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Accredited Professional Development Course

Deep Learning with TensorFlow

Offered Live Online & In Person

Deep learning has become standard in the tech industry, achieving state-of-the-art results in computer vision, natural language processing, and artificial intelligence. This course teaches the core concepts of deep learning using TensorFlow, Google’s open-source computation graph library.

Who the course is designed for:

Those who want to implement and research cutting-edge architectures while focusing on the structure of their models as opposed to mathematical minutiae. Those who want to learn modern techniques with hands-on model building, data collection/transformation, and deployment.


  • 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 of available datasets.
  • Foundational knowledge of recent deep learning literature and experience implementing concepts introduced in those papers.
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What you'll receive upon completion:

  • Certificate of completion
  • Certificate link and instructions on how to add to your LinkedIn profile
  • 3.3 Continuing Education Units

Dates & Instructors

Live Online

Deep Learning with TensorFlow

June 4 to July 11

Mondays and Wednesdays

6:30 - 9:30pm Eastern Time



Deep learning is not an entry-level subject. In order to get the most out of the course, students should be familiar with basic statistics, basic linear algebra (matrix multiplication, transposing matrices), basic calculus (derivatives, summations), and 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.

Considering our immersive data science bootcamp?

Professional development alumni can apply the amount of tuition paid for one part-time course towards enrollment in an upcoming bootcamp upon admittance.

Course Structure & Syllabus

Unit 0
Linear Algebra and Calculus Workshop (Optional)

Before class officially begins, we’ll have a brief, optional, workshop for those who need to brush up on linear algebra and calculus. While you won’t need all of the theoretical knowledge from those subjects, certain practical techniques are necessary, including:

  • Single variable derivatives
  • Summations (and derivatives of summations)
  • Matrix multiplication
  • Matrix manipulation (transpose, inverse)
Unit 1
TensorFlow and Machine Learning Fundamentals

Get acquainted with the TensorFlow framework and develop an intuition for techniques used throughout machine learning.

  • Installing TensorFlow
  • TensorFlow core API
  • Introduction to TensorBoard
  • Linear, logistic regression
  • Gradient descent
Unit 2
Neural Network Basics

Once familiarity with basic machine learning concepts is established, introduce the feedforward neural network, which lays the foundation for the rest of the models in the class.

  • Neural network structure
  • Neurons, activation functions (sigmoid, tanh, ReLU)
  • Forward propagation and backpropagation
  • Dropout, softmax, cross-entropy
Unit 3
Convolutional Neural Networks

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.

  • Convolutions, pooling
  • AlexNet, ResNet, Inception, and beyond
  • Transfer learning (for fun and profit!)
  • Object detection
Unit 4
Recurrent Neural Networks

Sometimes your inputs come in various lengths such as text (each sentence could be of different length), or are naturally sequential (such as time series). Recurrent neural networks (RNNs) are designed to handle data that comes in different lengths and have become incredibly popular for various natural language processing tasks.

  • RNN basics
  • Long short term memory (LSTM)
  • Sentence classification
  • Text generation
Unit 5
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++)
  • Accessing your model server remotely


Have more questions? No problem. Schedule a chat with admissions