Series Overview 🌟
The majority of data in the world is unlabeled and unstructured, for instance, images, sound, and text data. Shallow neural networks cannot easily capture relevant structures within this type of data, but deep networks are capable of discovering the hidden structures. With the help of various frameworks we can build neural networks that allow us to demystify various use cases based on various industries and predict outcomes, thus they have now become an integral part of our day to day lives.
As a follow-up to our Your Path to AI series which was conducted in 2020, we are now conducting Your Path to Deep Learning. A series which is mainly focused on the fundamentals of deep learning and with this series you will get an understanding of deep learning concepts, deep learning architectures, a comparison of deep learning frameworks and you will build various deep learning models throughout the series for linear and logistic regression, recurrent neural networks and Restricted Boltzmann Machines.
👩💻Resources
- GitHub Repository - https://github.com/IBMDeveloperMEA/YPDL-Recurrent-Neural-Networks-using-TensorFlow-Keras
- Workshop Slides - https://ibmdevelopermea.github.io/YPDL-Recurrent-Neural-Networks-using-TensorFlow-Keras/#/
- Survey - https://ibm.biz/YPDL-Survey
- Follow along for the hands-on - https://ibm.biz/rnn-tensorflow-NNLab
- Meetup page - https://www.meetup.com/IBM-Cloud-MEA/events/
🎈 Prerequisites
☁ Sign in/Login into IBM Cloud using: https://ibm.biz/YourPathToDeepLearning
🍉 Register for the live stream and replay on Crowdcast: https://www.crowdcast.io/e/ypdl-3
👩💻 Who should attend?
- Developers interested in building deep learning models using Python
- Deep learning & machine learning enthusiasts
- Developers discovering deep learning algorithms
- Developers building use cases around deep learning
What will you learn? ✍🏼
- More about deep learning algorithms
- Deep learning frameworks
- Deep learning architectures
- Compare deep learning frameworks
- Implementing deep learning algorithms
