Tensor Operations (Part 3)

Module 3: Tensor Operations — Part 4: Practice Projects

This module provides hands-on practice with PyTorch tensor operations, including matrix calculations, tensor reshaping, dimension manipulation, concatenation, stacking, splitting, and real-world deep learning workflows.

The goal is to move from learning individual PyTorch tensor functions to using them together in practical problems.

Learning Objectives

After completing this module, you will be able to:

• Build a matrix calculator using PyTorch tensors.
• Perform arithmetic and matrix operations with PyTorch.
• Apply tensor reduction operations such as sum, mean, maximum, and minimum.
• Reshape and flatten tensors for neural network processing.
• Add and remove tensor dimensions using unsqueeze() and squeeze().
• Rearrange tensor dimensions using permute() and transpose().
• Concatenate, stack, split, and chunk tensors.
• Prepare image tensors for convolutional neural networks.
• Work with batch dimensions correctly.
• Analyze tensor data using reduction operations.
• Solve practical PyTorch tensor manipulation problems.
• Apply tensor operations commonly used in CNN and Transformer workflows.

Why Tensor Operations Matter in PyTorch

PyTorch tensor operations are the foundation of almost every deep learning computation.

A neural network continuously transforms tensors:

textCopy
1Input Data
2    ↓
3Tensor
4    ↓
5Shape Transformation
6    ↓
7Neural Network Layer
8    ↓
9Matrix Multiplication
10    ↓
11Activation
12    ↓
13Loss Calculation
14    ↓
15Gradient Computation
16    ↓
17Updated Tensor

For example, an image classification model may process data through shapes such as:

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1Image
2(224, 224, 3)
3      ↓
4Permute
5(3, 224, 224)
6      ↓
7Add Batch Dimension
8(1, 3, 224, 224)
9      ↓
10Convolution
11      ↓
12Feature Maps
13      ↓
14Flatten
15      ↓
16Fully Connected Layer

Understanding tensor shapes and operations is therefore essential for learning PyTorch, deep learning, computer vision, neural networks, CNNs, and Transformers.

Project 1: PyTorch Matrix Calculator

Project Overview

In this project, you will build a simple matrix calculator with PyTorch.

The calculator demonstrates:

• Matrix addition
• Matrix subtraction
• Element-wise multiplication
• Matrix multiplication
• Matrix division
• Transpose
• Tensor shape inspection
• Sum
• Mean
• Maximum
• Minimum

This project combines several PyTorch tensor operations into a single practical program.

Step 1: Import PyTorch

pythonCopy
1import torch

Step 2: Create Two Matrices

We will create two 2 × 2 floating-point matrices.

pythonCopy
1A = torch.tensor(
2    [[1, 2],
3     [3, 4]],
4    dtype=torch.float32
5)
6
7B = torch.tensor(
8    [[5, 6],
9     [7, 8]],
10    dtype=torch.float32
11)
12
13print("Matrix A:")
14print(A)
15
16print("\nMatrix B:")
17print(B)

Output:

textCopy
1Matrix A:
2tensor([[1., 2.],
3        [3., 4.]])
4
5Matrix B:
6tensor([[5., 6.],
7        [7., 8.]])

Using float32 is common in deep learning because floating-point tensors are required by many neural network operations.

Matrix Addition

Matrix addition adds corresponding elements.

pythonCopy
1result = A + B
2
3print(result)

Output:

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1tensor([[ 6.,  8.],
2        [10., 12.]])

Calculation:

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11 + 5 = 6
22 + 6 = 8
33 + 7 = 10
44 + 8 = 12

You can also use:

pythonCopy
1result = torch.add(A, B)

Matrix Subtraction

pythonCopy
1result = A - B
2
3print(result)

Output:

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1tensor([[-4., -4.],
2        [-4., -4.]])

Subtraction is also an element-wise tensor operation.

Element-wise Multiplication

The * operator performs element-wise multiplication.

pythonCopy
1result = A * B
2
3print(result)

Output:

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1tensor([[ 5., 12.],
2        [21., 32.]])

The calculation is:

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11 × 5 = 5
22 × 6 = 12
33 × 7 = 21
44 × 8 = 32

This is different from matrix multiplication.

Matrix Multiplication

For matrix multiplication, use @ or torch.matmul().

pythonCopy
1result = A @ B
2
3print(result)

Output:

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1tensor([[19., 22.],
2        [43., 50.]])

The first element is calculated as:

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1(1 × 5) + (2 × 7)
2
3= 5 + 14
4
5= 19

The complete matrix multiplication is:

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1        [1  2]   [5  6]
2A × B = [3  4] × [7  8]
3
4        [19  22]
5      = [43  50]

The same operation can be written as:

pythonCopy
1result = torch.matmul(A, B)

Matrix Division

The / operator performs element-wise division.

pythonCopy
1result = A / B
2
3print(result)

Output:

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1tensor([[0.2000, 0.3333],
2        [0.4286, 0.5000]])

For example:

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11 / 5 = 0.2
22 / 6 ≈ 0.3333
33 / 7 ≈ 0.4286
44 / 8 = 0.5

Matrix Transpose

Transpose swaps the rows and columns of a 2D tensor.

pythonCopy
1result = A.T
2
3print(result)

Output:

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1tensor([[1., 3.],
2        [2., 4.]])

For a 2D tensor:

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1Original:
2
31  2
43  4
5
6Transpose:
7
81  3
92  4

You can also use:

pythonCopy
1result = torch.transpose(A, 0, 1)

Inspect Tensor Shape

Use .shape to inspect the dimensions of a tensor.

pythonCopy
1print(A.shape)

Output:

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1torch.Size([2, 2])

This means the matrix contains:

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12 rows × 2 columns

Calculate the Sum

pythonCopy
1result = torch.sum(A)
2
3print(result)

Output:

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1tensor(10.)

Calculation:

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11 + 2 + 3 + 4 = 10

Calculate the Mean

pythonCopy
1result = torch.mean(A)
2
3print(result)

Output:

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1tensor(2.5000)

Calculation:

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1(1 + 2 + 3 + 4) / 4 = 2.5

Find the Maximum Value

pythonCopy
1result = torch.max(A)
2
3print(result)

Output:

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1tensor(4.)

Find the Minimum Value

pythonCopy
1result = torch.min(A)
2
3print(result)

Output:

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1tensor(1.)

Complete PyTorch Matrix Calculator

The following program combines all the operations into one example.

pythonCopy
1import torch
2
3A = torch.tensor(
4    [[1, 2],
5     [3, 4]],
6    dtype=torch.float32
7)
8
9B = torch.tensor(
10    [[5, 6],
11     [7, 8]],
12    dtype=torch.float32
13)
14
15print("Matrix A:")
16print(A)
17
18print("\nMatrix B:")
19print(B)
20
21print("\nAddition:")
22print(A + B)
23
24print("\nSubtraction:")
25print(A - B)
26
27print("\nElement-wise Multiplication:")
28print(A * B)
29
30print("\nMatrix Multiplication:")
31print(A @ B)
32
33print("\nElement-wise Division:")
34print(A / B)
35
36print("\nTranspose of A:")
37print(A.T)
38
39print("\nShape of A:")
40print(A.shape)
41
42print("\nSum of A:")
43print(torch.sum(A))
44
45print("\nMean of A:")
46print(torch.mean(A))
47
48print("\nMaximum of A:")
49print(torch.max(A))
50
51print("\nMinimum of A:")
52print(torch.min(A))

This example gives you practical experience with several important PyTorch tensor functions.

Project 2: Tensor Manipulation Toolkit

The second project demonstrates common PyTorch tensor manipulation techniques.

We will work with:

• arange()
• reshape()
• flatten()
• unsqueeze()
• squeeze()
• permute()
• transpose()
• stack()
• cat()
• split()

These operations are frequently used when preparing data for neural networks.

Create a Tensor with torch.arange()

pythonCopy
1import torch
2
3x = torch.arange(24)
4
5print(x)

Output:

textCopy
1tensor([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11,
2        12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23])

The tensor contains 24 elements.

Reshape a Tensor

We can convert the one-dimensional tensor into a 3D tensor.

pythonCopy
1y = x.reshape(2, 3, 4)
2
3print(y)
4print("Shape:", y.shape)

Output shape:

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1Shape: torch.Size([2, 3, 4])

The number of elements must remain unchanged:

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12 × 3 × 4 = 24

Therefore:

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1(24)
2    ↓
3(2, 3, 4)

A reshape cannot change the total number of elements.

Flatten a Tensor

Flatten converts multiple dimensions into a single dimension.

pythonCopy
1flat = torch.flatten(y)
2
3print(flat)
4print("Shape:", flat.shape)

Output shape:

textCopy
1torch.Size([24])

A common neural network pattern is:

pythonCopy
1features = torch.flatten(x, start_dim=1)

This preserves the batch dimension while flattening the remaining dimensions.

Add a Dimension with unsqueeze()

unsqueeze() inserts a dimension of size 1.

pythonCopy
1z = y.unsqueeze(0)
2
3print("Original shape:", y.shape)
4print("New shape:", z.shape)

Output:

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1Original shape: torch.Size([2, 3, 4])
2New shape: torch.Size([1, 2, 3, 4])

This is commonly used to add a batch dimension.

For example:

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1Image:
2
3(3, 224, 224)
4
5        ↓ unsqueeze(0)
6
7(1, 3, 224, 224)

The 1 represents a batch containing one image.

Remove a Dimension with squeeze()

squeeze() removes dimensions whose size is 1.

pythonCopy
1result = z.squeeze()
2
3print(result.shape)

Output:

textCopy
1torch.Size([2, 3, 4])

The dimension added by unsqueeze() has been removed.

Rearrange Dimensions with permute()

permute() changes the order of multiple dimensions.

Suppose:

pythonCopy
1y.shape

is:

textCopy
1(2, 3, 4)

We can rearrange it:

pythonCopy
1result = y.permute(2, 0, 1)
2
3print(result.shape)

Output:

textCopy
1torch.Size([4, 2, 3])

The dimensions are reordered according to:

textCopy
1Original:
2
3(2, 3, 4)
4
5permute(2, 0, 1)
6
7↓
8
9(4, 2, 3)

Transpose Tensor Dimensions

transpose() swaps two dimensions.

pythonCopy
1result = y.transpose(1, 2)
2
3print(result.shape)

Output:

textCopy
1torch.Size([2, 4, 3])

The dimensions at positions 1 and 2 were exchanged.

permute() vs transpose()

Example:

pythonCopy
1x.permute(2, 0, 1)

can reorder all three dimensions.

Whereas:

pythonCopy
1x.transpose(1, 2)

only swaps dimensions 1 and 2.

Stack Tensors

torch.stack() combines tensors by creating a new dimension.

pythonCopy
1a = torch.tensor([1, 2, 3])
2b = torch.tensor([4, 5, 6])
3
4result = torch.stack((a, b))
5
6print(result)
7print("Shape:", result.shape)

Output:

textCopy
1tensor([[1, 2, 3],
2        [4, 5, 6]])
3
4Shape: torch.Size([2, 3])

The original tensors have shape:

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1(3)

After stacking:

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1(2, 3)

The rank increases by one.

Concatenate Tensors

torch.cat() joins tensors along an existing dimension.

pythonCopy
1result = torch.cat((a, b))
2
3print(result)
4print("Shape:", result.shape)

Output:

textCopy
1tensor([1, 2, 3, 4, 5, 6])
2
3Shape: torch.Size([6])

Unlike stack(), concatenation does not create a new dimension.

torch.cat() vs torch.stack()

This distinction is important in PyTorch tensor manipulation.

textCopy
1a = [1, 2, 3]
2b = [4, 5, 6]

Using cat():

textCopy
1[1, 2, 3, 4, 5, 6]
2
3Shape: (6)

Using stack():

textCopy
1[[1, 2, 3],
2 [4, 5, 6]]
3
4Shape: (2, 3)

Therefore:

Split a Tensor

torch.split() divides a tensor into smaller tensors.

pythonCopy
1parts = torch.split(x, 6)
2
3for part in parts:
4    print(part)

Output:

textCopy
1tensor([0, 1, 2, 3, 4, 5])
2tensor([6, 7, 8, 9, 10, 11])
3tensor([12, 13, 14, 15, 16, 17])
4tensor([18, 19, 20, 21, 22, 23])

The original tensor contains 24 elements.

Splitting it into groups of 6 creates:

textCopy
124 / 6 = 4 parts

Split with Different Sizes

You can specify the size of every section.

pythonCopy
1parts = torch.split(x, [4, 8, 12])
2
3for part in parts:
4    print(part)

This creates:

textCopy
1Part 1 → 4 elements
2Part 2 → 8 elements
3Part 3 → 12 elements

Total:

textCopy
14 + 8 + 12 = 24

Chunk a Tensor

torch.chunk() divides a tensor into a specified number of chunks.

pythonCopy
1x = torch.arange(12)
2
3parts = torch.chunk(x, 3)
4
5for part in parts:
6    print(part)

Output:

textCopy
1tensor([0, 1, 2, 3])
2tensor([4, 5, 6, 7])
3tensor([8, 9, 10, 11])

Here, 12 elements are divided into 3 chunks.

split() vs chunk()

Real-World Example: Preparing an Image for a CNN

Computer vision is one of the most important applications of PyTorch tensor operations.

An image may initially be represented as:

textCopy
1Height × Width × Channels
2
3(224, 224, 3)

Many PyTorch computer vision models use:

textCopy
1Channels × Height × Width
2
3(3, 224, 224)

We can rearrange the dimensions using permute().

pythonCopy
1image = torch.randn(224, 224, 3)
2
3print("Original:", image.shape)
4
5image = image.permute(2, 0, 1)
6
7print("CHW:", image.shape)

Output:

textCopy
1Original: torch.Size([224, 224, 3])
2CHW: torch.Size([3, 224, 224])

Add the Batch Dimension

Neural networks generally process batches of samples.

A single image has:

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1(3, 224, 224)

Add a batch dimension:

pythonCopy
1image = image.unsqueeze(0)
2
3print(image.shape)

Output:

textCopy
1torch.Size([1, 3, 224, 224])

Now the tensor represents:

textCopy
1Batch = 1
2Channels = 3
3Height = 224
4Width = 224

Flatten Image Features

Suppose a later stage of the model produces a tensor that needs to be passed into a fully connected layer.

You can flatten everything except the batch dimension:

pythonCopy
1features = torch.flatten(image, start_dim=1)
2
3print(features.shape)

For this example:

textCopy
1(1, 3, 224, 224)

becomes:

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1(1, 150528)

because:

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13 × 224 × 224 = 150528

The batch dimension remains unchanged.

Complete Image Tensor Pipeline

pythonCopy
1import torch
2
3image = torch.randn(224, 224, 3)
4
5print("Original:", image.shape)
6
7# Convert HWC to CHW
8image = image.permute(2, 0, 1)
9
10print("After permute:", image.shape)
11
12# Add batch dimension
13image = image.unsqueeze(0)
14
15print("After unsqueeze:", image.shape)
16
17# Flatten all dimensions except batch
18features = torch.flatten(image, start_dim=1)
19
20print("After flatten:", features.shape)

Output:

textCopy
1Original: torch.Size([224, 224, 3])
2After permute: torch.Size([3, 224, 224])
3After unsqueeze: torch.Size([1, 3, 224, 224])
4After flatten: torch.Size([1, 150528])

This demonstrates how PyTorch tensor shape manipulation is used in real deep learning pipelines.

Project 3: Student Marks Analysis

Tensor operations are not limited to images and neural networks. They can also be used for numerical data analysis.

Consider the following marks:

pythonCopy
1import torch
2
3marks = torch.tensor(
4    [
5        [80, 75, 90],
6        [85, 95, 88],
7        [70, 60, 78]
8    ],
9    dtype=torch.float32
10)
11
12print(marks)

The rows represent students:

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1Student 1
2Student 2
3Student 3

The columns represent subjects:

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1Math
2Science
3English

Calculate Total Marks for Each Student

Use dim=1 to reduce each row.

pythonCopy
1total = torch.sum(marks, dim=1)
2
3print(total)

Output:

textCopy
1tensor([245., 268., 208.])

Calculations:

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1Student 1:
280 + 75 + 90 = 245
3
4Student 2:
585 + 95 + 88 = 268
6
7Student 3:
870 + 60 + 78 = 208

Calculate Average Marks

pythonCopy
1average = torch.mean(marks, dim=1)
2
3print(average)

Output:

textCopy
1tensor([81.6667, 89.3333, 69.3333])

Find the Highest Mark

pythonCopy
1highest = torch.max(marks)
2
3print(highest)

Output:

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1tensor(95.)

Find the Best Student

First calculate the total marks:

pythonCopy
1total = torch.sum(marks, dim=1)

Then find the index of the largest total:

pythonCopy
1best_student = torch.argmax(total)
2
3print(best_student)

Output:

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1tensor(1)

Because PyTorch uses zero-based indexing:

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1Index 0 → Student 1
2Index 1 → Student 2
3Index 2 → Student 3

Therefore, Student 2 has the highest total score.

Find the Best Student's Score

You can also retrieve the highest total directly:

pythonCopy
1best_score = torch.max(total)
2
3print(best_score)

Output:

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1tensor(268.)

This illustrates an important distinction:

pythonCopy
1torch.max(total)

returns the maximum value.

Whereas:

pythonCopy
1torch.argmax(total)

returns the index of the maximum value.

Interview Questions

What is the difference between reshape() and view()?

reshape() returns a tensor with the requested shape and may return a view when possible or create a copy when necessary.

view() returns a view of the same underlying data and generally requires the tensor's memory layout to be compatible.

Example:

pythonCopy
1x = torch.arange(12)
2
3y = x.reshape(3, 4)
4
5print(y.shape)

Output:

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1torch.Size([3, 4])

A practical rule for beginners is to use reshape() when you simply need to change the shape.

What is the difference between torch.cat() and torch.stack()?

torch.cat() joins tensors along an existing dimension.

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

Result:

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1tensor([1, 2, 3, 4])

torch.stack() creates a new dimension.

pythonCopy
1print(torch.stack((a, b)))

Result:

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1tensor([[1, 2],
2        [3, 4]])

What is the difference between permute() and transpose()?

permute() can rearrange multiple dimensions:

pythonCopy
1x.permute(2, 0, 1)

transpose() swaps two dimensions:

pythonCopy
1x.transpose(1, 2)

Why is unsqueeze() useful in PyTorch?

unsqueeze() adds a dimension of size 1.

A common use is adding a batch dimension:

pythonCopy
1image = image.unsqueeze(0)

For example:

textCopy
1(3, 224, 224)
2        ↓
3(1, 3, 224, 224)

What is the difference between A * B and A @ B?

A * B performs element-wise multiplication.

pythonCopy
1A * B

A @ B performs matrix multiplication.

pythonCopy
1A @ B

These operations have different mathematical meanings.

What is broadcasting?

Broadcasting allows PyTorch to perform operations on tensors with compatible shapes without explicitly creating copies of the smaller tensor.

For example:

pythonCopy
1x = torch.tensor([1, 2, 3])
2
3print(x + 10)

Output:

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1tensor([11, 12, 13])

The scalar is logically applied to every element.

Common Tensor Operation Mistakes

Mistake 1: Using * for Matrix Multiplication

Incorrect when matrix multiplication is intended:

pythonCopy
1A * B

Correct:

pythonCopy
1A @ B

or:

pythonCopy
1torch.matmul(A, B)

Mistake 2: Invalid Reshape

This is invalid:

pythonCopy
1x = torch.arange(10)
2
3x.reshape(3, 4)

Why?

The original tensor has 10 elements, while the requested shape requires:

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13 × 4 = 12

The total number of elements must remain the same.

Mistake 3: Forgetting the Batch Dimension

A single image may have:

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1(3, 224, 224)

while a batch of images has:

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1(batch, 3, 224, 224)

For a single image:

pythonCopy
1image = image.unsqueeze(0)

adds the batch dimension.

Mistake 4: Confusing argmax() with max()

max() returns the maximum value:

pythonCopy
1torch.max(x)

argmax() returns the position of the maximum value:

pythonCopy
1torch.argmax(x)

For example:

pythonCopy
1x = torch.tensor([10, 50, 20])
2
3print(torch.max(x))
4print(torch.argmax(x))

Output:

textCopy
1tensor(50)
2tensor(1)

Mistake 5: Ignoring Tensor Shapes

Before performing tensor operations, inspect the shape:

pythonCopy
1print(x.shape)

For more detailed debugging:

pythonCopy
1print("Shape:", x.shape)
2print("Dimensions:", x.ndim)
3print("Dtype:", x.dtype)
4print("Device:", x.device)

Mistake 6: Using view() After Dimension Reordering Without Considering Memory Layout

Operations such as permute() can produce a tensor whose memory layout is non-contiguous.

For example:

pythonCopy
1x = torch.arange(12).reshape(3, 4)
2
3x = x.permute(1, 0)

When a view operation is not compatible with the resulting memory layout, use:

pythonCopy
1x = x.reshape(...)

or:

pythonCopy
1x = x.contiguous().view(...)

PyTorch Tensor Operations Best Practices

• Check tensor shapes frequently with .shape.
• Use .ndim when you need to know the number of dimensions.
• Use reshape() when changing tensor shape.
• Use permute() when changing the order of multiple dimensions.
• Use transpose() when swapping two dimensions.
• Use flatten(start_dim=1) when flattening model features while preserving the batch dimension.
• Use torch.cat() when joining tensors along an existing dimension.
• Use torch.stack() when a new dimension is required.
• Use torch.split() when exact split sizes are useful.
• Use torch.chunk() when you know how many pieces you want.
• Make sure tensors have compatible shapes before performing operations.
• Keep tensors on the same device before combining them.
• Prefer clear shape names and comments when working with complex neural network tensors.

Practice Exercises

Beginner Level

Create two 2 × 2 tensors and perform:

1. Addition
2. Subtraction
3. Element-wise multiplication
4. Element-wise division
5. Matrix multiplication
6. Transpose
7. Sum
8. Mean
9. Maximum
10. Minimum

Print the result of every operation.

Tensor Shape Practice

Create:

pythonCopy
1x = torch.arange(24)

Perform the following:

1. Reshape it to (2, 3, 4).
2. Flatten it.
3. Add a dimension using unsqueeze().
4. Remove the dimension using squeeze().
5. Rearrange its dimensions using permute().
6. Swap two dimensions using transpose().

Print the shape after every operation.

Intermediate Level

Create a batch of ten random RGB images:

pythonCopy
1images = torch.randn(10, 3, 64, 64)

Perform the following:

1. Print the shape.
2. Flatten each image while preserving the batch dimension.
3. Split the batch into two parts.
4. Concatenate the parts again.
5. Verify that the final tensor has the original shape.

Useful operations:

pythonCopy
1torch.flatten()
2torch.split()
3torch.cat()

Advanced Level: Transformer Tensor Shapes

Simulate Transformer input with:

pythonCopy
1batch_size = 4
2sequence_length = 128
3embedding_dimension = 768
4
5x = torch.randn(
6    batch_size,
7    sequence_length,
8    embedding_dimension
9)
10
11print(x.shape)

The resulting shape is:

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1(4, 128, 768)

Now convert it to:

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1(sequence_length, batch_size, embedding_dimension)

using:

pythonCopy
1x = x.permute(1, 0, 2)
2
3print(x.shape)

Expected shape:

textCopy
1torch.Size([128, 4, 768])

Convert it back:

pythonCopy
1x = x.permute(1, 0, 2)
2
3print(x.shape)

Expected:

textCopy
1torch.Size([4, 128, 768])

This exercise helps develop the tensor-shape skills required for Transformer models, large language models, and modern deep learning architectures.

Module 3 Final Summary

You have now completed the practical portion of PyTorch Tensor Operations.

You learned how to combine individual tensor operations into complete programs and practical data-processing pipelines.

Key concepts include:

• PyTorch arithmetic operations
• Element-wise tensor operations
• Broadcasting
• Matrix multiplication
• Dot products
• Reduction operations
• Tensor concatenation
• Tensor stacking
• Tensor splitting
• Tensor chunking
• Tensor reshaping
• Tensor flattening
• Adding and removing dimensions
• Dimension permutation
• Tensor transposition
• Image tensor preprocessing
• Batch dimension handling
• Tensor analysis with reduction functions

The most important skill is not memorizing every PyTorch function. It is learning to reason about tensor shapes, dimensions, data types, devices, and mathematical operations.

For example:

textCopy
1Image
2(224, 224, 3)
3      ↓
4permute()
5(3, 224, 224)
6      ↓
7unsqueeze()
8(1, 3, 224, 224)
9      ↓
10Neural Network
11      ↓
12flatten()
13(1, features)
14      ↓
15Linear Layer
16      ↓
17Prediction

Once you understand this type of tensor flow, many PyTorch errors become easier to diagnose.

Key PyTorch Tensor Functions to Remember

Next Module

In the next module, Tensor Indexing and Slicing, you will learn how to access individual tensor elements, select rows and columns, slice multidimensional tensors, modify tensor values, use boolean masks, and perform advanced indexing operations.

These techniques are essential for PyTorch data preprocessing, dataset manipulation, model debugging, feature extraction, and deep learning workflows.