PyTorch for Deep Learning with Python Bootcamp

17h 2m 14s
English
Paid
September 24, 2024

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.

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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

  • Artificial 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.

Requirements:
  • 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)
Who this course is for:
  • Intermediate to Advanced Python Developers wanting to learn about Deep Learning with PyTorch

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

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# Title Duration
1 COURSE OVERVIEW LECTURE - PLEASE DO NOT SKIP! 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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