DIY.AI: Artificial Intelligence and Deep Learning Demystified | Online
About this workshop
Neural Networks are everywhere! They power self-driving cars; speech recognition, e.g. Apple’s Siri and Amazon’s Alexa; Google and Facebook’s computer vision systems; automatic diagnoses of medical imagery; drug discovery; and a plethora of other applications. Successes in AI are driven by neural networks. These successes are also driving significant investments in people who can train and scale neural networks technology — starting offers being high as $1 million per year (http://nyti.ms/1RBj2UF).
Takeaways
- Train and build convolutional neural networks to classify and detect objects from images
- Apply word2vec and recurrent neural network models to perform sentiment analysis and generate language
- Evaluate feasibility of solving tasks using neural network methodologies
- Apply neural networks to identify artistic style from paintings of famous painters
Prereqs & Preparation
Students should have familiarity with Python and High School Level Calculus. Background in Linear Algebra and Gradient Calculus is preferred but not required.
Required: - Install Tensorflow and other relevant Python packages related to the material before class. Instructor will send out details for this. Instructor will also send out materials that will allow students to familiarize themselves Linear Algebra and Calculus.
