By Lesson 30, you ship a real deep learning system — end to end.