This course teaches the core concepts of deep learning using TensorFlow,
Google’s open-source computation graph library.
Deep learning has become standard in the tech industry, achieving
state-of-the-art results in computer vision, natural language processing,
and artificial intelligence. TensorFlow provides the flexibility needed to
implement and research cutting edge architectures while allowing users to
focus on the structure of their models as opposed to mathematical minutiae.
Students will learn modern techniques with hands-on model building, data
collection/transformation, and deployment.
Deep learning is not an entry-level subject. In order to get the most out of the course, students should be familiar with the following:
Basic linear algebra (matrix multiplication, transposing matrices)
Basic calculus (derivatives, summations)
Programming: Python preferred, but those comfortable with another language should be able to learn the material
There will be a pre-course workshop to refresh students on the requisite linear algebra and calculus techniques. Students with familiarity with NumPy will find it easier to pick up the material, but necessary components will be taught along with the rest of the course. Course materials will be hosted on GitHub, so knowledge of the bash console, Git, and the GitHub platform are beneficial, but not required.
Upon completion of the Deep Learning with TensorFlow course, students have:
An understanding of the techniques, concepts, terminology, and mathematics of deep machine learning for computer vision and natural language processing
Proficiency in TensorFlow to implement, train, visualize, export, and deploy deep models from scratch, as well as utilize pre-trained sources
Skills for cleaning, normalizing, and generating data to make the most out of available datasets.
Foundational knowledge of recent deep learning literature and experience implementing concepts introduced in those papers
There are currently no events scheduled for this course.
Join our meetup (CHI | NYC | SEA | SF) to be alerted of future events.
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Course Structure and Syllabus
Linear Algebra and Calculus Workshop (Optional)
Before class officially begins, we’ll have a brief, optional, on-site workshop for those in the class who need to brush up on their linear algebra and calculus.
We won’t need all of the theoretical knowledge from those subjects, but certain practical techniques are necessary:
Single variable derivatives
Summations (and derivatives of summations)
Matrix manipulation (transpose, inverse)
TensorFlow and Machine Learning Fundamentals
This unit focuses on getting students acquainted with the TensorFlow framework as well as developing an intuition for techniques that are used throughout machine learning.
TensorFlow core API
Introduction to TensorBoard
Linear, logistic regression
Neural Network Basics
Once we have familiarity with basic machine learning concepts, we will introduce the feedforward neural network, which lays the foundation for the rest of the models in the class.
Using pixels as independent inputs has several weaknesses, is there a better way? Yes! Convolutional layers are inspired from traditional image processing techniques, and are currently used in all manner of state of the art convolutional neural networks, or CNNs.
AlexNet, ResNet, Inception, and beyond
Transfer learning (for fun and profit!)
Recurrent Neural Networks
Sometimes your inputs come in various lengths, such as text (each sentence could be of different length), or be naturally sequential (such as time series). Recurrent neural networks, or RNNs, are designed to handle data that comes in different lengths, and have become incredibly popular for various natural language processing tasks.
Long short term memory (LSTM)
Deploying TensorFlow Models
TensorFlow includes functionality to help developers easily put their models up in a deployment setting, which is one of the more attractive features of the software. With this, students should be able to take their trained models and get them ready to be used out in the wild.
Rudimentary TensorFlow deployment in pure Python
Setting up a basic TensorFlow Serving server (with some C++)