Understand the math.
Build the model.
Explain the why.
A hands-on Deep Learning Handbook built for genuine fundamentals and technical interviews. Every chapter will be its own readable HTML lesson—with plain-English explanations, mathematical derivations, live visualizations, real scenarios, NumPy, PyTorch, experiments, and interview prep.
Designed for learners with basic Python and Machine Learning knowledge · Start with Part I: Foundations
Every concept, six ways.
You should never have to accept “it just works.” Every future chapter page will follow this teaching system and provide runnable code.
Simple meaning
Understand what the idea solves, using plain language and familiar analogies.
Derive it
Break down each symbol, assumption and calculation with worked numerical examples.
See it move
Manipulate learning rates, gradients, activations and model outputs in live diagrams.
Code twice
First implement with Python/NumPy; then build the practical PyTorch version.
Use real scenarios
Work with realistic datasets, split data correctly, evaluate models and debug mistakes.
Defend your choices
Explain trade-offs, solve coding tasks, answer why-questions and diagnose failures.
No black boxes
First understand the mathematics and computation. Only then rely on high-level APIs.
Reusable skills
Concept map, formulas, annotated code, animated demo, experiments, quiz and sources.
Reading ≠ mastery. A chapter is finished when you can derive its key equation, implement the central idea, explain failure modes, and pass its interview test.
Where Part I takes you next
We will complete these four volumes in sequence. This home page includes the full detailed syllabus for the first volume only.
Deep Learning Foundations
Math, neural networks, backpropagation, optimization, generalization, PyTorch engineering.
22 chapters · 8–12 weeks estimatedNatural Language Processing
Text preprocessing, language representation, embeddings, RNNs, LSTMs, attention.
Planned · 4–6 weeksTransformers & LLMs
Transformers, pretraining, fine-tuning, LoRA, RAG, evaluation and inference.
Planned · 6–8 weeksComputer Vision
CNNs, ResNet, classification, detection, segmentation and Vision Transformers.
Planned · 5–7 weeksYour Deep Learning syllabus.
Six progressive modules. Expand a chapter's details to see exactly what you will study, derive, code and explain in interviews. Chapter pages will be delivered as separate HTML files as we write them.
The mathematics should move.
A preview of the learning tools we'll include in the separate chapter pages. Change the parameter and learning rate to watch the optimization process. This simple example uses the loss L(w) = (w − 3)².
01 · Gradient descent explorer
The derivative tells us the local slope. Gradient descent subtracts a learning-rate-scaled gradient to seek a lower loss.
02 · Worked real-life example
Imagine adjusting a simple model that estimates delivery cost from travel distance:
Suppose the actual charge for a 4 km delivery is ₹50. If w = 8 and b = 10, the model predicts:
Its squared error is (42 − 50)² = 64. We can calculate how a change in the weight or bias would affect this error and update both parameters during training.
4 km→Model
8x + 10→Prediction
₹42→Loss
64
03 · From math to real code
# NumPy: a single gradient step
import numpy as np
x, y = 4.0, 50.0
w, b = 8.0, 10.0
lr = 0.01
prediction = w * x + b
error = prediction - y
dw = 2 * error * x
db = 2 * error
w -= lr * dw
b -= lr * db
print(w, b) # 8.64, 10.16A one-example illustrative update. Later chapters cover datasets, scaling, learning-rate selection and convergence.
What you will be able to do.
① Explain the fundamentals
Distinguish architectures, losses, activations, gradients, optimizers and generalization concepts in simple and technical language.
② Derive core mathematics
Work with tensors, calculus, backpropagation and gradient updates without blindly memorizing formulas.
③ Implement from scratch
Code linear regression, a multilayer perceptron, gradient checks and training components in NumPy.
④ Engineer with PyTorch
Build data pipelines, models, training loops, evaluation routines, checkpoints and reproducible experiments.
⑤ Debug and evaluate
Diagnose exploding gradients, overfitting, loss instability, shape mismatches, data leakage and poor metrics.
⑥ Interview with confidence
Reason through whiteboard questions, implement algorithms, discuss trade-offs and explain a complete project.
Primary references, not random tutorials.
We will link to official documentation and reputable learning resources in individual chapter pages. Framework behavior can change, so we will cite the current documentation and explain the version assumptions when needed.
Chapter 1 · What is Deep Learning?
The first individual chapter page will begin with AI vs ML vs Deep Learning, parameters and predictions, linear regression, loss functions, the chain rule, and gradient descent—with an interactive training animation, worked numerical problems, NumPy and PyTorch code, a quiz and interview questions.
Proposed standalone filename: chapter-01-introduction.html · This page is the syllabus/home page, not a substitute for full chapter lessons.