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Data Science
Deep Learning
PyTorch for Deep Learning with Python Bootcamp
4.6 (1,292 ratings)
Course Ratings are calculated from individual students’ ratings and a variety of other signals, like age of rating and reliability, to ensure that they reflect course quality fairly and accurately.
9,428 students enrolled

PyTorch for Deep Learning with Python Bootcamp

Learn how to create state of the art neural networks for deep learning with Facebook's PyTorch Deep Learning library!
4.6 (1,292 ratings)
Course Ratings are calculated from individual students’ ratings and a variety of other signals, like age of rating and reliability, to ensure that they reflect course quality fairly and accurately.
9,428 students enrolled
Created by Jose Portilla
Last updated 9/2019
English
English [Auto], French [Auto]
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PyTorch for Deep Learning with Python Bootcamp
Current price: $139.99 Original price: $199.99 Discount: 30% off
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This course includes
  • 17 hours on-demand video
  • 2 articles
  • 2 downloadable resources
  • Full lifetime access
  • Access on mobile and TV
  • Certificate of Completion
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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
Course content
Expand all 97 lectures 17:00:56
+ – COURSE OVERVIEW CONFIRMATION CHECK
0 lectures 00:00

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

DID YOU WATCH THE COURSE OVERVIEW LECTURE?
1 question
+ – Crash Course: NumPy
7 lectures 46:23
Introduction to NumPy
00:44
NumPy Arrays
10:45
NumPy Arrays Part Two
08:10
Numpy Index Selection
11:35
NumPy Operations
06:46
Numpy Exercises
01:18
Numpy Exercises - Solutions
07:05
+ – Crash Course: Pandas
9 lectures 01:13:19
Pandas Overview
01:10
Pandas Series
10:01
Pandas DataFrames - Part One
13:24
Pandas DataFrames - Part Two
11:09
GroupBy Operations
05:43
Pandas Operations
09:21
Data Input and Output
10:18
Pandas Exercises
03:38
Pandas Exercises - Solutions
08:35
+ – PyTorch Basics
7 lectures 54:32
PyTorch Basics Introduction
03:20
Tensor Basics
Preview 08:10
Tensor Basics - Part Two
15:12
Tensor Operations
13:29
Tensor Operations - Part Two
06:27
PyTorch Basics - Exercise
02:33
PyTorch Basics - Exercise Solutions
05:21
+ – Machine Learning Concepts Overview
6 lectures 46:57
What is Machine Learning?
03:40
Supervised Learning
08:21
Overfitting
Preview 07:59
Evaluating Performance - Classification Error Metrics
16:37
Evaluating Performance - Regression Error Metrics
05:36
Unsupervised Learning
04:44
+ – ANN - Artificial Neural Networks
22 lectures 04:44:09
Introduction to ANN Section
01:45
Theory - Perceptron Model
10:39
Theory - Neural Network
07:19
Theory - Activation Functions
10:39
Multi-Class Classification
10:34
Theory - Cost Functions and Gradient Descent
18:13
Theory - BackPropagation
14:47
PyTorch Gradients
12:23
Linear Regression with PyTorch
11:01
Linear Regression with PyTorch - Part Two
20:31
DataSets with PyTorch
15:59
Basic Pytorch ANN - Part One
11:34
Basic PyTorch ANN - Part Two
15:35
Basic PyTorch ANN - Part Three
14:23
Introduction to Full ANN with PyTorch
Preview 06:52
Full ANN Code Along - Regression - Part One - Feature Engineering
19:35
Full ANN Code Along - Regression - Part 2 - Categorical and Continuous Features
19:42
Full ANN Code Along - Regression - Part Three - Tabular Model
17:09
Full ANN Code Along - Regression - Part Four - Training and Evaluation
16:42
Full ANN Code Along - Classification Example
06:52
ANN - Exercise Overview
05:30
ANN - Exercise Solutions
16:25
+ – CNN - Convolutional Neural Networks
22 lectures 04:21:05
Introduction to CNNs
01:56
Understanding the MNIST data set
03:25
ANN with MNIST - Part One - Data
19:22
ANN with MNIST - Part Two - Creating the Network
10:34
ANN with MNIST - Part Three - Training
15:28
ANN with MNIST - Part Four - Evaluation
09:15
Image Filters and Kernels
Preview 11:35
Convolutional Layers
14:01
Pooling Layers
06:47
MNIST Data Revisited
02:11
MNIST with CNN - Code Along - Part One
18:21
MNIST with CNN - Code Along - Part Two
18:18
MNIST with CNN - Code Along - Part Three
08:57
CIFAR-10 DataSet with CNN - Code Along - Part One
07:13
CIFAR-10 DataSet with CNN - Code Along - Part Two
18:40
Loading Real Image Data - Part One
16:12
Loading Real Image Data - Part Two
18:26
CNN on Custom Images - Part One - Loading Data
22:20
CNN on Custom Images - Part Two - Training and Evaluating Model
13:09
CNN on Custom Images - Part Three - PreTrained Networks
14:14
CNN Exercise
02:49
CNN Exercise Solutions
07:52
+ – Recurrent Neural Networks
12 lectures 02:10:20
Introduction to Recurrent Neural Networks
02:00
RNN Basic Theory
07:41
Vanishing Gradients
06:47
LSTMS and GRU
11:23
RNN Batches Theory
07:49
RNN - Creating Batches with Data
12:11
Basic RNN - Creating the LSTM Model
12:56
Basic RNN - Training and Forecasting
20:28
RNN on a Time Series - Part One
14:35
RNN on a Time Series - Part Two
18:45
RNN Exercise
04:14
RNN Exercise - Solutions
11:31
+ – Using a GPU with PyTorch and CUDA
2 lectures 30:47
Why do we need GPUs?
13:07
Using GPU for PyTorch
17:40
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)
Description

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

Who this course is for:
  • Intermediate to Advanced Python Developers wanting to learn about Deep Learning with PyTorch
Featured review
Philip Lieberman
Philip Lieberman ( 56 courses, 27 reviews )
11 months ago
Outstanding course that provides a ton of missing details not covered in other courses. If you have ever taken a course and seen the instructor take a gigantic leap and left out the gory details of why certain mechanics were used, this instructor is kind enough to walk you though most everything. This course is not yet complete at the time of the review, but the material so far is worth its weight in gold. Thank you!