PyTorch for Deep Learning with Python Bootcamp

PyTorch for Deep Learning with Python Bootcamp, available at $124.99, has an average rating of 4.6, with 97 lectures, 1 quizzes, based on 4899 reviews, and has 33417 subscribers.

You will learn about 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 This course is ideal for individuals who are Intermediate to Advanced Python Developers wanting to learn about Deep Learning with PyTorch It is particularly useful for Intermediate to Advanced Python Developers wanting to learn about Deep Learning with PyTorch.

Enroll now: PyTorch for Deep Learning with Python Bootcamp

Summary

Title: PyTorch for Deep Learning with Python Bootcamp

Price: $124.99

Average Rating: 4.6

Number of Lectures: 97

Number of Quizzes: 1

Number of Published Lectures: 97

Number of Published Quizzes: 1

Number of Curriculum Items: 98

Number of Published Curriculum Objects: 98

Original Price: $199.99

Quality Status: approved

Status: Live

What You Will 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 Should Attend

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

Target Audiences

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

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

Course Curriculum

Chapter 1: Course Overview, Installs, and Setup

Lecture 1: COURSE OVERVIEW LECTURE – PLEASE DO NOT SKIP!

Lecture 2: Installation and Environment Setup

Chapter 2: COURSE OVERVIEW CONFIRMATION CHECK

Chapter 3: Crash Course: NumPy

Lecture 1: Introduction to NumPy

Lecture 2: NumPy Arrays

Lecture 3: NumPy Arrays Part Two

Lecture 4: Numpy Index Selection

Lecture 5: NumPy Operations

Lecture 6: Numpy Exercises

Lecture 7: Numpy Exercises – Solutions

Chapter 4: Crash Course: Pandas

Lecture 1: Pandas Overview

Lecture 2: Pandas Series

Lecture 3: Pandas DataFrames – Part One

Lecture 4: Pandas DataFrames – Part Two

Lecture 5: GroupBy Operations

Lecture 6: Pandas Operations

Lecture 7: Data Input and Output

Lecture 8: Pandas Exercises

Lecture 9: Pandas Exercises – Solutions

Chapter 5: PyTorch Basics

Lecture 1: PyTorch Basics Introduction

Lecture 2: Tensor Basics

Lecture 3: Tensor Basics – Part Two

Lecture 4: Tensor Operations

Lecture 5: Tensor Operations – Part Two

Lecture 6: PyTorch Basics – Exercise

Lecture 7: PyTorch Basics – Exercise Solutions

Chapter 6: Machine Learning Concepts Overview

Lecture 1: What is Machine Learning?

Lecture 2: Supervised Learning

Lecture 3: Overfitting

Lecture 4: Evaluating Performance – Classification Error Metrics

Lecture 5: Evaluating Performance – Regression Error Metrics

Lecture 6: Unsupervised Learning

Chapter 7: ANN – Artificial Neural Networks

Lecture 1: Introduction to ANN Section

Lecture 2: Theory – Perceptron Model

Lecture 3: Theory – Neural Network

Lecture 4: Theory – Activation Functions

Lecture 5: Multi-Class Classification

Lecture 6: Theory – Cost Functions and Gradient Descent

Lecture 7: Theory – BackPropagation

Lecture 8: PyTorch Gradients

Lecture 9: Linear Regression with PyTorch

Lecture 10: Linear Regression with PyTorch – Part Two

Lecture 11: DataSets with PyTorch

Lecture 12: Basic Pytorch ANN – Part One

Lecture 13: Basic PyTorch ANN – Part Two

Lecture 14: Basic PyTorch ANN – Part Three

Lecture 15: Introduction to Full ANN with PyTorch

Lecture 16: Full ANN Code Along – Regression – Part One – Feature Engineering

Lecture 17: Full ANN Code Along – Regression – Part 2 – Categorical and Continuous Features

Lecture 18: Full ANN Code Along – Regression – Part Three – Tabular Model

Lecture 19: Full ANN Code Along – Regression – Part Four – Training and Evaluation

Lecture 20: Full ANN Code Along – Classification Example

Lecture 21: ANN – Exercise Overview

Lecture 22: ANN – Exercise Solutions

Chapter 8: CNN – Convolutional Neural Networks

Lecture 1: Introduction to CNNs

Lecture 2: Understanding the MNIST data set

Lecture 3: ANN with MNIST – Part One – Data

Lecture 4: ANN with MNIST – Part Two – Creating the Network

Lecture 5: ANN with MNIST – Part Three – Training

Lecture 6: ANN with MNIST – Part Four – Evaluation

Lecture 7: Image Filters and Kernels

Lecture 8: Convolutional Layers

Lecture 9: Pooling Layers

Lecture 10: MNIST Data Revisited

Lecture 11: MNIST with CNN – Code Along – Part One

Lecture 12: MNIST with CNN – Code Along – Part Two

Lecture 13: MNIST with CNN – Code Along – Part Three

Lecture 14: CIFAR-10 DataSet with CNN – Code Along – Part One

Lecture 15: CIFAR-10 DataSet with CNN – Code Along – Part Two

Lecture 16: Loading Real Image Data – Part One

Lecture 17: Loading Real Image Data – Part Two

Lecture 18: CNN on Custom Images – Part One – Loading Data

Lecture 19: CNN on Custom Images – Part Two – Training and Evaluating Model

Lecture 20: CNN on Custom Images – Part Three – PreTrained Networks

Lecture 21: CNN Exercise

Lecture 22: CNN Exercise Solutions

Chapter 9: Recurrent Neural Networks

Lecture 1: Introduction to Recurrent Neural Networks

Lecture 2: RNN Basic Theory

Lecture 3: Vanishing Gradients

Lecture 4: LSTMS and GRU

Lecture 5: RNN Batches Theory

Lecture 6: RNN – Creating Batches with Data

Lecture 7: Basic RNN – Creating the LSTM Model

Lecture 8: Basic RNN – Training and Forecasting

Lecture 9: RNN on a Time Series – Part One

Lecture 10: RNN on a Time Series – Part Two

Lecture 11: RNN Exercise

Lecture 12: RNN Exercise – Solutions

Chapter 10: Using a GPU with PyTorch and CUDA

Lecture 1: Why do we need GPUs?

Lecture 2: Using GPU for PyTorch

Instructors

Jose Portilla
Head of Data Science at Pierian Training

Pierian Training
Data Science and Machine Learning Training

Rating Distribution

1 stars: 39 votes

2 stars: 50 votes

3 stars: 282 votes

4 stars: 1495 votes

5 stars: 3035 votes

Frequently Asked Questions

How long do I have access to the course materials?

You can view and review the lecture materials indefinitely, like an on-demand channel.

Can I take my courses with me wherever I go?

Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don’t have an internet connection, some instructors also let their students download course lectures. That’s up to the instructor though, so make sure you get on their good side!