CourseFlix

PyTorch for Deep Learning with Python Bootcamp

17h 2m 14s
English
Paid

Welcome to the ultimate online course for mastering Deep Learning with Python and PyTorch! PyTorch is an open-source deep learning platform that seamlessly transitions from research prototyping to production deployment. As one of the most popular deep learning frameworks for Python, it allows for the integration of popular libraries, facilitating the creation of neural network layers. With a rich ecosystem, PyTorch supports development in fields such as computer vision, natural language processing, and more.

Course Overview

This course strikes a balance between theoretical concepts and practical, hands-on exercises. We provide projects that equip you to apply the learned concepts to your own datasets. Upon enrolling, you'll gain access to meticulously crafted notebooks that simplify concepts with both code and explanatory notes presented side-by-side. You'll also access slides that clarify theory through comprehensible visualizations.

Course Content

Throughout this course, you'll learn essential skills for starting with Deep Learning using PyTorch, including:

  • NumPy
  • Pandas
  • Machine Learning Theory
  • Test/Train/Validation Data Splits
  • Model Evaluation - Regression and Classification Tasks
  • Unsupervised Learning Tasks
  • Tensors with PyTorch
  • Neural Network Theory
    • Perceptrons
    • Networks
    • Activation Functions
    • Cost/Loss Functions
    • Backpropagation
    • Gradients
  • Artificial Neural Networks
  • Convolutional Neural Networks
  • Recurrent Neural Networks
  • and much more!

By the end of this course, you'll be capable of creating a wide range of deep learning models to resolve your unique challenges using your datasets.

Requirements

  • Understanding of Python basic topics (data types, loops, functions) with Python OOP recommended.
  • Ability to perform basic derivative calculations.
  • Admin permissions on your computer (necessary for downloading files).

Target Audience

  • Intermediate to advanced Python developers aiming to specialize in Deep Learning with PyTorch.

Learning Outcomes

By completing this course, you will:

  • Learn to use NumPy to format data into arrays.
  • Utilize pandas for data manipulation and cleaning.
  • Understand classic machine learning theory principles.
  • Apply the PyTorch Deep Learning Library for image classification.
  • Employ PyTorch with Recurrent Neural Networks for sequence and time series data.
  • Create state-of-the-art Deep Learning models to handle tabular data.

About the Author: Udemy

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Udemy is the largest open marketplace for online courses on the internet. Founded in 2010 by Eren Bali, Oktay Caglar, and Gagan Biyani and headquartered in San Francisco, the company went public on the Nasdaq in 2021 under the ticker UDMY. The platform hosts well over two hundred thousand courses across software development, IT and cloud, data science, design, business, marketing, and creative skills, taught by tens of thousands of independent instructors. Roughly seventy million learners use it worldwide, and the corporate arm — Udemy Business — supplies a curated subset of that catalog to enterprise customers.

Because Udemy is a marketplace rather than a single editorial publisher, the catalog is uneven by design. The strongest material lives in the long-form, project-based courses authored by working engineers — full-stack JavaScript, React, Node.js, Python data science, AWS, Docker and Kubernetes, mobile development with Flutter and React Native, and cloud certification preparation. The CourseFlix listing under this source is the slice of that catalog that has been mirrored here for offline-friendly viewing, organized by topic and updated as new releases land. Pricing on Udemy itself swings dramatically with the site's near-permanent sales, which is why the platform is best treated as a deep reference catalog: pick instructors with strong reviews and a track record of updating their material rather than buying on the headline price alone.

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#1: COURSE OVERVIEW LECTURE - PLEASE DO NOT SKIP!
All Course Lessons (95)
#Lesson TitleDurationAccess
1
COURSE OVERVIEW LECTURE - PLEASE DO NOT SKIP! Demo
06:42
2
Installation and Environment Setup
18:22
3
Introduction to NumPy
00:45
4
NumPy Arrays
10:46
5
NumPy Arrays Part Two
08:11
6
Numpy Index Selection
11:36
7
NumPy Operations
06:47
8
Numpy Exercises
01:19
9
Numpy Exercises - Solutions
07:06
10
Pandas Overview
01:11
11
Pandas Series
10:02
12
Pandas DataFrames - Part One
13:25
13
Pandas DataFrames - Part Two
11:10
14
GroupBy Operations
05:44
15
Pandas Operations
09:22
16
Data Input and Output
10:19
17
Pandas Exercises
03:39
18
Pandas Exercises - Solutions
08:36
19
PyTorch Basics Introduction
03:21
20
Tensor Basics
08:11
21
Tensor Basics - Part Two
15:13
22
Tensor Operations
13:30
23
Tensor Operations - Part Two
06:28
24
PyTorch Basics - Exercise
02:34
25
PyTorch Basics - Exercise Solutions
05:22
26
What is Machine Learning?
03:41
27
Supervised Learning
08:22
28
Overfitting
08:00
29
Evaluating Performance - Classification Error Metrics
16:38
30
Evaluating Performance - Regression Error Metrics
05:37
31
Unsupervised Learning
04:45
32
Introduction to ANN Section
01:46
33
Theory - Perceptron Model
10:40
34
Theory - Neural Network
07:20
35
Theory - Activation Functions
10:40
36
Multi-Class Classification
10:35
37
Theory - Cost Functions and Gradient Descent
18:14
38
Theory - BackPropagation
14:48
39
PyTorch Gradients
12:24
40
Linear Regression with PyTorch
11:02
41
Linear Regression with PyTorch - Part Two
20:32
42
DataSets with PyTorch
16:00
43
Basic Pytorch ANN - Part One
11:35
44
Basic PyTorch ANN - Part Two
15:36
45
Basic PyTorch ANN - Part Three
14:24
46
Introduction to Full ANN with PyTorch
06:53
47
Full ANN Code Along - Regression - Part One - Feature Engineering
19:36
48
Full ANN Code Along - Regression - Part 2 - Categorical and Continuous Features
19:43
49
Full ANN Code Along - Regression - Part Three - Tabular Model
17:10
50
Full ANN Code Along - Regression - Part Four - Training and Evaluation
16:43
51
Full ANN Code Along - Classification Example
06:53
52
ANN - Exercise Overview
05:31
53
ANN - Exercise Solutions
16:26
54
Introduction to CNNs
01:57
55
Understanding the MNIST data set
03:26
56
ANN with MNIST - Part One - Data
19:23
57
ANN with MNIST - Part Two - Creating the Network
10:35
58
ANN with MNIST - Part Three - Training
15:29
59
ANN with MNIST - Part Four - Evaluation
09:16
60
Image Filters and Kernels
11:36
61
Convolutional Layers
14:02
62
Pooling Layers
06:48
63
MNIST Data Revisited
02:12
64
MNIST with CNN - Code Along - Part One
18:22
65
MNIST with CNN - Code Along - Part Two
18:19
66
MNIST with CNN - Code Along - Part Three
08:58
67
CIFAR-10 DataSet with CNN - Code Along - Part One
07:14
68
CIFAR-10 DataSet with CNN - Code Along - Part Two
18:41
69
Loading Real Image Data - Part One
16:13
70
Loading Real Image Data - Part Two
18:27
71
CNN on Custom Images - Part One - Loading Data
22:21
72
CNN on Custom Images - Part Two - Training and Evaluating Model
13:10
73
CNN on Custom Images - Part Three - PreTrained Networks
14:15
74
CNN Exercise
02:50
75
CNN Exercise Solutions
07:53
76
Introduction to Recurrent Neural Networks
02:01
77
RNN Basic Theory
07:42
78
Vanishing Gradients
06:48
79
LSTMS and GRU
11:24
80
RNN Batches Theory
07:50
81
RNN - Creating Batches with Data
12:12
82
Basic RNN - Creating the LSTM Model
12:57
83
Basic RNN - Training and Forecasting
20:29
84
RNN on a Time Series - Part One
14:36
85
RNN on a Time Series - Part Two
18:46
86
RNN Exercise
04:15
87
RNN Exercise - Solutions
11:32
88
Why do we need GPUs?
13:08
89
Using GPU for PyTorch
17:41
90
Introduction to NLP with PyTorch
02:38
91
Encoding Text Data
15:50
92
Generating Training Batches
14:41
93
Creating the LSTM Model
12:35
94
Training the LSTM Model
11:55
95
Generating Predictions
10:32
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Course content

95 lessons · 17h 2m 14s
Show all 95 lessons
  1. 1 COURSE OVERVIEW LECTURE - PLEASE DO NOT SKIP! 06:42
  2. 2 Installation and Environment Setup 18:22
  3. 3 Introduction to NumPy 00:45
  4. 4 NumPy Arrays 10:46
  5. 5 NumPy Arrays Part Two 08:11
  6. 6 Numpy Index Selection 11:36
  7. 7 NumPy Operations 06:47
  8. 8 Numpy Exercises 01:19
  9. 9 Numpy Exercises - Solutions 07:06
  10. 10 Pandas Overview 01:11
  11. 11 Pandas Series 10:02
  12. 12 Pandas DataFrames - Part One 13:25
  13. 13 Pandas DataFrames - Part Two 11:10
  14. 14 GroupBy Operations 05:44
  15. 15 Pandas Operations 09:22
  16. 16 Data Input and Output 10:19
  17. 17 Pandas Exercises 03:39
  18. 18 Pandas Exercises - Solutions 08:36
  19. 19 PyTorch Basics Introduction 03:21
  20. 20 Tensor Basics 08:11
  21. 21 Tensor Basics - Part Two 15:13
  22. 22 Tensor Operations 13:30
  23. 23 Tensor Operations - Part Two 06:28
  24. 24 PyTorch Basics - Exercise 02:34
  25. 25 PyTorch Basics - Exercise Solutions 05:22
  26. 26 What is Machine Learning? 03:41
  27. 27 Supervised Learning 08:22
  28. 28 Overfitting 08:00
  29. 29 Evaluating Performance - Classification Error Metrics 16:38
  30. 30 Evaluating Performance - Regression Error Metrics 05:37
  31. 31 Unsupervised Learning 04:45
  32. 32 Introduction to ANN Section 01:46
  33. 33 Theory - Perceptron Model 10:40
  34. 34 Theory - Neural Network 07:20
  35. 35 Theory - Activation Functions 10:40
  36. 36 Multi-Class Classification 10:35
  37. 37 Theory - Cost Functions and Gradient Descent 18:14
  38. 38 Theory - BackPropagation 14:48
  39. 39 PyTorch Gradients 12:24
  40. 40 Linear Regression with PyTorch 11:02
  41. 41 Linear Regression with PyTorch - Part Two 20:32
  42. 42 DataSets with PyTorch 16:00
  43. 43 Basic Pytorch ANN - Part One 11:35
  44. 44 Basic PyTorch ANN - Part Two 15:36
  45. 45 Basic PyTorch ANN - Part Three 14:24
  46. 46 Introduction to Full ANN with PyTorch 06:53
  47. 47 Full ANN Code Along - Regression - Part One - Feature Engineering 19:36
  48. 48 Full ANN Code Along - Regression - Part 2 - Categorical and Continuous Features 19:43
  49. 49 Full ANN Code Along - Regression - Part Three - Tabular Model 17:10
  50. 50 Full ANN Code Along - Regression - Part Four - Training and Evaluation 16:43
  51. 51 Full ANN Code Along - Classification Example 06:53
  52. 52 ANN - Exercise Overview 05:31
  53. 53 ANN - Exercise Solutions 16:26
  54. 54 Introduction to CNNs 01:57
  55. 55 Understanding the MNIST data set 03:26
  56. 56 ANN with MNIST - Part One - Data 19:23
  57. 57 ANN with MNIST - Part Two - Creating the Network 10:35
  58. 58 ANN with MNIST - Part Three - Training 15:29
  59. 59 ANN with MNIST - Part Four - Evaluation 09:16
  60. 60 Image Filters and Kernels 11:36
  61. 61 Convolutional Layers 14:02
  62. 62 Pooling Layers 06:48
  63. 63 MNIST Data Revisited 02:12
  64. 64 MNIST with CNN - Code Along - Part One 18:22
  65. 65 MNIST with CNN - Code Along - Part Two 18:19
  66. 66 MNIST with CNN - Code Along - Part Three 08:58
  67. 67 CIFAR-10 DataSet with CNN - Code Along - Part One 07:14
  68. 68 CIFAR-10 DataSet with CNN - Code Along - Part Two 18:41
  69. 69 Loading Real Image Data - Part One 16:13
  70. 70 Loading Real Image Data - Part Two 18:27
  71. 71 CNN on Custom Images - Part One - Loading Data 22:21
  72. 72 CNN on Custom Images - Part Two - Training and Evaluating Model 13:10
  73. 73 CNN on Custom Images - Part Three - PreTrained Networks 14:15
  74. 74 CNN Exercise 02:50
  75. 75 CNN Exercise Solutions 07:53
  76. 76 Introduction to Recurrent Neural Networks 02:01
  77. 77 RNN Basic Theory 07:42
  78. 78 Vanishing Gradients 06:48
  79. 79 LSTMS and GRU 11:24
  80. 80 RNN Batches Theory 07:50
  81. 81 RNN - Creating Batches with Data 12:12
  82. 82 Basic RNN - Creating the LSTM Model 12:57
  83. 83 Basic RNN - Training and Forecasting 20:29
  84. 84 RNN on a Time Series - Part One 14:36
  85. 85 RNN on a Time Series - Part Two 18:46
  86. 86 RNN Exercise 04:15
  87. 87 RNN Exercise - Solutions 11:32
  88. 88 Why do we need GPUs? 13:08
  89. 89 Using GPU for PyTorch 17:41
  90. 90 Introduction to NLP with PyTorch 02:38
  91. 91 Encoding Text Data 15:50
  92. 92 Generating Training Batches 14:41
  93. 93 Creating the LSTM Model 12:35
  94. 94 Training the LSTM Model 11:55
  95. 95 Generating Predictions 10:32

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Frequently asked questions

What is PyTorch for Deep Learning with Python Bootcamp about?
Welcome to the ultimate online course for mastering Deep Learning with Python and PyTorch! PyTorch is an open-source deep learning platform that seamlessly transitions from research prototyping to production deployment. As one of the most…
Who teaches PyTorch for Deep Learning with Python Bootcamp?
PyTorch for Deep Learning with Python Bootcamp is taught by Udemy. You can find more courses by this instructor on the corresponding source page.
How long is PyTorch for Deep Learning with Python Bootcamp?
PyTorch for Deep Learning with Python Bootcamp contains 95 lessons with a total runtime of 17 hours 2 minutes. All lessons are available to watch online at your own pace.
Is PyTorch for Deep Learning with Python Bootcamp free to watch?
PyTorch for Deep Learning with Python Bootcamp is part of CourseFlix's premium catalog. A CourseFlix subscription unlocks the full video player; the course description, table of contents, and preview information are available to everyone.
Where can I watch PyTorch for Deep Learning with Python Bootcamp online?
PyTorch for Deep Learning with Python Bootcamp is available to watch online on CourseFlix at https://courseflix.net/course/pytorch-for-deep-learning-with-python-bootcamp. The page hosts every lesson with the integrated video player; no download is required.