Practical Deep Learning with PyTorch
4.2 (90 ratings)
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Practical Deep Learning with PyTorch

Accelerate your deep learning with PyTorch covering all the fundamentals of deep learning with a python-first framework.
4.2 (90 ratings)
Instead of using a simple lifetime average, Udemy calculates a course's star rating by considering a number of different factors such as the number of ratings, the age of ratings, and the likelihood of fraudulent ratings.
560 students enrolled
Last updated 7/2017
English
Current price: $10 Original price: $100 Discount: 90% off
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Includes:
  • 6.5 hours on-demand video
  • 2 Supplemental Resources
  • Full lifetime access
  • Access on mobile and TV
  • Certificate of Completion
What Will I Learn?
  • Effectively wield PyTorch, a Python-first framework, to build your deep learning projects
  • Master deep learning concepts and implement them in PyTorch
View Curriculum
Requirements
  • You need to know basic python such as lists, dictionaries, loops, functions and classes
  • You need to know basic differentiation
  • You need to know basic algebra
Description

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.

 

Made for Anyone

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.

 

Code As You Learn

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.

 

Gradual Learning Style

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.

 

Diagram-Driven Code

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.

 

Mentor Availability

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. 

Who is the target audience?
  • Anyone who wants to learn deep learning
  • Deep learning researchers using other frameworks like TensorFlow, Keras, Torch, and Caffe
  • Any python programmer
Compare to Other Deep Learning Courses
Curriculum For This Course
58 Lectures
06:26:56
+
Introduction
1 Lecture 04:59
+
PyTorch Fundamentals: Matrices
7 Lectures 24:47

Seed for Reproducibility
03:30

Torch to NumPy Bridge
04:59

NumPy to Torch Bridge
00:53

GPU and CPU Toggling
01:43

Basic Mathematical Tensor Operations
09:04

Summary of Matrices
01:30
+
PyTorch Fundamentals: Variables and Gradients
3 Lectures 10:55
Variables
03:17

Gradients
06:51

Summary of Variables and Gradients
00:47
+
Linear Regression with PyTorch
4 Lectures 37:53
Linear Regression Introduction
04:06

Linear Regression in PyTorch
22:56

There is the source code attached that is capable of running on the GPU or CPU.

Linear Regression From CPU to GPU in PyTorch
07:44

Summary of Linear Regression
03:07
+
Logistic Regression with PyTorch
6 Lectures 01:13:15
Logistic Regression Introduction
03:28

Linear Regression Problems
03:57

Logistic Regression In-depth
11:30

Logistic Regression with PyTorch
44:11

There is the source code attached that is capable of running on the GPU or CPU.

Logistic Regression From CPU to GPU in PyTorch
06:56

Summary of Logistic Regression
03:13
+
Feedforward Neural Network with PyTorch
6 Lectures 55:54
Logistic Regression Transition to Feedforward Neural Network
03:39

Non-linearity
07:09

Feedforward Neural Network in PyTorch
23:51

More Feedforward Neural Network Models in PyTorch
11:30

There is the source code attached that is capable of running on the GPU or CPU.

Feedforward Neural Network From CPU to GPU in PyTorch
04:39

Summary of Feedforward Neural Network
05:06
+
Convolutional Neural Network (CNN) with PyTorch
14 Lectures 01:17:17
Feedforward Neural Network Transition to CNN
02:46

One Convolutional Layer, Input Depth of 1
10:47

One Convolutional Layer, Input Depth of 3
06:23

One Convolutional Layer Summary
02:12

Multiple Convolutional Layers Overview
02:16

Pooling Layers
05:31

Padding for Convolutional Layers
07:50

Output Size Calculation
04:25

CNN in PyTorch
18:30

More CNN Models in PyTorch
08:19

CNN Models Summary
01:25

Expanding Model's Capacity
01:31

There is the source code attached that is capable of running on the GPU or CPU.

CNN From CPU to GPU in PyTorch
02:27

Summary of CNN
02:55
+
Recurrent Neural Networks (RNN)
5 Lectures 37:23
Introduction to RNN
06:53

RNN in PyTorch
13:09

More RNN Models in PyTorch
11:39

There is the source code attached that is capable of running on the GPU or CPU.

RNN From CPU to GPU in PyTorch
03:28

Summary of RNN
02:14
+
Long Short-Term Memory Networks (LSTM)
6 Lectures 36:14
Introduction to LSTMs
08:14

LSTM Equations
03:43

LSTM in PyTorch
11:56

More LSTM Models in PyTorch
07:54

There is the source code attached that is capable of running on the GPU or CPU.

LSTM From CPU to GPU in PyTorch
02:40

Summary of LSTM
01:47
1 More Section
About the Instructor
Deep Learning Wizard
4.2 Average rating
88 Reviews
560 Students
1 Course
Deep Learning Researcher, NUS

I am a deep learning researcher from the National University of Singapore (NUS). I work with researchers across the world such as IVADO, MILA, Cornell, MIT, Tsinghua and more. I have also published in top workshops and conferences such as ICML. And I have taught deep learning across the world alongside reputable speakers from top companies like NVIDIA.