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Development Data Science Deep Learning

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!
Rating: 4.6 out of 54.6 (1,986 ratings)
13,592 students
Created by Jose Portilla
Last updated 9/2019
English
English [Auto], French [Auto]
30-Day Money-Back Guarantee

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
Curated for the Udemy for Business collection

Course content

12 sections • 97 lectures • 17h 0m total length

  • Preview06:41
  • Installation and Environment Setup
    18:21

  • DID YOU WATCH THE COURSE OVERVIEW LECTURE?
    1 question

  • 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

  • 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 Introduction
    03:20
  • Preview08:10
  • 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

  • What is Machine Learning?
    03:40
  • Supervised Learning
    08:21
  • Preview07:59
  • Evaluating Performance - Classification Error Metrics
    16:37
  • Evaluating Performance - Regression Error Metrics
    05:36
  • Unsupervised Learning
    04:44

  • 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
  • Preview06:52
  • 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

  • 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
  • Preview11:35
  • 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

  • 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

  • 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
62 courses
28 reviews
Rating: 5.0 out of 5a year 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!

Instructor

Jose Portilla
Head of Data Science, Pierian Data Inc.
Jose Portilla
  • 4.6 Instructor Rating
  • 718,598 Reviews
  • 2,198,001 Students
  • 32 Courses

  Jose Marcial Portilla has a BS and MS in Mechanical Engineering from Santa Clara University and years of experience as a professional instructor and trainer for Data Science and programming. He has publications and patents in various fields such as microfluidics, materials science, and data science technologies. Over the course of his career he has developed a skill set in analyzing data and he hopes to use his experience in teaching and data science to help other people learn the power of programming the ability to analyze data, as well as present the data in clear and beautiful visualizations. Currently he works as the Head of Data Science for Pierian Data Inc. and provides in-person data science and python programming training courses to employees working at top companies, including General Electric, Cigna, The New York Times, Credit Suisse, McKinsey and many more. Feel free to contact him on LinkedIn for more information on in-person training sessions or group training sessions in Las Vegas, NV.

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