PyTorch for Deep Learning with Python Bootcamp

Focused View

Jose Portilla

17:00:49

15 View

1 - Course Overview Installs and Setup

1 - COURSE OVERVIEW LECTURE PLEASE DO NOT SKIP.mp4

06:41

1 - PYTORCH-NOTEBOOKS.zip

2 - Installation and Environment Setup.mp4

18:21

2 - Link for yml environment file.txt

2 - PYTORCH-NOTEBOOKS.zip

2 - COURSE OVERVIEW CONFIRMATION CHECK

1 - DID YOU WATCH THE COURSE OVERVIEW LECTURE.html

3 - Crash Course NumPy

3 - Introduction to NumPy.mp4

00:44

4 - NumPy Arrays.mp4

10:45

5 - NumPy Arrays Part Two.mp4

08:10

6 - Numpy Index Selection.mp4

11:35

7 - NumPy Operations.mp4

06:46

8 - Numpy Exercises.mp4

01:18

9 - Numpy Exercises Solutions.mp4

07:05

4 - Crash Course Pandas

10 - Pandas Overview.mp4

01:11

11 - Pandas Series.mp4

10:01

12 - Pandas DataFrames Part One.mp4

13:24

13 - Pandas DataFrames Part Two.mp4

11:09

14 - GroupBy Operations.mp4

05:44

15 - Pandas Operations.mp4

09:21

16 - Data Input and Output.mp4

10:19

17 - Pandas Exercises.mp4

03:38

18 - Pandas Exercises Solutions.mp4

08:35

5 - PyTorch Basics

19 - PyTorch Basics Introduction.mp4

03:21

20 - Tensor Basics.mp4

08:10

21 - Tensor Basics Part Two.mp4

15:12

22 - Tensor Operations.mp4

13:30

23 - Tensor Operations Part Two.mp4

06:27

24 - PyTorch Basics Exercise.mp4

02:33

25 - PyTorch Basics Exercise Solutions.mp4

05:21

6 - Machine Learning Concepts Overview

26 - What is Machine Learning.mp4

03:40

27 - Supervised Learning.mp4

08:21

28 - Overfitting.mp4

07:59

29 - Evaluating Performance Classification Error Metrics.mp4

16:37

30 - Evaluating Performance Regression Error Metrics.mp4

05:36

31 - Unsupervised Learning.mp4

04:44

7 - ANN Artificial Neural Networks

32 - Introduction to ANN Section.mp4

01:45

33 - Theory Perceptron Model.mp4

10:39

34 - Theory Neural Network.mp4

07:19

35 - Theory Activation Functions.mp4

10:39

36 - MultiClass Classification.mp4

10:34

37 - Theory Cost Functions and Gradient Descent.mp4

18:13

38 - BackPropagation Explained 1.txt

38 - Backpropagation Great Theory Book.txt

38 - Theory BackPropagation.mp4

14:47

39 - PyTorch Gradients.mp4

12:23

40 - Linear Regression with PyTorch.mp4

11:02

41 - Linear Regression with PyTorch Part Two.mp4

20:31

42 - DataSets with PyTorch.mp4

15:59

43 - Basic Pytorch ANN Part One.mp4

11:34

44 - Basic PyTorch ANN Part Two.mp4

15:35

45 - Basic PyTorch ANN Part Three.mp4

14:23

46 - Introduction to Full ANN with PyTorch.mp4

06:52

47 - Full ANN Code Along Regression Part One Feature Engineering.mp4

19:35

48 - Full ANN Code Along Regression Part 2 Categorical and Continuous Features.mp4

19:43

49 - Full ANN Code Along Regression Part Three Tabular Model.mp4

17:09

50 - Full ANN Code Along Regression Part Four Training and Evaluation.mp4

16:42

51 - Full ANN Code Along Classification Example.mp4

06:52

52 - ANN Exercise Overview.mp4

05:30

53 - ANN Exercise Solutions.mp4

16:25

8 - CNN Convolutional Neural Networks

54 - Introduction to CNNs.mp4

01:56

55 - Understanding the MNIST data set.mp4

03:25

56 - ANN with MNIST Part One Data.mp4

19:22

57 - ANN with MNIST Part Two Creating the Network.mp4

10:34

58 - ANN with MNIST Part Three Training.mp4

15:28

59 - ANN with MNIST Part Four Evaluation.mp4

09:15

60 - Image Filters and Kernels.mp4

11:35

61 - Convolutional Layers.mp4

14:01

62 - Pooling Layers.mp4

06:47

63 - MNIST Data Revisited.mp4

02:11

64 - MNIST with CNN Code Along Part One.mp4

18:21

65 - MNIST with CNN Code Along Part Two.mp4

18:18

66 - MNIST with CNN Code Along Part Three.mp4

08:57

67 - CIFAR10 DataSet with CNN Code Along Part One.mp4

07:13

68 - CIFAR10 DataSet with CNN Code Along Part Two.mp4

18:40

69 - Google Drive Download Link for CATSDOGS zip file.txt

69 - Loading Real Image Data Part One.mp4

16:12

70 - Loading Real Image Data Part Two.mp4

18:26

71 - CNN on Custom Images Part One Loading Data.mp4

22:20

72 - CNN on Custom Images Part Two Training and Evaluating Model.mp4

13:10

73 - CNN on Custom Images Part Three PreTrained Networks.mp4

14:14

74 - CNN Exercise.mp4

02:49

75 - CNN Exercise Solutions.mp4

07:52

9 - Recurrent Neural Networks

76 - Introduction to Recurrent Neural Networks.mp4

02:00

77 - RNN Basic Theory.mp4

07:41

78 - Vanishing Gradients.mp4

06:47

79 - LSTMS and GRU.mp4

11:23

80 - RNN Batches Theory.mp4

07:49

81 - RNN Creating Batches with Data.mp4

12:11

82 - Basic RNN Creating the LSTM Model.mp4

12:56

83 - Basic RNN Training and Forecasting.mp4

20:28

84 - RNN on a Time Series Part One.mp4

14:35

85 - RNN on a Time Series Part Two.mp4

18:46

86 - RNN Exercise.mp4

04:15

87 - RNN Exercise Solutions.mp4

11:31

10 - Using a GPU with PyTorch and CUDA

88 - CUDA with PyTorch.txt

88 - NVIDIA CUDA Installation Page.txt

88 - Official Docs on Pytorch on Google Colab.txt

88 - PyTorch Official Install Page.txt

88 - PyTorch on AWS.txt

88 - PyTorch on Google Cloud Platform.txt

88 - PyTorch on Microsoft Azure.txt

88 - Why do we need GPUs.mp4

13:07

89 - Using GPU for PyTorch.mp4

17:40

11 - NLP with PyTorch

90 - Introduction to NLP with PyTorch.mp4

02:37

91 - Encoding Text Data.mp4

15:49

92 - Generating Training Batches.mp4

14:40

93 - Creating the LSTM Model.mp4

12:34

94 - Training the LSTM Model.mp4

11:54

95 - Google Drive Link for another Model.txt

95 - OUR MODEL FOR DOWNLOAD.html

96 - Generating Predictions.mp4

10:31

12 - BONUS SECTION THANK YOU

97 - BONUS LECTURE.html

Description

Learn how to create state of the art neural networks for deep learning with Facebook's PyTorch Deep Learning library!

What You'll Learn?

Learn how to use NumPy to format data into arrays
Use pandas for data manipulation and cleaning
Learn classic machine learning theory principals
Use PyTorch Deep Learning Library for image classification
Use PyTorch with Recurrent Neural Networks for Sequence Time Series Data
Create state of the art Deep Learning models to work with tabular data

Who is this for?

Intermediate to Advanced Python Developers wanting to learn about Deep Learning with PyTorch

What You Need to Know?

Understanding of Python Basic Topics (data types,loops,functions) also Python OOP recommended

Be able to work through basic derivative calculations

Admin Permissions on your computer (ability to download our files)

More details

Description
Welcome to the best online course for learning about Deep Learning with Python and PyTorch!
PyTorch is an open source deep learning platform that provides a seamless path from research prototyping to production deployment. It is rapidly becoming one of the most popular deep learning frameworks for Python. Deep integration into Python allows popular libraries and packages to be used for easily writing neural network layers in Python. A rich ecosystem of tools and libraries extends PyTorch and supports development in computer vision, NLP and more.
This course focuses on balancing important theory concepts with practical hands-on exercises and projects that let you learn how to apply the concepts in the course to your own data sets! When you enroll in this course you will get access to carefully laid out notebooks that explain concepts in an easy to understand manner, including both code and explanations side by side. You will also get access to our slides that explain theory through easy to understand visualizations.
In this course we will teach you everything you need to know to get started with Deep Learning with 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
ArtificialÂ Neural Networks
Convolutional Neural Networks
Recurrent Neural Networks
and much more!
By the end of this course you will be able to create a wide variety of deep learning models to solve your own problems with your own data sets.
So what are you waiting for? Enroll today and experience the true capabilities of Deep Learning with PyTorch! I'll see you inside the course!
-Jose
Who this course is for:
Intermediate to Advanced Python Developers wanting to learn about Deep Learning with PyTorch

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Jose Marcial Portilla has a BS and MS in Mechanical Engineering from Santa Clara University and years of experience as a professional instructor and trainer for Data Science, Machine Learning and Python Programming. He has publications and patents in various fields such as microfluidics, materials science, and data science. Over the course of his career he has developed a skill set in analyzing data and he hopes to use his experience in teaching and data science to help other people learn the power of programming, the ability to analyze data, and the skills needed to present the data in clear and beautiful visualizations. Currently he works as the Head of Data Science for Pierian Training and provides in-person data science and python programming training courses to employees working at top companies, including General Electric, Cigna, The New York Times, Credit Suisse, McKinsey and many more. Feel free to check out the website link to find out more information about training offerings.

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