Neural Network from Scratch in TensorFlow

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In this Guided Project, you will:

How to implement a neural network from scratch using TensorFlow.

How to solve a multi-class classification problem using the neural network implementation.

Clock2 hours
IntermediateIntermediate
CloudNo download needed
VideoSplit-screen video
Comment DotsEnglish
LaptopDesktop only

In this 2-hours long project-based course, you will learn how to implement a Neural Network model in TensorFlow using its core functionality (i.e. without the help of a high level API like Keras). You will also implement the gradient descent algorithm with the help of TensorFlow's automatic differentiation. While it’s easier to get started with TensorFlow with the Keras API, it’s still worth understanding how a slightly lower level implementation might work in tensorflow, and this project will give you a great starting point. In order to be successful in this project, you should be familiar with python programming, TensorFlow basics, conceptual understanding of Neural Networks and gradient descent. Note: This course works best for learners who are based in the North America region. We’re currently working on providing the same experience in other regions.

Skills you will develop

Data ScienceDeep LearningMathematical OptimizationArtificial Neural NetworkTensorflow

Learn step-by-step

In a video that plays in a split-screen with your work area, your instructor will walk you through these steps:

  1. Create the Neural Network class

  2. Create a forward pass function

  3. Use the cross entropy loss with logits

  4. Create a predict function

  5. Create the main training mechanism and implement gradient descent with automatic differentiation

  6. Break down data-set in batches

  7. Apply the neural network model to solve a multi-class classification problem

  8. Plot the training results

How Guided Projects work

Your workspace is a cloud desktop right in your browser, no download required

In a split-screen video, your instructor guides you step-by-step

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Frequently Asked Questions

More questions? Visit the Learner Help Center.