Deep Learning Wizard
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.
Instructor Details
Courses : 1
Specification: Practical Deep Learning with PyTorch
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12 reviews for Practical Deep Learning with PyTorch
Add a review
| Price | $14.99 |
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| Provider | |
| Duration | 6.5 hours |
| Year | 2018 |
| Level | Beginner |
| Language | English |
| Certificate | Yes |
| Quizzes | No |
$14.99
Naya Baslan –
Really good course that helped me understand the fundamentals of deep learning. Thank you for the effort in preparing this course and for the clear guidance throughout all the topics.
Levon –
mice. but rnn and lstm could be better
Nazira Kaibalina –
This course gave a solid foundation of Deep Learning with Pytorch. Special thanks to the intstructor for your hard work!
Nazira Kaibalina –
This course gave a solid foundation of Deep Learning with Pytorch. Special thanks to the intstructor for your hard work!
James Brits –
I got a better understanding of the mathematics. The models are complicated but gave me a better understanding
James Brits –
I got a better understanding of the mathematics. The models are complicated but gave me a better understanding
Charan Tej Thota –
Faster RCNN could’ve been great.
Liberi Paulo –
Great visuals and explanations of DL concepts. Recommended if you want to learn the foundations of each DL algorithm.
Martin Larios Osorio –
No tiene sonido
Rachit Jain –
Can give updates with the newer pytorch version.
Eric Laster –
Lots of installation involved… Would love to use a playground environment at the moment to get a quick start with PyTorch.
Enrico Michelotti –
The course is well presented with Jupiter Notebook code and presentation, and the teacher tries to make the core concept to be clear as much as it is possible. Nevertheless it lacks possibility to actively participate: no exercise, no homework, no possibility to develop our code during lesson, only a quick going through his code. It lacks interaction. The teacher doesn’t even run his code and when I run on my pc I got even very much different results in term of accuracy. Overall it is an ok course