Practical Deep Learning with PyTorch

Practical Deep Learning with PyTorch, available at $54.99, has an average rating of 4.1, with 58 lectures, based on 1700 reviews, and has 6711 subscribers.

You will learn about Effectively wield PyTorch, a Python-first framework, to build your deep learning projects Master deep learning concepts and implement them in PyTorch This course is ideal for individuals who are Anyone who wants to learn deep learning or Deep learning researchers using other frameworks like TensorFlow, Keras, Torch, and Caffe or Any python programmer It is particularly useful for Anyone who wants to learn deep learning or Deep learning researchers using other frameworks like TensorFlow, Keras, Torch, and Caffe or Any python programmer.

Enroll now: Practical Deep Learning with PyTorch

Summary

Title: Practical Deep Learning with PyTorch

Price: $54.99

Average Rating: 4.1

Number of Lectures: 58

Number of Published Lectures: 58

Number of Curriculum Items: 58

Number of Published Curriculum Objects: 58

Original Price: $89.99

Quality Status: approved

Status: Live

What You Will Learn

Effectively wield PyTorch, a Python-first framework, to build your deep learning projects

Master deep learning concepts and implement them in PyTorch

Who Should Attend

Anyone who wants to learn deep learning

Deep learning researchers using other frameworks like TensorFlow, Keras, Torch, and Caffe

Any python programmer

Target Audiences

Anyone who wants to learn deep learning

Deep learning researchers using other frameworks like TensorFlow, Keras, Torch, and Caffe

Any python programmer

Growing Importance of Deep Learning

Deep learning underpins a lot of important and increasingly important applications today ranging from facial recognition, to self-driving cars, to medical diagnostics and more.

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Made for Anyone

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Although many courses are very mathematical or too practical in nature, this course strikes a careful balance between the two to provide a solid foundation in deep learning for you to explore further if you are interested in research in the field of deep learning and/or applied deep learning. It is purposefully made for anyone without a strong background in mathematics. And for those with a strong background, it would accelerate your learning in understanding the different models in deep learning.

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Code As You Learn

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This entire course is delivered in a Python Notebook such that you can follow along the videos and replicate the results. You can practice and tweak the models until you truly understand every line of code as we go along.?I highly recommend you to type every line of code?when you are listening to the videos as this will help a lot in getting used to the syntax.

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Gradual Learning Style

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The thing about many guides out there is that they lack the transition from the very basics and people often get lost or miss out vital links that are critical in understanding certain models. Because of this, you can see how every single topic is closely linked with one another. In fact, at the beginning of every topic from logistic regression, I take the time to carefully explain how one model is simply a modification from the previous. That is the marvel of deep learning, we can trace back some part of it to linear regression where we will start.

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Diagram-Driven Code

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This course uses more than 100 custom-made diagrams where I took hundreds of hours to carefully create such that you can clearly see the transition from one model to another and understand the models comprehensively. Also, the diagrams are created so you can clearly see the link between the theory that I would teach and the code you would learn.

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Mentor Availability

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When I first started learning, I wished I had a mentor to guide me through the basics till the advanced theories where you can publish research papers and/or implement very complicated projects. And this course provides you with free access to ask any question, no matter how basic. I will be there and try my very best to answer your question. Even if the material is covered here, I will take the effort to point you to where you can learn here and more resources beyond this course.

Math Prerequisite FAQ

This is not a course that emphasizes heavily on the mathematics behind deep learning. It focuses on getting you to understand how everything works first which is very important for you to easily catch up on the mathematics later on.?There are mathematics involved but they are limited with the sole aim?to enhance your understanding and provide a gentle learning curve for?future courses that would dive much deeper into it.?

Latest Python Notebooks Compatible with PyTorch 0.4 and 1.0

There are very small changes from PyTorch 0.3 for this deep learning series where you will find it is extremely easy to transit over!?

Course Curriculum

Chapter 1: Introduction

Lecture 1: Introduction

Chapter 2: Software Requirements

Lecture 1: CPU Software Requirements

Lecture 2: CPU Installation of PyTorch

Lecture 3: PyTorch with GPU on AWS

Lecture 4: PyTorch with GPU on Linux

Lecture 5: PyTorch with GPU on MacOSX

Chapter 3: PyTorch Fundamentals: Matrices

Lecture 1: Matrix Basics

Lecture 2: Seed for Reproducibility

Lecture 3: Torch to NumPy Bridge

Lecture 4: NumPy to Torch Bridge

Lecture 5: GPU and CPU Toggling

Lecture 6: Basic Mathematical Tensor Operations

Lecture 7: Summary of Matrices

Chapter 4: PyTorch Fundamentals: Variables and Gradients

Lecture 1: Variables

Lecture 2: Gradients

Lecture 3: Summary of Variables and Gradients

Chapter 5: Linear Regression with PyTorch

Lecture 1: Linear Regression Introduction

Lecture 2: Linear Regression in PyTorch

Lecture 3: Linear Regression From CPU to GPU in PyTorch

Lecture 4: Summary of Linear Regression

Chapter 6: Logistic Regression with PyTorch

Lecture 1: Logistic Regression Introduction

Lecture 2: Linear Regression Problems

Lecture 3: Logistic Regression In-depth

Lecture 4: Logistic Regression with PyTorch

Lecture 5: Logistic Regression From CPU to GPU in PyTorch

Lecture 6: Summary of Logistic Regression

Chapter 7: Feedforward Neural Network with PyTorch

Lecture 1: Logistic Regression Transition to Feedforward Neural Network

Lecture 2: Non-linearity

Lecture 3: Feedforward Neural Network in PyTorch

Lecture 4: More Feedforward Neural Network Models in PyTorch

Lecture 5: Feedforward Neural Network From CPU to GPU in PyTorch

Lecture 6: Summary of Feedforward Neural Network

Chapter 8: Convolutional Neural Network (CNN) with PyTorch

Lecture 1: Feedforward Neural Network Transition to CNN

Lecture 2: One Convolutional Layer, Input Depth of 1

Lecture 3: One Convolutional Layer, Input Depth of 3

Lecture 4: One Convolutional Layer Summary

Lecture 5: Multiple Convolutional Layers Overview

Lecture 6: Pooling Layers

Lecture 7: Padding for Convolutional Layers

Lecture 8: Output Size Calculation

Lecture 9: CNN in PyTorch

Lecture 10: More CNN Models in PyTorch

Lecture 11: CNN Models Summary

Lecture 12: Expanding Models Capacity

Lecture 13: CNN From CPU to GPU in PyTorch

Lecture 14: Summary of CNN

Chapter 9: Recurrent Neural Networks (RNN)

Lecture 1: Introduction to RNN

Lecture 2: RNN in PyTorch

Lecture 3: More RNN Models in PyTorch

Lecture 4: RNN From CPU to GPU in PyTorch

Lecture 5: Summary of RNN

Chapter 10: Long Short-Term Memory Networks (LSTM)

Lecture 1: Introduction to LSTMs

Lecture 2: LSTM Equations

Lecture 3: LSTM in PyTorch

Lecture 4: More LSTM Models in PyTorch

Lecture 5: LSTM From CPU to GPU in PyTorch

Lecture 6: Summary of LSTM

Chapter 11: Whats Next?

Lecture 1: Whats Next?

Instructors

Deep Learning Wizard
Deep Learning Researcher, NUS

Rating Distribution

1 stars: 45 votes

2 stars: 81 votes

3 stars: 233 votes

4 stars: 648 votes

5 stars: 693 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.

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