CF

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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Frequently asked questions

What prerequisites are needed before enrolling in this course?
Before enrolling in the PyTorch for Deep Learning with Python Bootcamp, it is recommended to have a basic understanding of Python programming. Familiarity with libraries such as NumPy and Pandas is beneficial, as these are covered at the beginning of the course. A foundational knowledge of machine learning concepts will also enhance understanding, although the course does cover essential machine learning theory.
What types of projects will I work on during the course?
The course includes hands-on projects that focus on applying deep learning concepts using PyTorch. Students will work on projects involving regression and classification tasks, as well as an introduction to convolutional neural networks (CNNs) using the MNIST dataset. These projects are designed to solidify understanding of neural network theory, model evaluation, and practical application of PyTorch in various tasks.
Who is the target audience for this course?
This course is ideal for individuals looking to deepen their understanding of deep learning and neural networks using PyTorch. It caters to those who have foundational programming skills in Python and are interested in transitioning from research prototyping to production deployment in fields such as computer vision and natural language processing.
How does the course content compare with similar deep learning courses?
This course offers a balanced approach between theoretical concepts and practical exercises. Unlike some courses that focus solely on theory or application, it provides a comprehensive learning experience with 95 lessons, including detailed notebooks and visual aids. The course covers both foundational topics such as NumPy and Pandas, and advanced neural network theories, enabling a thorough understanding of deep learning with PyTorch.
What specific tools and platforms will I learn to use?
Students will learn to use PyTorch, a leading deep learning framework, along with Python libraries such as NumPy and Pandas. The course will guide you through tensor operations, neural network modeling, and data handling in PyTorch. These tools are essential for creating and evaluating deep learning models across various applications.
What topics are not covered in this course?
The course does not cover advanced topics like reinforcement learning, generative adversarial networks (GANs), or natural language processing frameworks beyond the introductory level. While it provides a comprehensive foundation in deep learning with PyTorch, learners interested in these specialized areas may need to seek additional resources.
What is the expected time commitment for this course?
The course consists of 95 lessons, with a mix of theoretical and practical content. The time commitment will vary depending on individual learning pace, but students should expect to spend several hours per week over the span of a few weeks to fully engage with the material, complete exercises, and gain proficiency in the covered topics.