PyTorch for Deep Learning Computer Vision Bootcamp 2024

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Manifold AI Learning ®

11:40:17

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1 - Welcome Aboard

1 - Why PyTorch is Powerful.mp4

03:51

2 - Introduction

2 - Introduction to Pytorch.mp4

01:24

3 - Getting System Ready.mp4

08:06

4 - Create Tensors in Pytorch.mp4

06:19

5 - Tensor Slicing and Reshape.mp4

03:25

6 - Mathematical Operations on Tensors.mp4

02:15

7 - Numpy in Pytorch.mp4

04:35

8 - What is CUDA.mp4

04:04

9 - Pytorch on GPU.mp4

07:12

10 - Download Materials.html

10 - Section-Intro-To-Pytorch.zip

3 - AutoGrad in Pytorch

11 - Autograd in Pytorch.mp4

12:04

12 - Implementing Gradient Descent using Autograd.mp4

04:51

13 - Download Materials.html

13 - Pytorch-AutoGrad.zip

4 - Creating Deep Neural Networks in Pytorch

14 - Building first neural network.mp4

08:11

15 - Writing Deep neural network.mp4

04:26

16 - Writing Custom NN module.mp4

06:10

17 - Download Materials.html

17 - Writing-Deep-NN-pytorch.zip

5 - CNN on Pytorch

18 - Data Loading CIFAR10.mp4

10:53

19 - Data Visualization.mp4

04:35

20 - CNN Recap.mp4

03:44

21 - First CNN.mp4

07:45

22 - CNN Deep layers.mp4

07:37

23 - CNN-CIFAR10-Part-1.zip

23 - Download Materials.html

6 - LeNet Architecture in Pytorch

24 - LeNet Overview.mp4

03:46

25 - LeNet Model in Pytorch.mp4

11:25

26 - Preparation Evaluation.mp4

08:50

27 - CNN-CIFAR10-Part-2.zip

27 - Download Materials.html

7 - Optional Learning Python Basics

28 - Why Computer Programming Language.mp4

05:45

29 - Why Python.mp4

02:38

30 - Getting System Ready Installing Jupyter Notebook.mp4

07:19

31 - Jupyter Notebook Tips Tricks.mp4

05:56

32 - What is Covered in this section.mp4

01:52

33 - Variables in Python.mp4

08:36

34 - Print Function.mp4

03:27

35 - Numeric Data Type.mp4

05:02

36 - String Data Type.mp4

03:48

37 - Boolean Data Type.mp4

01:55

38 - Type Conversion Type Casting.mp4

06:18

39 - Adding Comments in Python Programming Language.mp4

02:05

40 - Data Structures in Python.mp4

09:11

41 - Tuples Sets in Python.mp4

08:25

42 - Python Dictionaries.mp4

05:22

43 - Conditional Statements in Python if.mp4

11:16

44 - Conditional Statements in Python While.mp4

06:08

45 - Inbuilt Functions in Python range input.mp4

07:43

46 - For Loops.mp4

04:36

47 - Functions in Python.mp4

09:16

48 - Classes in Python.mp4

12:17

8 - Mini Project with Python Basics

49 - DLAI-S05-Mini-Project-Hangman.zip

49 - Section Attachment.html

50 - Mini Project Hangman.mp4

06:18

51 - Writing a class.mp4

07:54

52 - Mini Project Continued.mp4

05:22

53 - Logic Building.mp4

06:18

54 - Logic for Single Letter input.mp4

10:06

55 - Final Testing.mp4

06:48

9 - Python for Data Science Numpy

56 - Numpy Library Code.html

56 - dlai-s07a02-to-a06-numpy.zip

57 - Why Numpy.html

58 - Numpy.mp4

16:21

59 - Resize Reshape of Arrays.mp4

08:42

60 - Slicing.mp4

06:04

61 - Broadcasting.mp4

12:35

62 - Mathematical Operations Functions in Numpy.mp4

07:13

10 - Python for Data Science Pandas

63 - Pandas-Attachment.zip

63 - Section Attachments.html

64 - Pandas Library.mp4

16:17

65 - Pandas Dataframe.mp4

05:28

66 - Pandas Dataframe Load from External file.mp4

08:28

67 - Working with null values.mp4

06:20

68 - Slicing Pandas Dataframe.mp4

05:42

69 - Imputation.mp4

03:44

11 - Python for DataScience Matplotlib

70 - Section Attachments.html

70 - dlai-s09-matplotlib.zip

71 - Matplotlib Introduction.mp4

09:54

72 - Format the plot.mp4

08:12

73 - Plot Formatting Scatter Plot.mp4

03:27

74 - Histplot.mp4

06:57

12 - Additional Concepts from Machine Learning

75 - Bonus How do you select a Model in ML.mp4

23:16

76 - Bonus Get More from Learning Journey.html

13 - Machine Learning for Projects

77 - Understanding Generative AI.mp4

01:17:44

78 - Machine learning Deployment Part 1 Model Prep End to End.mp4

15:31

79 - Machine learning Deployment Part 2 Deploy Flask App End to End.mp4

11:48

80 - Streamlit Tutorial.mp4

19:22

81 - Bonus Content References.html

82 - Packaging the ML Models.mp4

56:26

83 - Docker Containers for Data Science and ML Projects.mp4

59:15

84 - Course Trailer on MLOps.mp4

04:22

Description

Master Computer Vision in PyTorch/Python: Beginner to Pro with Expert Tips on Convolutional Neural Networks (CNNs)

What You'll Learn?

Master how to Perform Computer Vision Task with Deep Learning
Learn to Work with PyTorch
Convolutional Neural Networks with Torch Library
Build Intuition on Convolution Operation on Images
Learn to Implement LeNet Architecture on CIFAR10 dataset which has 60000 images

Who is this for?

Software Developer

Machine Learning Practitioner

Data Scientist

Anyone interested to learn PyTorch

Anyone interested in Deep learning

What You Need to Know?

Basic Machine learning with Python Programming Language

More details

Description
Dive into Computer Vision with PyTorch: Master Deep Learning, CNNs, and GPU Computing for Real-World Applications - 2024 Edition"
Unlock the potential of Deep Learning in Computer Vision, where groundbreaking advancements shape the future of technology. Explore applications ranging from Facebook's image tagging and Google Photo's People Recognition to fraud detection and facial recognition. Delve into the core operations of Deep Learning Computer Vision, including convolution operations on images, as you master the art of extracting valuable information from digital images.
In this comprehensive course, we focus on one of the most widely used Deep Learning frameworks â€“ PyTorch. Recognized as the go-to tool for Deep Learning in both product prototypes and academia, PyTorch stands out for its Pythonic nature, ease of learning, higher developer productivity, dynamic approach for graph computation through AutoGrad, and GPU support for efficient computation.
Why PyTorch?
Pythonic: PyTorch aligns seamlessly with the Python programming language, offering a natural and intuitive experience for learners.
Easy to Learn: The simplicity of PyTorch makes it accessible for beginners, allowing a smooth learning curve.
Higher Developer Productivity: PyTorch's design prioritizes developer productivity, promoting efficiency in building and experimenting with models.
Dynamic Approach for Graph Computation - AutoGrad: PyTorch's dynamic computational graph through AutoGrad enables flexible and efficient model development.
GPU Support: PyTorch provides GPU support for accelerated computation, enhancing performance in handling large datasets and complex models.
Course Highlights:
Gain a foundational understanding of PyTorch, essential for delving into the world of Deep Learning.
Learn GPU programming and explore how to access free GPU resources for efficient learning.
Master the AutoGrad feature of PyTorch, a key aspect for dynamic graph computation.
Implement Deep Learning models using PyTorch, transitioning from theory to practical application.
Explore the basics of Convolutional Neural Networks (CNNs) in PyTorch, a fundamental architecture for computer vision tasks.
Apply CNNs to real-world datasets, developing hands-on experience with practical applications.
Our Approach:
We believe that true learning extends beyond theoretical understanding; it involves building confidence through practical application. Throughout the course, we've incorporated assignments at the end of each section, enabling you to measure your progress and reinforce your learning. We aspire to empower you with the skills and confidence needed to navigate the dynamic field of Deep Learning in Computer Vision.
Embark on this journey with Manifold AI Learning, where innovation meets education. We look forward to welcoming you inside the course and witnessing your success. Best of luck!
Manifold AI Learning
Who this course is for:
Software Developer
Machine Learning Practitioner
Data Scientist
Anyone interested to learn PyTorch
Anyone interested in Deep learning

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Manifold AI Learning ® is an online Academy with the goal to empower the students with the knowledge and skills that can be directly applied to solving the Real world problems in Data Science, Machine Learning and Artificial intelligence.Checkout our instructor profile for the complete list of courses.All the best for your Learning.- Team ManifoldAILearning ®"Learn the Future"

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