Deep Learning Architecture Design: A Comprehensive Learning Roadmap

A structured guide to mastering the skills needed to design, build, and train neural network architectures that combine CNNs, Transformers, and generative models for real-world applications.

Overview
Phase 1: Mathematical Foundations
Phase 2: Programming and Tools
Phase 3: Machine Learning Fundamentals
Phase 4: Deep Learning Core Concepts
Phase 5: Architecture Deep Dives
Phase 6: Generative Models
Phase 7: Advanced Training Techniques
Phase 8: Domain Specialization
Phase 9: Research and Innovation
Project Milestones
Resource Summary

Overview

What This Roadmap Covers

By following this roadmap, you will learn to:

Understand the mathematical foundations behind neural networks
Implement core architectures (MLP, CNN, RNN, Transformer) from scratch
Design hybrid architectures combining multiple techniques
Build and train generative models (VAE, GAN, Diffusion, Flow Matching)
Apply these skills to real-world domains (vision, audio, text, multimodal)
Scale training to production-level systems

Prerequisites

Basic programming experience (any language)
High school mathematics
Curiosity and persistence

Time Estimate

Track	Duration
Part-time (10-15 hrs/week)	18-24 months
Full-time (40+ hrs/week)	6-9 months

Phase 1: Mathematical Foundations

Duration: 4-6 weeks

1.1 Linear Algebra (Essential)

Everything in deep learning is matrix operations.

Topics to Master

Topic	Why It Matters
Vectors and matrices	Data representation
Matrix multiplication	Core of neural networks
Transpose, inverse	Weight manipulation
Eigenvalues/eigenvectors	Understanding PCA, stability
Norms (L1, L2)	Loss functions, regularization
Dot product, cosine similarity	Attention mechanisms
Broadcasting	Efficient tensor operations

Resources

Resource	Type	Cost
3Blue1Brown "Essence of Linear Algebra"	Video series	Free
MIT 18.06 Linear Algebra (Gilbert Strang)	Full course	Free
"Linear Algebra Done Right" by Axler	Textbook	~$40
Khan Academy Linear Algebra	Interactive	Free

Checkpoint

You should be able to:

[ ] Multiply matrices by hand and understand dimensions
[ ] Explain what eigenvalues represent geometrically
[ ] Implement matrix operations in NumPy without looking up syntax

1.2 Calculus (Essential)

Backpropagation is just the chain rule applied systematically.

Topics to Master

Topic	Why It Matters
Derivatives	Gradient computation
Partial derivatives	Multi-variable functions
Chain rule	Backpropagation
Gradients and Jacobians	Vector calculus for NNs
Basic integrals	Probability distributions

Resources

Resource	Type	Cost
3Blue1Brown "Essence of Calculus"	Video series	Free
Khan Academy Calculus	Interactive	Free
"Calculus Made Easy" by Thompson	Textbook	Free online

Checkpoint

You should be able to:

[ ] Compute derivatives of common functions
[ ] Apply chain rule to composite functions
[ ] Calculate gradients of multi-variable functions

1.3 Probability and Statistics (Essential)

Understanding uncertainty, distributions, and model evaluation.

Topics to Master

Topic	Why It Matters
Probability basics	Bayesian thinking
Random variables	Modeling uncertainty
Common distributions (Gaussian, Bernoulli, Categorical)	Generative models
Expectation and variance	Loss functions
Bayes' theorem	Probabilistic models
Maximum likelihood estimation	Training objective
KL divergence	VAE, information theory
Sampling methods	Generative inference

Resources

Resource	Type	Cost
Khan Academy Statistics & Probability	Interactive	Free
"Think Stats" by Downey	Textbook	Free online
StatQuest YouTube channel	Video series	Free
"Pattern Recognition and ML" Ch. 1-2 by Bishop	Textbook	~$80

Checkpoint

You should be able to:

[ ] Explain Bayes' theorem with an example
[ ] Sample from a Gaussian distribution and explain parameters
[ ] Derive maximum likelihood for simple distributions
[ ] Explain what KL divergence measures

1.4 Optimization (Important)

How neural networks actually learn.

Topics to Master

Topic	Why It Matters
Gradient descent	Core optimization algorithm
Convexity	Understanding loss landscapes
Local vs global minima	Training challenges
Learning rate	Hyperparameter tuning
Momentum, Adam	Modern optimizers

Resources

Resource	Type	Cost
"Convex Optimization" Ch. 1-3 by Boyd	Textbook	Free online
Sebastian Ruder's "Gradient Descent Overview"	Blog post	Free

Phase 2: Programming and Tools

Duration: 3-4 weeks

2.1 Python Proficiency

Topics to Master

Topic	Why It Matters
Data structures (lists, dicts, sets)	Efficient coding
Object-oriented programming	Building models
Functional programming (map, lambda)	Data pipelines
Decorators, context managers	Advanced patterns
Debugging and profiling	Finding bottlenecks

Resources

Resource	Type	Cost
"Automate the Boring Stuff with Python"	Book	Free online
"Fluent Python" by Ramalho	Book	~$50
Real Python tutorials	Website	Free/Paid

2.2 NumPy (Essential)

The foundation of scientific Python.

Topics to Master

# You should be fluent in:
np.array, np.zeros, np.ones, np.random
np.reshape, np.transpose, np.squeeze, np.expand_dims
np.matmul, np.dot, @
Broadcasting rules
Indexing and slicing
np.sum, np.mean, np.max (with axis parameter)
np.concatenate, np.stack

Resources

Resource	Type	Cost
NumPy official tutorial	Documentation	Free
"From Python to NumPy" by Rougier	Book	Free online

2.3 PyTorch (Essential)

The dominant framework for research.

Topics to Master

Topic	Priority
Tensors and operations	Essential
Autograd (automatic differentiation)	Essential
nn.Module and building models	Essential
DataLoader and datasets	Essential
Training loops	Essential
GPU usage (.to(device))	Essential
Saving/loading models	Important
Custom layers and functions	Important
Hooks for debugging	Useful
torch.compile (PyTorch 2.0)	Useful

Resources

Resource	Type	Cost
PyTorch official tutorials	Documentation	Free
"Deep Learning with PyTorch" by Stevens	Book	Free online
fast.ai course	Video course	Free

Checkpoint Project

Implement from scratch (no nn.Linear, etc.):

[ ] A linear layer with forward and backward pass
[ ] A simple 2-layer MLP
[ ] Train on MNIST using manual gradient computation

2.4 Development Environment

Essential Tools

Tool	Purpose
Git	Version control
VS Code / PyCharm	IDE
Jupyter notebooks	Experimentation
conda / venv	Environment management
Docker (later)	Reproducibility

Useful Tools

Tool	Purpose
TensorBoard	Visualization
Weights & Biases	Experiment tracking
Hydra	Configuration management
pytest	Testing

Phase 3: Machine Learning Fundamentals

Duration: 4-6 weeks

3.1 Core Concepts

Topics to Master

Topic	Description
Supervised learning	Learning from labeled data
Unsupervised learning	Finding patterns without labels
Train/validation/test splits	Proper evaluation
Cross-validation	Robust evaluation
Overfitting and underfitting	Model capacity
Bias-variance tradeoff	Understanding errors
Regularization (L1, L2)	Preventing overfitting
Feature engineering	Domain knowledge integration
Hyperparameter tuning	Model optimization

3.2 Classical Algorithms

Understanding these helps appreciate what deep learning improves upon.

Algorithm	Learn To
Linear/Logistic regression	Implement from scratch
Decision trees	Understand concept
Random forests	Understand ensembling
SVM	Understand kernels
K-means clustering	Implement from scratch
PCA	Understand dimensionality reduction

3.3 Resources

Resource	Type	Cost
"Hands-On Machine Learning" by Géron (Ch. 1-8)	Book	~$60
Andrew Ng's Machine Learning (Coursera)	Course	Free to audit
"The Elements of Statistical Learning"	Book	Free online
Scikit-learn tutorials	Documentation	Free

3.4 Checkpoint Project

[ ] Implement logistic regression from scratch with gradient descent
[ ] Build a complete ML pipeline: data loading, preprocessing, training, evaluation
[ ] Achieve >95% on MNIST with a non-deep-learning method

Phase 4: Deep Learning Core Concepts

Duration: 6-8 weeks

4.1 Neural Network Fundamentals

Topics to Master

Topic	Description	Priority
Perceptron	Single neuron	Essential
Multi-layer perceptron (MLP)	Basic architecture	Essential
Activation functions	ReLU, GELU, Sigmoid, Tanh, Softmax	Essential
Loss functions	MSE, Cross-entropy, etc.	Essential
Backpropagation	How networks learn	Essential
Weight initialization	Xavier, He, etc.	Essential
Batch normalization	Training stability	Essential
Layer normalization	Transformer standard	Essential
Dropout	Regularization	Essential
Residual connections	Deep networks	Essential

Activation Functions Deep Dive

ReLU:      max(0, x)           - Simple, sparse, can "die"
LeakyReLU: max(0.01x, x)       - Prevents dying ReLU
GELU:      x * Φ(x)            - Smooth, used in Transformers
Sigmoid:   1/(1+e^(-x))        - Output in (0,1), gradients vanish
Tanh:      (e^x - e^(-x))/...  - Output in (-1,1), centered
Softmax:   e^xi / Σe^xj        - Probability distribution
SiLU/Swish: x * sigmoid(x)     - Self-gated, smooth

Understanding Backpropagation

This is critical. You must understand:

Forward pass: compute outputs
Loss computation: compare to target
Backward pass: compute gradients via chain rule
Parameter update: gradient descent step

4.2 Optimizers

Optimizer	Key Idea	When to Use
SGD	Basic gradient descent	Simple baselines
SGD + Momentum	Accumulate velocity	Better convergence
Adam	Adaptive learning rates	Default choice
AdamW	Adam + weight decay fix	Current best practice
LAMB	Large batch training	Distributed training

4.3 Normalization Techniques

Technique	Normalizes Across	Used In
Batch Norm	Batch dimension	CNNs
Layer Norm	Feature dimension	Transformers
Instance Norm	Spatial dimensions	Style transfer
Group Norm	Channel groups	Small batches
RMS Norm	Simplified Layer Norm	LLMs

4.4 Resources

Resource	Type	Cost
"Deep Learning" by Goodfellow (Ch. 6-8)	Book	Free online
"Dive into Deep Learning" (d2l.ai)	Interactive book	Free
CS231n Stanford (first 5 lectures)	Course	Free
Andrej Karpathy's "Neural Networks: Zero to Hero"	YouTube	Free

4.5 Checkpoint Projects

[ ] Implement backpropagation manually for a 2-layer MLP
[ ] Train MLP on MNIST, achieve >98% accuracy
[ ] Implement BatchNorm and LayerNorm from scratch
[ ] Experiment with different optimizers, document findings

Phase 5: Architecture Deep Dives

Duration: 10-12 weeks

This is the core of understanding modern architectures.

5.1 Convolutional Neural Networks (CNNs)

Duration: 3-4 weeks

Core Concepts

Concept	Description
Convolution operation	Sliding filter over input
Kernels/filters	Learnable patterns
Stride and padding	Output size control
Pooling (max, average)	Downsampling
Receptive field	What each neuron "sees"
Feature maps	Intermediate representations
1D vs 2D vs 3D convolutions	Different data types
Depthwise separable convolutions	Efficient variant
Dilated/atrous convolutions	Increased receptive field
Transposed convolutions	Upsampling

Architecture Evolution

Study these in order:

Year	Architecture	Key Innovation
2012	AlexNet	GPU training, ReLU, dropout
2014	VGGNet	Deeper with small (3x3) filters
2014	GoogLeNet/Inception	Multi-scale processing
2015	ResNet	Skip connections (revolutionary!)
2016	DenseNet	Dense connections
2017	MobileNet	Depthwise separable convolutions
2019	EfficientNet	Compound scaling
2022	ConvNeXt	Modernized with Transformer tricks

ConvNeXt Deep Dive

Since ConvNeXt represents modern CNN design, understand:

ConvNeXt Block:
1. Depthwise Conv 7x7 (large kernel, each channel separate)
2. LayerNorm (from Transformers)
3. Linear → 4x expansion (inverted bottleneck)
4. GELU activation (from Transformers)
5. Linear → back to original dim
6. Residual connection

Key modernizations:

Larger kernels (7x7) like Transformer's global attention
LayerNorm instead of BatchNorm
GELU instead of ReLU
Inverted bottleneck (expand then shrink)
Fewer, wider layers

Resources

Resource	Type	Cost
CS231n CNN lectures	Course	Free
"A ConvNet for the 2020s" (ConvNeXt paper)	Paper	Free
d2l.ai CNN chapters	Book	Free

Checkpoint Projects

[ ] Implement Conv2d from scratch
[ ] Build and train LeNet-5 on MNIST
[ ] Implement ResNet-18 from scratch
[ ] Implement ConvNeXt block from scratch
[ ] Train on CIFAR-10, achieve >90% accuracy

5.2 Recurrent Neural Networks (RNNs)

Duration: 2 weeks

Less critical now but provides important context.

Core Concepts

Concept	Description
Vanilla RNN	Basic recurrence
Hidden state	Memory mechanism
BPTT	Backpropagation through time
Vanishing/exploding gradients	Why vanilla RNNs fail
LSTM	Gated memory cells
GRU	Simplified gating
Bidirectional RNNs	Both directions
Sequence-to-sequence	Encoder-decoder

Why Learn RNNs?

Historical context for understanding Transformer motivation
Still used in some applications
Understanding sequential processing concepts

Resources

Resource	Type	Cost
"The Unreasonable Effectiveness of RNNs" by Karpathy	Blog	Free
d2l.ai RNN chapters	Book	Free
Chris Olah's "Understanding LSTM Networks"	Blog	Free

Checkpoint Projects

[ ] Implement vanilla RNN from scratch
[ ] Implement LSTM cell from scratch
[ ] Train character-level language model

5.3 Transformers (Critical!)

Duration: 4-5 weeks

This is the most important architecture to master deeply.

Core Concepts

Concept	Description	Priority
Self-attention	Each position attends to all others	Essential
Query, Key, Value	Attention mechanism components	Essential
Scaled dot-product attention	QK^T/√d_k	Essential
Multi-head attention	Multiple attention patterns	Essential
Positional encoding	Injecting position information	Essential
Feed-forward network	Per-position MLP	Essential
Encoder vs Decoder	Different attention masks	Essential
Causal/masked attention	Autoregressive generation	Essential
Cross-attention	Attending to different sequence	Essential
Layer normalization placement	Pre-norm vs post-norm	Important
Residual connections	Gradient flow	Essential

Attention Mechanism Deeply Understood

Input: X (sequence of embeddings)

Step 1: Project to Q, K, V
    Q = X @ W_q    (queries: what am I looking for?)
    K = X @ W_k    (keys: what do I contain?)
    V = X @ W_v    (values: what do I output?)

Step 2: Compute attention scores
    scores = Q @ K.T / sqrt(d_k)

Step 3: Apply softmax (per query)
    weights = softmax(scores)

Step 4: Weighted sum of values
    output = weights @ V

Multi-Head Attention

Instead of one attention:
- Split into h heads
- Each head has smaller dimension (d_k / h)
- Concatenate outputs
- Project back to original dimension

Why? Different heads learn different patterns:
- Head 1: syntactic relationships
- Head 2: semantic relationships
- Head 3: positional patterns
- etc.

Architecture Variants

Architecture	Type	Key Use
Original Transformer	Encoder-Decoder	Machine translation
BERT	Encoder only	Understanding, embeddings
GPT	Decoder only	Generation
T5	Encoder-Decoder	Text-to-text
Vision Transformer (ViT)	Encoder	Image classification
DETR	Encoder-Decoder	Object detection

Modern Improvements

Improvement	Description
RoPE	Rotary position embeddings
ALiBi	Attention with Linear Biases
Flash Attention	Memory-efficient attention
Grouped Query Attention	Efficient KV sharing
RMS Norm	Simplified normalization
SwiGLU	Gated activation
Pre-norm	LayerNorm before attention

Resources

Resource	Type	Cost
"The Illustrated Transformer" by Jay Alammar	Blog	Free
"Attention Is All You Need" (original paper)	Paper	Free
Andrej Karpathy's "Let's build GPT"	YouTube	Free
"The Annotated Transformer" by Harvard NLP	Blog	Free
d2l.ai Attention chapters	Book	Free
Lilian Weng's "The Transformer Family"	Blog	Free

Checkpoint Projects

[ ] Implement scaled dot-product attention from scratch
[ ] Implement multi-head attention from scratch
[ ] Implement positional encoding (sinusoidal and learned)
[ ] Build complete Transformer encoder from scratch
[ ] Build complete Transformer decoder from scratch
[ ] Train small GPT on character-level text
[ ] Implement cross-attention for encoder-decoder model

5.4 Hybrid Architectures

Duration: 2 weeks

Understanding how to combine architectures.

Common Combinations

Combination	Example	Use Case
CNN + Transformer	ViT, Swin Transformer	Vision
CNN + Attention	CBAM, SE-Net	Vision enhancement
Transformer + CNN head	Detection Transformers	Object detection
ConvNeXt + Cross-Attention	SupertonicTTS style	Multimodal

Design Principles

Match architecture to data structure
- Images: Start with CNN for local features
- Sequences: Transformer for global relationships
- Both: Hybrid
Consider computational budget
- Attention: O(n²) in sequence length
- Convolution: O(n) in sequence length
- Use CNN for long sequences, attention where needed
Conditioning mechanisms
- Cross-attention: Flexible, powerful
- Concatenation: Simple, limited
- FiLM/AdaLN: Efficient for style transfer
- Addition: Global conditioning

Phase 6: Generative Models

Duration: 10-12 weeks

This phase covers how to generate new data.

6.1 Autoencoders

Duration: 2 weeks

The foundation of generative latent models.

Concepts

Concept	Description
Encoder	Compress input to latent
Decoder	Reconstruct from latent
Latent space	Compressed representation
Bottleneck	Forces meaningful compression
Reconstruction loss	Training objective

Types

Type	Description	Use Case
Vanilla AE	Basic encoder-decoder	Compression
Denoising AE	Reconstruct from noisy input	Robust features
Sparse AE	L1 penalty on latent	Interpretable features
Contractive AE	Penalty on Jacobian	Robust latent

Checkpoint

[ ] Implement autoencoder for MNIST
[ ] Visualize latent space
[ ] Implement denoising autoencoder

6.2 Variational Autoencoders (VAEs)

Duration: 2-3 weeks

Probabilistic generative model.

Key Concepts

Concept	Description
Probabilistic encoder	Output distribution, not point
Reparameterization trick	Enable backprop through sampling
KL divergence	Regularize latent to prior
ELBO	Evidence Lower Bound objective
Latent prior	Usually N(0, I)
Posterior collapse	When decoder ignores latent

The Math

Encoder outputs: μ, σ (parameters of q(z|x))
Sampling: z = μ + σ * ε, where ε ~ N(0, I)
Loss = Reconstruction + β * KL(q(z|x) || p(z))

Variants

Variant	Innovation
β-VAE	Disentangled representations
VQ-VAE	Discrete latents
VAE-GAN	Combined with adversarial loss
Hierarchical VAE	Multiple latent levels

Resources

Resource	Type	Cost
"Auto-Encoding Variational Bayes" (original paper)	Paper	Free
"Tutorial on VAEs" by Carl Doersch	Paper	Free
Lilian Weng's VAE blog	Blog	Free

Checkpoint Projects

[ ] Implement VAE from scratch
[ ] Train on MNIST, generate samples
[ ] Implement reparameterization trick
[ ] Interpolate in latent space
[ ] Implement β-VAE, compare disentanglement

6.3 Generative Adversarial Networks (GANs)

Duration: 2-3 weeks

Adversarial training paradigm.

Key Concepts

Concept	Description
Generator	Creates fake samples
Discriminator	Distinguishes real vs fake
Adversarial training	Two-player game
Mode collapse	Generator produces limited variety
Training instability	GANs are hard to train

The Training Loop

For each batch:
    1. Train Discriminator:
       - Real samples → D should output 1
       - Fake samples (from G) → D should output 0

    2. Train Generator:
       - Generate fake samples
       - D(fake) should output 1 (fool discriminator)

Evolution of GANs

Year	Model	Innovation
2014	GAN	Original
2015	DCGAN	Convolutional architecture
2017	WGAN	Wasserstein distance
2017	WGAN-GP	Gradient penalty
2018	BigGAN	Large scale
2019	StyleGAN	Style-based generator
2020	StyleGAN2	Improved quality

Resources

Resource	Type	Cost
"Generative Adversarial Networks" (original)	Paper	Free
"NIPS 2016 GAN Tutorial" by Goodfellow	Tutorial	Free
"GAN Hacks" repository	GitHub	Free

Checkpoint Projects

[ ] Implement vanilla GAN from scratch
[ ] Train on MNIST
[ ] Implement DCGAN
[ ] Implement Wasserstein loss
[ ] Diagnose and fix mode collapse

6.4 Diffusion Models

Duration: 3-4 weeks

Current state-of-the-art for generation.

Key Concepts

Concept	Description
Forward process	Gradually add noise to data
Reverse process	Learn to denoise
Noise schedule	How fast to add noise
Score function	Gradient of log probability
U-Net	Common architecture
Classifier-free guidance	Conditional generation

The Process

Forward (fixed):
x_0 → x_1 → x_2 → ... → x_T (pure noise)
     +ε     +ε          +ε

Reverse (learned):
x_T → x_{T-1} → ... → x_1 → x_0 (clean data)
    predict   predict      predict
    noise     noise        noise

Training

1. Sample clean data x_0
2. Sample timestep t
3. Sample noise ε
4. Create noisy version: x_t = √(ᾱ_t) * x_0 + √(1-ᾱ_t) * ε
5. Predict noise: ε_pred = model(x_t, t)
6. Loss = ||ε - ε_pred||²

Inference

1. Start with pure noise x_T
2. For t = T, T-1, ..., 1:
   - Predict noise: ε_pred = model(x_t, t)
   - Remove noise: x_{t-1} = denoise(x_t, ε_pred, t)
3. Return x_0

Important Papers

Paper	Contribution
"Deep Unsupervised Learning using Nonequilibrium Thermodynamics"	Original idea
"Denoising Diffusion Probabilistic Models" (DDPM)	Practical formulation
"Score-Based Generative Modeling through SDEs"	Unified framework
"Denoising Diffusion Implicit Models" (DDIM)	Faster sampling
"Classifier-Free Diffusion Guidance"	Better conditioning
"High-Resolution Image Synthesis with Latent Diffusion"	Stable Diffusion

Resources

Resource	Type	Cost
"What are Diffusion Models?" by Lilian Weng	Blog	Free
"The Annotated Diffusion Model"	Blog	Free
Hugging Face Diffusers course	Tutorial	Free
"Understanding Diffusion Models: A Unified Perspective"	Paper	Free

Checkpoint Projects

[ ] Implement forward diffusion process
[ ] Implement simple noise prediction network
[ ] Train DDPM on MNIST
[ ] Implement DDIM sampling
[ ] Implement classifier-free guidance
[ ] Train on CIFAR-10

6.5 Flow Matching

Duration: 2-3 weeks

Modern alternative to diffusion.

Key Concepts

Concept	Description
Continuous Normalizing Flows	ODE-based transformation
Vector field	Direction at each point
Optimal transport	Straight paths
Simulation-free training	Efficient training

Comparison with Diffusion

Aspect	Diffusion	Flow Matching
Predicts	Noise ε	Direction v
Update	Subtract noise	Add direction
Paths	Can be curved	Often straighter
Steps needed	Often 50-1000	Often 10-50
Training	Noise prediction	Direction prediction

The Math

Interpolation:
x_t = (1-t) * x_0 + t * x_1
where x_0 ~ noise, x_1 ~ data

True velocity:
v = x_1 - x_0

Training:
Loss = ||v_pred - v||²

Resources

Resource	Type	Cost
"Flow Matching for Generative Modeling"	Paper	Free
"Flow Straight and Fast"	Paper	Free
Meta's flow matching tutorial	Blog	Free

Checkpoint Projects

[ ] Implement flow matching training
[ ] Compare with diffusion on same task
[ ] Experiment with different ODE solvers
[ ] Implement conditional flow matching

6.6 Latent Generative Models

Duration: 2 weeks

Combining autoencoders with generative models.

Architecture Pattern

Training:
1. Train autoencoder: data ↔ latent
2. Train generative model in latent space

Inference:
1. Generate latent from noise
2. Decode latent to data

Why Latent Space?

Benefit	Description
Computational efficiency	Smaller space to model
Semantic compression	Latent captures meaning
Decoupled training	Separate reconstruction from generation

Examples

Model	Autoencoder	Generative
Stable Diffusion	VAE	Diffusion
DALL-E	VQ-VAE	Transformer
AudioLDM	VAE	Diffusion

Checkpoint Projects

[ ] Train VAE, then train diffusion in latent space
[ ] Compare quality vs training in pixel space
[ ] Experiment with different latent dimensions

Phase 7: Advanced Training Techniques

Duration: 4-6 weeks

Scaling to real-world systems.

7.1 Large-Scale Training

Topics

Topic	Description
Multi-GPU training	DataParallel, DistributedDataParallel
Mixed precision	FP16/BF16 training
Gradient accumulation	Simulate larger batches
Gradient checkpointing	Trade compute for memory
Model parallelism	Split model across GPUs
Pipeline parallelism	Sequential stages

Resources

Resource	Type	Cost
PyTorch Distributed tutorial	Documentation	Free
Hugging Face Accelerate	Library + docs	Free
DeepSpeed documentation	Library + docs	Free

7.2 Stabilizing Training

Techniques

Technique	Purpose
Learning rate warmup	Stable early training
Learning rate scheduling	Cosine, linear decay
Gradient clipping	Prevent explosions
Weight decay	Regularization
EMA (Exponential Moving Average)	Smooth checkpoints
Proper initialization	Stable forward pass

7.3 Hyperparameter Optimization

Approaches

Approach	Description
Grid search	Exhaustive (expensive)
Random search	Often better than grid
Bayesian optimization	Sample efficient
Population-based training	Adaptive

7.4 Debugging Deep Learning

Common Issues and Solutions

Issue	Diagnosis	Solution
Loss NaN	Gradient explosion	Gradient clipping, lower LR
Loss plateau	Stuck in local minimum	LR schedule, different init
Overfitting	Train/val gap	Regularization, data augmentation
Underfitting	High train loss	Larger model, longer training
Mode collapse (GAN)	Limited variety	Spectral norm, WGAN

Debugging Tools

# Things to monitor:
- Loss curves (train and val)
- Gradient norms per layer
- Activation statistics
- Weight distributions
- Learning rate
- GPU memory usage

Phase 8: Domain Specialization

Choose one or more domains to specialize in.

8.1 Computer Vision

Duration: 4-6 weeks

Topics

Topic	Models
Image classification	ResNet, ViT, ConvNeXt
Object detection	YOLO, DETR
Semantic segmentation	U-Net, SegFormer
Instance segmentation	Mask R-CNN
Image generation	GAN, Diffusion
Image-to-image	pix2pix, CycleGAN

Resources

Resource	Type	Cost
CS231n Stanford	Course	Free
"Deep Learning for Vision" (d2l.ai)	Book	Free

8.2 Natural Language Processing

Duration: 4-6 weeks

Topics

Topic	Models
Text classification	BERT
Named entity recognition	BERT + CRF
Machine translation	Transformer, T5
Language modeling	GPT
Text generation	GPT, LLaMA
Question answering	BERT, T5

Resources

Resource	Type	Cost
CS224n Stanford	Course	Free
Hugging Face NLP course	Course	Free
"Speech and Language Processing"	Book	Free online

8.3 Audio and Speech

Duration: 4-6 weeks

Fundamentals

Topic	Description
Digital audio	Sampling, quantization
Fourier transform	Time to frequency
Spectrograms	Time-frequency representation
Mel scale	Perceptual frequency scale
MFCC	Classic features

Tasks

Task	Description
Speech recognition (ASR)	Audio → text
Text-to-speech (TTS)	Text → audio
Voice conversion	Change speaker identity
Music generation	Create music
Audio classification	Classify sounds
Source separation	Unmix audio

Key Models

Model	Task
Wav2Vec 2.0	Speech representation
Whisper	Speech recognition
Tacotron	TTS
VITS	TTS
HiFi-GAN	Vocoder
Vocos	Vocoder (ConvNeXt)

Resources

Resource	Type	Cost
"Speech and Language Processing"	Book	Free online
ESPnet tutorials	Code	Free
librosa documentation	Library	Free

8.4 Multimodal Learning

Duration: 4-6 weeks

Combining multiple modalities.

Topics

Topic	Examples
Vision-Language	CLIP, BLIP
Text-to-Image	Stable Diffusion, DALL-E
Image-to-Text	Image captioning
Audio-Visual	Video understanding
Any-to-any	Unified models

Phase 9: Research and Innovation

9.1 Reading Papers Effectively

Strategy

First pass (5 min): Title, abstract, figures, conclusion
Second pass (30 min): Introduction, method overview, experiments
Third pass (2+ hrs): Full details, math, implementation

Where to Find Papers

Source	Description
arXiv	Preprints
Papers With Code	Papers + implementations
Google Scholar	Search + citations
Semantic Scholar	AI-powered search
Conference proceedings	ICML, NeurIPS, ICLR, CVPR

9.2 Implementing Papers

Process

Read paper multiple times
Find official code (if available)
Identify key components
Implement incrementally
Reproduce results on small scale first
Debug against reference implementation

9.3 Staying Current

Resources

Resource	Type
Twitter/X ML community	Social
r/MachineLearning	Forum
Papers With Code newsletter	Email
Two Minute Papers (YouTube)	Video
The Batch (Andrew Ng)	Newsletter

Project Milestones

Beginner Projects

Project	Skills Practiced
MNIST classifier (MLP)	Basics, training loop
CIFAR-10 classifier (CNN)	Convolutions, augmentation
Sentiment analysis (RNN)	Sequential processing
Character-level LM (Transformer)	Attention, generation

Intermediate Projects

Project	Skills Practiced
Image autoencoder	Encoder-decoder, latent space
VAE for faces	Probabilistic models
DCGAN for images	Adversarial training
BERT fine-tuning	Transfer learning
Small GPT training	Language modeling

Advanced Projects

Project	Skills Practiced
Diffusion model for images	Modern generative models
Latent diffusion model	Combining AE + diffusion
Vision Transformer from scratch	Full architecture
Multimodal model	Cross-attention, multiple encoders
Custom TTS system	Full pipeline integration

Capstone Ideas

Project	Description
Novel architecture design	Combine techniques in new way
Reproduce recent paper	Validate understanding
Domain application	Apply to specific problem
Efficiency improvement	Make existing model faster/smaller

Resource Summary

Essential Books

Book	Focus	Cost
"Deep Learning" by Goodfellow et al.	Theory	Free online
"Dive into Deep Learning" (d2l.ai)	Practical	Free online
"Hands-On ML" by Géron	Applied	~$60
"Understanding Deep Learning" by Prince	Modern	Free online

Essential Courses

Course	Provider	Cost
CS231n (CNNs)	Stanford	Free
CS224n (NLP)	Stanford	Free
Fast.ai	Fast.ai	Free
Deep Learning Specialization	Coursera	Free to audit

Essential Blogs

Blog	Focus
Jay Alammar	Visual explanations
Lilian Weng	Comprehensive surveys
Chris Olah	Neural network intuition
Sebastian Ruder	NLP
The Gradient	Research summaries

Essential YouTube Channels

Channel	Focus
3Blue1Brown	Math intuition
Andrej Karpathy	Implementation
Yannic Kilcher	Paper reviews
Two Minute Papers	Research summaries
StatQuest	Statistics

Essential Code Repositories

Repository	Content
lucidrains	Clean implementations
huggingface/transformers	Production models
karpathy/nanoGPT	Minimal GPT
huggingface/diffusers	Diffusion models
labmlai/annotated_deep_learning_paper_implementations	Paper implementations

Final Notes

Keys to Success

Consistency over intensity: Regular practice beats sporadic cramming
Implement everything: Reading is not enough
Start simple: Get basics working before adding complexity
Debug systematically: Use visualization, logging, unit tests
Join communities: Learn from others, ask questions
Read code: Study implementations, not just papers
Build projects: Apply knowledge to real problems

Common Pitfalls to Avoid

Pitfall	Solution
Tutorial hell	Build original projects
Skipping math	Invest time in foundations
Only reading, not coding	Implement everything
Jumping to advanced topics	Master basics first
Working in isolation	Join communities
Ignoring debugging skills	Learn systematic debugging

Measure Your Progress

You're ready for the next level when you can:

[ ] Basics: Implement MLP, train on MNIST without tutorials
[ ] CNN: Implement ResNet block, explain receptive fields
[ ] Transformer: Implement attention from scratch, explain every component
[ ] Generative: Train VAE, GAN, or diffusion model from scratch
[ ] Integration: Design hybrid architecture for a new problem
[ ] Research: Read papers and implement them independently

Last updated: November 2025

Remember: The goal is not to memorize, but to understand deeply enough to create something new.

Deep Learning Architecture Design: A Comprehensive Learning Roadmap

Table of Contents

Overview

What This Roadmap Covers

Prerequisites

Time Estimate

Phase 1: Mathematical Foundations

1.1 Linear Algebra (Essential)

Topics to Master

Resources

Checkpoint

1.2 Calculus (Essential)

Topics to Master

Resources

Checkpoint

1.3 Probability and Statistics (Essential)

Topics to Master

Resources

Checkpoint

1.4 Optimization (Important)

Topics to Master

Resources

Phase 2: Programming and Tools

2.1 Python Proficiency

Topics to Master

Resources

2.2 NumPy (Essential)

Topics to Master

Resources

2.3 PyTorch (Essential)

Topics to Master

Resources

Checkpoint Project

2.4 Development Environment

Essential Tools

Useful Tools

Phase 3: Machine Learning Fundamentals

3.1 Core Concepts

Topics to Master

3.2 Classical Algorithms

3.3 Resources

3.4 Checkpoint Project

Phase 4: Deep Learning Core Concepts

4.1 Neural Network Fundamentals

Topics to Master

Activation Functions Deep Dive

Understanding Backpropagation

4.2 Optimizers

4.3 Normalization Techniques

4.4 Resources

4.5 Checkpoint Projects

Phase 5: Architecture Deep Dives

5.1 Convolutional Neural Networks (CNNs)

Core Concepts

Architecture Evolution

ConvNeXt Deep Dive

Resources

Checkpoint Projects

5.2 Recurrent Neural Networks (RNNs)

Core Concepts

Why Learn RNNs?

Resources

Checkpoint Projects

5.3 Transformers (Critical!)

Core Concepts

Attention Mechanism Deeply Understood

Multi-Head Attention

Architecture Variants

Modern Improvements

Resources

Checkpoint Projects

5.4 Hybrid Architectures

Common Combinations

Design Principles

Phase 6: Generative Models

6.1 Autoencoders

Concepts

Types

Checkpoint

6.2 Variational Autoencoders (VAEs)