#1a Basics +UI

10 Modules · Hands-On

🔥 PyTorch Hands-On
Course for Beginners

Practical deep learning with real code, exercises, quizzes, and assignments.

10 Modules

Code + Exercises

MCQ Quizzes

01 PyTorch Basics

// Overview

This module introduces PyTorch tensors, the fundamental data structure used to store data in PyTorch. You learn how to create tensors, inspect their shape, and understand data types.

// Learning Outcomes

▶Understand what a tensor is

▶Create tensors using PyTorch

▶Inspect tensor shape and data type

// Code

python

import torch

x = torch.tensor([1, 2, 3])
print(x)
print(x.dtype)
print(x.shape)

📝

Exercises

• Create a tensor with values [10, 20, 30]
• Print tensor shape and data type
• Create a 2×2 zero tensor

🎯

Assignment

Create a tensor representing marks of 5 students and compute the average.

🧠

MCQ Quiz

1. What is the core data structure in PyTorch?

A. List

B. Array

C. Tensor

D. Matrix

2. Which function creates a tensor?

A. torch.create()

B. torch.tensor()

C. torch.make()

D. torch.build()

3. What does x.shape return?

A. Data type

B. Dimensions of tensor

C. Memory size

D. Values

4. PyTorch is mainly used for?

A. Web design

B. Deep learning

C. OS development

D. Gaming

5. Which library supports GPU acceleration?

A. Pandas

B. NumPy

C. PyTorch

D. Matplotlib

02 Tensor Operations

// Overview

This module covers basic mathematical operations on tensors such as addition, multiplication, and dot products using PyTorch.

// Learning Outcomes

▶Perform arithmetic operations on tensors

▶Apply element-wise operations

▶Understand tensor math behavior

// Code

python

import torch

a = torch.tensor([2, 4])
b = torch.tensor([1, 3])

print(a + b)
print(a * b)
print(torch.dot(a, b))

📝

Exercises

• Subtract tensors
• Compute mean

🎯

Assignment

Simulate monthly expenses using tensors.

🧠

MCQ Quiz

1. Which operator performs element-wise addition?

A. +

B. dot()

C. sum()

D. mean()

2. What does torch.dot() compute?

A. Element-wise product

B. Dot product

C. Matrix multiplication

D. Sum

3. Which operation multiplies tensors element-wise?

A. torch.dot()

B. *

C. torch.mul_all()

D. add()

4. Tensor operations are?

A. Scalar only

B. Slow

C. Vectorized

D. Manual

5. Which function calculates average?

A. torch.sum()

B. torch.mean()

C. torch.avg()

D. torch.calc()

03 Autograd

// Overview

This module explains PyTorch's autograd system, which automatically computes gradients required for training neural networks.

// Learning Outcomes

▶Understand automatic differentiation

▶Compute gradients using backward()

▶Explain the role of gradients in learning

// Code

python

import torch

x = torch.tensor(2.0, requires_grad=True)
y = x**3 + 4*x
y.backward()
print(x.grad)

📝

Exercises

• Compute gradient of y = x² + 5x
• Change value of x and observe gradient

🎯

Assignment

Derive gradient for a cubic equation using autograd.

🧠

MCQ Quiz

1. What does autograd compute?

A. Values

B. Gradients

C. Shapes

D. Loss

2. Which flag enables gradient tracking?

A. grad=True

B. requires_grad=True

C. track=True

D. backward=True

3. Which method computes gradients?

A. grad()

B. backward()

C. step()

D. compute()

4. Gradients are used in?

A. Evaluation

B. Optimization

C. Data loading

D. Saving

5. Gradients are stored in?

A. x.value

B. x.grad

C. x.data

D. x.diff

04 Linear Regression

// Overview

In this module, you build a simple linear regression model using PyTorch's built-in layers, loss functions, and optimizers.

// Learning Outcomes

▶Build a linear regression model

▶Use loss functions and optimizers

▶Train a model using gradient descent

// Code

python

import torch

x = torch.tensor([[1.],[2.],[3.]])
y = torch.tensor([[2.],[4.],[6.]])

model = torch.nn.Linear(1,1)
loss_fn = torch.nn.MSELoss()
optimizer = torch.optim.SGD(model.parameters(), lr=0.01)

for _ in range(200):
    loss = loss_fn(model(x), y)
    optimizer.zero_grad()
    loss.backward()
    optimizer.step()

print(model.weight.item(), model.bias.item())

📝

Exercises

• Change learning rate and observe loss
• Increase training epochs

🎯

Assignment

Train a model to predict salary based on years of experience.

🧠

MCQ Quiz

1. Linear regression predicts?

A. Categories

B. Continuous values

C. Images

D. Text

2. Which loss is used for regression?

A. BCELoss

B. MSELoss

C. CrossEntropy

D. L1Loss

3. Which optimizer updates weights?

A. Loss

B. Optimizer

C. Dataset

D. Model

4. Learning rate controls?

A. Data size

B. Step size

C. Accuracy

D. Output

5. Which layer is used?

A. nn.Linear

B. nn.Conv2d

C. nn.RNN

D. nn.Dropout

05 Dataset & DataLoader

// Overview

This module introduces Dataset and DataLoader classes to efficiently load data in batches during training.

// Learning Outcomes

▶Create a custom Dataset class

▶Use DataLoader for batching

▶Understand data iteration during training

// Code

python

from torch.utils.data import Dataset, DataLoader
import torch

class MyData(Dataset):
    def __init__(self):
        self.x = torch.arange(1,11).float()
        self.y = self.x * 2

    def __len__(self):
        return len(self.x)

    def __getitem__(self, i):
        return self.x[i], self.y[i]

loader = DataLoader(MyData(), batch_size=2)
for x, y in loader:
    print(x, y)

📝

Exercises

• Change batch size to 5
• Add random noise to dataset

🎯

Assignment

Create a Dataset class for student marks and grades.

🧠

MCQ Quiz

1. Dataset class is used to?

A. Store data

B. Train model

C. Optimize loss

D. Save model

2. DataLoader provides?

A. Prediction

B. Batching

C. Loss

D. Accuracy

3. __getitem__ returns?

A. Length

B. One sample

C. All data

D. Batch size

4. DataLoader improves?

A. Accuracy

B. Efficiency

C. Loss

D. Weights

5. Batch size controls?

A. Epochs

B. Samples per iteration

C. Learning rate

D. Layers

06 Neural Network

// Overview

This module demonstrates how to construct a multi-layer neural network using linear layers and activation functions.

// Learning Outcomes

▶Understand neural network architecture

▶Create hidden layers

▶Apply activation functions

// Code

python

import torch.nn as nn

model = nn.Sequential(
    nn.Linear(2, 4),
    nn.ReLU(),
    nn.Linear(4, 1)
)
print(model)

📝

Exercises

• Add another hidden layer
• Change activation function to Tanh

🎯

Assignment

Design a neural network with 3 hidden layers.

🧠

MCQ Quiz

1. Neural networks consist of?

A. Layers

B. Tables

C. Loops

D. Files

2. ReLU is a?

A. Loss

B. Activation function

C. Optimizer

D. Dataset

3. Hidden layers increase?

A. Model capacity

B. Data size

C. Speed

D. Storage

4. nn.Sequential is used to?

A. Stack layers

B. Train model

C. Load data

D. Save weights

5. Output layer size depends on?

A. Problem type

B. Dataset size

C. Epochs

D. Optimizer

07 Training Loop

// Overview

This module explains the standard training loop used in PyTorch, including forward pass, loss computation, backward pass, and parameter updates.

// Learning Outcomes

▶Implement a complete training loop

▶Understand model parameter updates

▶Monitor training progress

// Code

python

import torch, torch.nn as nn

x = torch.randn(100, 2)
y = x.sum(dim=1, keepdim=True)

model = nn.Linear(2, 1)
opt = torch.optim.Adam(model.parameters(), lr=0.01)
loss_fn = nn.MSELoss()

for _ in range(100):
    loss = loss_fn(model(x), y)
    opt.zero_grad()
    loss.backward()
    opt.step()

📝

Exercises

• Print loss every 10 epochs
• Change optimizer to SGD

🎯

Assignment

Implement early stopping logic.

🧠

MCQ Quiz

1. First step in training loop?

A. Forward pass

B. Backward pass

C. Save model

D. Eval

2. backward() computes?

A. Gradients

B. Loss

C. Output

D. Accuracy

3. optimizer.step() does?

A. Update weights

B. Reset loss

C. Load data

D. Print output

4. optimizer.zero_grad() is used to?

A. Clear gradients

B. Save memory

C. Increase speed

D. Stop training

5. Epoch means?

A. One full dataset pass

B. One batch

C. One layer

D. One sample

08 Evaluation

// Overview

This module focuses on evaluating trained models by switching to evaluation mode and disabling gradient computation.

// Learning Outcomes

▶Use model.eval() correctly

▶Disable gradients during evaluation

▶Run inference safely

// Code

python

model.eval()
with torch.no_grad():
    print(model(torch.tensor([[3.0, 5.0]])))

📝

Exercises

• Test model with multiple inputs
• Compare outputs in train vs eval mode

🎯

Assignment

Write evaluation code for accuracy calculation.

🧠

MCQ Quiz

1. model.eval() is used for?

A. Evaluation

B. Training

C. Saving

D. Loading

2. torch.no_grad() does?

A. Disables gradients

B. Speeds GPU

C. Clears memory

D. Stops training

3. Evaluation mode affects?

A. Dropout & BatchNorm

B. Loss only

C. Optimizer

D. Dataset

4. Evaluation should be done?

A. After training

B. Before training

C. During loading

D. During saving

5. Gradients are needed during eval?

A. No

B. Yes

C. Sometimes

D. Always

09 Save & Load

// Overview

This module teaches how to save trained models to disk and load them later without retraining.

// Learning Outcomes

▶Save model parameters

▶Reload trained models

▶Reuse models for deployment

// Code

python

torch.save(model.state_dict(), "model.pth")
model.load_state_dict(torch.load("model.pth"))

📝

Exercises

• Save model after training
• Reload model and predict

🎯

Assignment

Implement versioned model saving.

🧠

MCQ Quiz

1. state_dict stores?

A. Model parameters

B. Dataset

C. Loss

D. Code

2. torch.save() is used to?

A. Save model

B. Train model

C. Evaluate

D. Load data

3. Model loading requires?

A. Same architecture

B. Same data

C. Same loss

D. Same optimizer

4. Saved models are useful for?

A. Deployment

B. Data cleaning

C. Visualization

D. Preprocessing

5. File extension commonly used?

A. .pth

B. .csv

C. .txt

D. .json

10 Binary Classification Final

// Overview

This module integrates all previous concepts to build a complete binary classification model using a neural network.

// Learning Outcomes

▶Build an end-to-end classification model

▶Use sigmoid activation and BCELoss

▶Train and evaluate a real-world model

// Code

python

import torch, torch.nn as nn

x = torch.randn(200, 2)
y = (x.sum(1) > 0).float().unsqueeze(1)

model = nn.Sequential(
    nn.Linear(2, 8),
    nn.ReLU(),
    nn.Linear(8, 1),
    nn.Sigmoid()
)

loss_fn = nn.BCELoss()
opt = torch.optim.Adam(model.parameters(), lr=0.01)

for _ in range(300):
    loss = loss_fn(model(x), y)
    opt.zero_grad()
    loss.backward()
    opt.step()

print("Training done")

🚀

Mini Project

• Generate your own dataset
• Train binary classifier
• Evaluate accuracy

🏆

Final Assignment

Build a spam vs ham classifier using PyTorch.

🧠

MCQ Quiz

1. Binary classification predicts?

A. Two classes

B. Multiple classes

C. Numbers

D. Images

2. Sigmoid output range?

A. 0 to 1

B. -1 to 1

C. 0 to 10

D. Any value

3. BCELoss is used for?

A. Binary classification

B. Regression

C. Clustering

D. Detection

4. Threshold commonly used?

A. 0.5

B. 1.0

C. 0.1

D. 0.9

5. This module combines?

A. All previous concepts

B. Only tensors

C. Only datasets

D. Only saving

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