“All knowledge is connected to all other knowledge. The fun is in making the connections.” - Arthur Aufderheide
The objective for the Deep Learning bootcamp is to ensure that the participants have enough theory and practical concepts of building a deep learning solution in the space of computer vision and natural language processing. Post the bootcamp, all the participants would be familiar with the following key concepts and would be able to apply them to a problem.
Key Deep Learning Concept
- Theory: DL Motivation, Back-propagation, Activation
- Paradigms: Supervised, Unsupervised
- Models: Architecture, Pre-trained Models (Transfer Learning)
- Methods: Perceptron, Convolution, Pooling, Dropouts, Recurrent, LSTM
- Process: Setup, Encoding, Training, Serving
The material for the workshop is hosted on github:
This is from the popular workshop series by the speakers on deep learning. Additional materials relevant to learning Deep Learning would be shared prior to the workshop.
- A machine learning practitioner
- A programmer interested in building data science products
- Anyone (researcher, student, professional) learning machine learning
- Corporates and Start-ups looking to add DL to their product or service offerings
- This is a hands-on course and hence, participants should be comfortable with programming. Familiarity with python data stack is ideal.
- Prior knowledge of machine learning will be helpful. Participants should have some practice with basic machine learning problems e.g. regression, classification.
- While the DL concepts will be taught in an intuitive way, some prior knowledge of linear algebra and calculus would be helpful.
We will be using Python data stack for the workshop with
tensorflow for the deep learning component. Please install Ananconda for Python 3 for the workshop. Additional requirement will be communicated to participants.