PyTorch for Deep Learning with Python Bootcamp from Udemy

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

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

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! :)

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

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

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Ensures a strong foundation in linear algebra, which is essential for deep learning.

Browse courses on Linear Algebra

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Review basic linear algebra concepts, such as vectors, matrices, and transformations.
Solve linear algebra problems to practice applying these concepts.

Review Python basics

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Refreshes basic Python skills, which are necessary for implementing deep learning algorithms.

Browse courses on Python

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Review basic Python syntax and data structures.
Solve simple Python coding problems to practice applying these concepts.

Read Deep Learning

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Provides a comprehensive foundation in deep learning concepts and algorithms.

View Deep Learning on Amazon

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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

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Organize and review course materials

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Strengthens understanding of course concepts by consolidating and reviewing materials.

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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

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Provides hands-on experience with PyTorch, the deep learning library used in the course.

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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

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Encourages collaboration and discussion, fostering a deeper understanding of deep learning.

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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

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Reinforces understanding of deep learning algorithms through practical application.

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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

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Enhances understanding of deep learning concepts by requiring clear and concise explanation.

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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

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Provides an opportunity to apply deep learning knowledge to solve a real-world problem.

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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:

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PyTorch for Deep Learning with Python Bootcamp

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