Practical Deep Learning with PyTorch (Udemy.com)

Accelerate your deep learning with PyTorch covering all the fundamentals of deep learning with a python-first framework.

( 308 Reviews )

Created by: Deep Learning Wizard

Produced in 2018

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

Quality Score

Content Quality

Video Quality

Qualified Instructor

Course Pace

Course Depth & Coverage

Overall Score : 82 / 100

Course Description

Growing Importance of Deep LearningDeep 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 codewhen 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 FAQThis 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 aimto enhance your understanding and provide a gentle learning curve forfuture courses that would dive much deeper into it.
Latest Python Notebooks Compatible with PyTorch 0.
4 and 1.
0There are very small changes from PyTorch 0.
3 for this deep learning series where you will find it is extremely easy to transit over!
Who this course is for:
Anyone who wants to learn deep learningDeep learning researchers using other frameworks like TensorFlow, Keras, Torch, and CaffeAny python programmer

Time: 6.5h

Instructor Details

Deep Learning Wizard

4.1 Rating
308 Reviews

Currently I am leading artificial intelligence with my colleagues inensemblecap, an AI hedge fund based in Singapore comprising quants and traders from JPMorgan and Nomura. I have built the whole AI tech stack in a production environment with rigorous time-sensitive and fail-safe software testing powering multi-million dollar trades daily.

I am also anNVIDIA Deep Learning Institute instructorleading all deep learning workshops in NUS, Singapore and conducting workshops across Southeast Asia.

My passion for enabling anyone to leverage on deep learning has led to the creation ofDeep Learning Wizardwhere I have taught and still continue to teach more than 2000 students in over 60 countries around the world. The course is recognized bySoumith Chintala, Facebook AI Research, andAlfredo Canziani, Post-Doctoral Associate under Yann Lecun, as the first comprehensive PyTorch Video Tutorial.

In my free time, Im into deep learning research with researchers based inNExT++ (NUS) led by Chua Tat-SengandMILA led by Yoshua Bengio. I was previously conducting research in meta-learning for hyperparameter optimization for deep learning algorithms in NExT Search Centre that is jointly setup between National University of Singapore (NUS), Tsinghua University and University of Southampton led by co-directorsProf Tat-Seng Chua(KITHCT Chair Professor at the School of Computing),Prof Sun Maosong(Dean of Department of Computer Science and Technology, Tsinghua University), andProf

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Reviews

4.1

308 total reviews

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Write a Review

By Isaac Privett on 11/14/2020

Good conceptual course but it doesn't touch on any practical uses for any of the concepts.

By Umino on 10/18/2020

Transformer

By Thiago de Paulo Faleiros on 9/21/2020

Yes, it is great!

By Shivam Rawat on 9/13/2020

Wonderfully explained how to use AWS

By Aakriti Adhikari on 9/2/2020

Yes it was

By Matt Yates on 8/25/2020

Overall, a very nice course. Excellent coding introduction to PyTorch. Math explanations seem hot and cold; meaning some are explanations are questionable. For deep learning math, I highly recommend Andrew Ng on Coursera. For getting up and running with PyTorch coding, this is a fantastic course (I'd just take some of the math explanations with a grain of salt). For example, non-linearities can be incorporated into Logistic Regression by means of polynomial features or categorical binning then 1-hot encoding your bins. I get what the instructor is trying to convey, but his explanation makes it sounds like Logistic Regression is incapable of capturing nonlinearities, which is not the case (MLP is just better at it). I would take this course as a great intro to PyTorch, and for further technical details, I would seak out university courses like Coursera's Stanford ML course. I would give this course a 5 if he assumed the audience knew the math (or was getting that elsewhere) and just spent more time on the PyTorch framework vs. explaining how an MLP works.

By Aneeta Christopher on 8/21/2020

good presentation. would like to be updated to the current model, getting to knowbetter about pytorch, with elaborate explanation

By Roozbeh Bakhshi on 8/10/2020

Explanations are pretty poor. The instructor reads over the code without really explaining why he wrote that specific code. Also, the codes are full of bugs and do not run.

By Vedant Phuse on 8/10/2020

The best course to get a brief insight into both PyTorch and DeepLearning, from the research side.

By Prayson Wilfred Daniel on 8/2/2020

I would love to code together, instead of looking at pre-ran Jupyter cells. As introduction, the explanation and transitions between model architectures is beyond awesomeness!

By Noreen Wu on 7/22/2020

I was really stuck on the configuration of PyTorch. AWS is confusing and I have a Mac so I couldn't do the suggested installation. I ended up just installing with pip3 which is way easier. The instruction that follows the configuration is a lot more interesting and actually does not require that I do everything alongside the video.

By Martin Larios Osorio on 7/21/2020