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Deep Learning

Deep Learning


NYC Data Science Academy
500 8th Ave
Ste 905
New York, NY 10018
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Saturday, March 16, 2019 - 7:00am


Facilitated by the intersection of inexpensive computing power, unprecedently large data sets, and clever computational statistics advances, Deep Learning algorithms are driving an Artificial Intelligence revolution. Deep Learning has emerged as uniquely influential across a broad range of the statistical domain, including classification (e.g., visual recognition, sentiment analysis), prediction (e.g., stock markets, health outcomes) and generation (e.g., creating images, composing music). In the past few years, Deep Neural Networks have made their way into countless everyday applications, including Teslas Autopilot, Siris voice recognition, Facebooks face identification, and hundreds of products at Google such as Inbox with its suggested replies. Indeed, Deep Learning algorithms have exceeded human performance on previously intractable computational problems like language translation, object detection, and the game of Go. Via analogy to biological neurons and human perception, this course is an introduction to artificial neural networks that brings high-level theory to life with interactive labs that feature the most popular open-source Deep Learning library, TensorFlow, and its high-level API, Keras. Essential theory will be covered in a manner that provides students with an intuitive understanding of Deep Learnings underlying foundations. Paired with hands-on code run-throughs in Jupyter notebooks as well as strategies for overcoming common pitfalls, this foundational knowledge will empower individuals with no previous understanding of neural networks to build production-ready Deep Learning applications across the major contemporary families: Convolutional Nets for machine vision Long Short-Term Memory Recurrent Nets for natural language processing and time series analysis Generative Adversarial Networks for producing realistic images and Reinforcement Learning for playing video games Goals This is a short course of five weeks, with six hours of class per week. Classes will be given in a lab setting, with hands-on exercises mixed with lectures. Students should bring a laptop to class. You will have the option of creating and completing your own major Deep Learning project over the duration of the program. Who Is This Course For This course is perfect for software engineers, data scientists, analysts, and statisticians with an interest in Deep Learning. No previous knowledge of Deep Learning is assumed. Previous experience with statistics or machine learning is not necessary, but will be an asset if you have it. Code examples are provided in Python, so familiarity with it or another object-oriented programming language would be helpful. Prerequisites It will be challenging to follow along through the code demos and exercises without some experience in: Object-oriented programming, ideally Python Simple shell commands, e.g., in Bash (tutorial of the fundamentals) Outcomes By the end of the course, you will be able to: Build Deep Learning models in TensorFlow and Keras Interpret the results of Deep Learning models Troubleshoot and improve Deep Learning models Understand the language and fundamentals of artificial neural networks Build your own Deep Learning project

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