This course is an accumulation of well-grounded knowledge and experience in deep learning. It provides you with the basic concepts you need in order to start working with and training various machine learning models.
You will cover both basic and intermediate concepts including but not limited to: convolutional neural networks, recurrent neural networks, generative adversarial networks as well as transformers.
After completing this course, you will have a comprehensive understanding of the fundamental architectural components of deep learning. Whether you’re a data and computer scientist, computer and big data engineer, solution architect, or software engineer, you will benefit from this course.
Understand the basic principles behind Neural Networks, Gradient Descent and Pytorch.
Familiarize yourself with optimization algorithms, activation functions and train your first model.
Learn about convolution, how it is used in convolutional networks and advanced concepts such a batch normalization and skip connections.
Build RNNs and LSTMs from scratch and get to know how to use them for time series analysis and prediction.
Learn the theory and mathematics behind Autoencoders and Variational Autoencoders.
Dive into adversarial attacks, GANs and explore how to train one using Pytorch.
Discover attention-based models, the Transformer architecture and how they revolutionalized the natural language processing field.
Process and analyze graph data with Graph Neural Networks.
Educative.io offers strictly interactive text-based courses focusing on hands-on experience without the hassle of videos. That way you speed up the learning process, keep notes without pausing the video and access anything fast.
You can quickly jump on coding using the provided live coding environements and jupyter notebooks. No setup, no installing dependencies. Program and execute your code straight in your browser.
Build and train your models in Jupyter notebooks with zero setup. Experiment with different hyperparameters, optimization algorithms and architectures all in your browser.
You can test your understanding using interactive quizzes, easy leetcode-style coding challenges with solutions. In the end, you will also need to pass a final assesment finish the course.
We believe that text is more effective and faster for learning advanced concepts such as Deep Learning.
For more than a year, we wrote weekly deep learning articles for the AI Summer blog. One day we came across educative.io and we had this awesome idea. To combine some of them into a single resource and give you the ability to learn Deep Learning by doing. That's why 70% of the course's content can be found in our blog for free. So if you can't afford this course, you are more than welcome to read our free articles. If on the other hand you want to support us for our efforts and learn using a highly interactive platform, you can spend a few bucks.
Because deep learning is an advanced field, you will probably need
You can purchase it for one year with $39 or you can subscribe to educative.io and get access to all the course with a small monthly fee
All coding exercises and jupyter notebooks can be found on our Github repository
You will write your code in Pytorch and perhaps you will need to use Numpy.
Of course, every lesson you complete is saved in your profile.
Yes, you can view a few selected lessons beforehand.
If you purchased the course within 7 days, you can request a refund by sending us an email with your order ID at firstname.lastname@example.org.
You can find the order ID in the confirmation email that you received or transactions.
Note that this return policy only applies to individual course subscriptions and NOT unlimited access subscriptions.
I'm afraid not. Educative.io prohibits to download any content and reshare it.
Feel free to use the contact form on the Contact page or email us at email@example.com, firstname.lastname@example.org