I have launched a new course that is the first in a series that puts you on a path to deep learning wizardry with PyTorch.
- PyTorch Fundamentals
- Linear Regression
- Logistic Regression
- Feedforward Neural Networks
- Convolutional Neural Networks (CNNs)
- Recurrent Neural Networks (RNNs)
- Long Short-Term Memory Networks (LSTMs)
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.
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.
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.
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.
Udemy link: https://www.udemy.com/practical-deep-learning-with-pytorch