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PyTorch for Deep Learning with Python Bootcamp

Jose Portilla and Pierian Training

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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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

  • 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.

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

Enroll now

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What's inside

Learning objectives

  • 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

Syllabus

Let's get an overview of the course and get you setup!
COURSE OVERVIEW LECTURE - PLEASE DO NOT SKIP!
Installation and Environment Setup
Sorry to bother, but this is just to make sure students don't skip the course overview lecture! :)
Read more

Please watch the course overview lecture if you have not done so already!

Let's get you up to speed on numpy we will use in the course!
Introduction to NumPy
NumPy Arrays
NumPy Arrays Part Two
Numpy Index Selection
NumPy Operations
Numpy Exercises
Numpy Exercises - Solutions
Let's learn the basics of Pandas we'll use in this course!
Pandas Overview
Pandas Series
Pandas DataFrames - Part One
Pandas DataFrames - Part Two
GroupBy Operations
Pandas Operations
Data Input and Output
Pandas Exercises
Pandas Exercises - Solutions
It's time to learn the basics of using PyTorch!
PyTorch Basics Introduction
Tensor Basics
Tensor Basics - Part Two
Tensor Operations
Tensor Operations - Part Two
PyTorch Basics - Exercise
PyTorch Basics - Exercise Solutions
Let's get some theory overview of machine learning concepts, no code in this section!
What is Machine Learning?
Supervised Learning
Overfitting
Evaluating Performance - Classification Error Metrics
Evaluating Performance - Regression Error Metrics
Unsupervised Learning
Let's see how to develop full ANNs with PyTorch!
Introduction to ANN Section
Theory - Perceptron Model
Theory - Neural Network
Theory - Activation Functions
Multi-Class Classification
Theory - Cost Functions and Gradient Descent
Theory - BackPropagation
PyTorch Gradients
Linear Regression with PyTorch
Linear Regression with PyTorch - Part Two
DataSets with PyTorch
Basic Pytorch ANN - Part One
Basic PyTorch ANN - Part Two
Basic PyTorch ANN - Part Three
Introduction to Full ANN with PyTorch
Full ANN Code Along - Regression - Part One - Feature Engineering
Full ANN Code Along - Regression - Part 2 - Categorical and Continuous Features
Full ANN Code Along - Regression - Part Three - Tabular Model
Full ANN Code Along - Regression - Part Four - Training and Evaluation
Full ANN Code Along - Classification Example
ANN - Exercise Overview
ANN - Exercise Solutions
Let's explore using Convolutional Neural Networks for Images and Deep Learning!
Introduction to CNNs
Understanding the MNIST data set
ANN with MNIST - Part One - Data
ANN with MNIST - Part Two - Creating the Network
ANN with MNIST - Part Three - Training
ANN with MNIST - Part Four - Evaluation
Image Filters and Kernels
Convolutional Layers
Pooling Layers
MNIST Data Revisited
MNIST with CNN - Code Along - Part One
MNIST with CNN - Code Along - Part Two
MNIST with CNN - Code Along - Part Three
CIFAR-10 DataSet with CNN - Code Along - Part One
CIFAR-10 DataSet with CNN - Code Along - Part Two
Loading Real Image Data - Part One
Loading Real Image Data - Part Two
CNN on Custom Images - Part One - Loading Data
CNN on Custom Images - Part Two - Training and Evaluating Model
CNN on Custom Images - Part Three - PreTrained Networks
CNN Exercise
CNN Exercise Solutions
Let's learn how to deal with sequence data with Recurrent Neural Networks!
Introduction to Recurrent Neural Networks
RNN Basic Theory
Vanishing Gradients
LSTMS and GRU
RNN Batches Theory
RNN - Creating Batches with Data
Basic RNN - Creating the LSTM Model
Basic RNN - Training and Forecasting
RNN on a Time Series - Part One
RNN on a Time Series - Part Two
RNN Exercise
RNN Exercise - Solutions
Let's learn how to utilize a GPU with PyTorch!
Why do we need GPUs?
Using GPU for PyTorch

Good to know

Know what's good
, what to watch for
, and possible dealbreakers
Covers the fundamentals of deep learning and necessary software, including NumPy, Pandas, and PyTorch
Taught by Jose Portilla and Pierian Training, who are recognized for their expertise in deep learning
Develops a solid foundation in deep learning theory and practice, suitable for beginners to intermediate learners
Provides hands-on exercises and projects to reinforce learning and practical application
Utilizes a mix of theory explanations and practical implementations to enhance understanding
Covers a comprehensive range of deep learning concepts, including neural networks, CNNs, and RNNs

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Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in PyTorch for Deep Learning with Python Bootcamp with these activities:
Review linear algebra concepts
Ensures a strong foundation in linear algebra, which is essential for deep learning.
Browse courses on Linear Algebra
Show steps
  • Review basic linear algebra concepts, such as vectors, matrices, and transformations.
  • Solve linear algebra problems to practice applying these concepts.
Review Python basics
Refreshes basic Python skills, which are necessary for implementing deep learning algorithms.
Browse courses on Python
Show steps
  • Review basic Python syntax and data structures.
  • Solve simple Python coding problems to practice applying these concepts.
Read Deep Learning
Provides a comprehensive foundation in deep learning concepts and algorithms.
View Deep Learning on Amazon
Show steps
  • Read chapters 1-3 to gain an overview of deep learning.
  • Complete the exercises in chapters 1-3 to practice implementing deep learning algorithms.
  • Read chapters 4-6 to learn about advanced deep learning architectures.
  • Complete the exercises in chapters 4-6 to practice implementing advanced deep learning architectures.
  • Read chapters 7-9 to learn about applications of deep learning.
Six other activities
Expand to see all activities and additional details
Show all nine activities
Organize and review course materials
Strengthens understanding of course concepts by consolidating and reviewing materials.
Show steps
  • Gather all course materials, including notes, assignments, and quizzes.
  • Organize the materials into a logical structure.
  • Review the materials regularly to reinforce learning.
Follow PyTorch tutorials
Provides hands-on experience with PyTorch, the deep learning library used in the course.
Show steps
  • Identify a PyTorch tutorial that is relevant to the course material.
  • Follow the tutorial step-by-step and implement the code.
  • Test and refine your implementation to ensure it works correctly.
Join a study group or online forum for deep learning
Encourages collaboration and discussion, fostering a deeper understanding of deep learning.
Show steps
  • Identify a study group or online forum that is relevant to the course material.
  • Join the group or forum.
  • Participate in discussions and ask questions.
  • Collaborate with other members on deep learning projects or assignments.
  • Review and provide feedback on other members' work.
Solve coding problems on LeetCode
Reinforces understanding of deep learning algorithms through practical application.
Show steps
  • Identify a problem on LeetCode that is related to deep learning.
  • Develop a solution to the problem using deep learning techniques.
  • Test and refine your solution to ensure accuracy.
Create a blog post or article about deep learning
Enhances understanding of deep learning concepts by requiring clear and concise explanation.
Show steps
  • Identify a specific aspect of deep learning to focus on.
  • Research and gather information about the topic.
  • Develop an outline for your blog post or article.
  • Write the content of your blog post or article.
  • Edit and proofread your work.
Develop a deep learning project
Provides an opportunity to apply deep learning knowledge to solve a real-world problem.
Show steps
  • Identify a problem that you want to solve using deep learning.
  • Gather and prepare data for your project.
  • Design and implement a deep learning model.
  • Train and evaluate your model.
  • Deploy your model and monitor its performance.

Career center

Learners who complete PyTorch for Deep Learning with Python Bootcamp will develop knowledge and skills that may be useful to these careers:

Reading list

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Effort
17 total hours
Via
Udemy
Instructors
Jose Portilla
Pierian Training
Language
English
Good to know
Covers the fundamentals of deep learning and necessary software, including NumPy, Pandas, and PyTorch
Taught by Jose Portilla and Pierian Training, who are recognized for their expertise in deep learning
Develops a solid foundation in deep learning theory and practice, suitable for beginners to intermediate learners
Provides hands-on exercises and projects to reinforce learning and practical application
Utilizes a mix of theory explanations and practical implementations to enhance understanding
Covers a comprehensive range of deep learning concepts, including neural networks, CNNs, and RNNs
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