TensorFlow: Deep Neural Networks and Image Classification


Overview/Description
Expected Duration
Lesson Objectives
Course Number
Expertise Level



Overview/Description

Discover how to apply deep learning techniques to images, and how to leverage TensorFlow estimators in building image classification models.



Expected Duration (hours)
1.3

Lesson Objectives

TensorFlow: Deep Neural Networks and Image Classification

  • distinguish between traditional machine learning and deep learning
  • recognize the architecture and design of a neural network
  • identify what is meant by model weights or model parameters
  • identify the precise operations performed by a neuron
  • recognize gradient descent as the training process in a neural network
  • distinguish between the operations in the forward and backward passes during training
  • describe how images are fed into a machine learning algorithm
  • configure TensorFlow and use Jupyter notebooks
  • load and explore the MNIST dataset for image classification
  • train a deep neural network estimator for image classification
  • use an estimator to predict image labels
  • describe why deep neural networks don't work well with images
  • "define how neural networks work "
  • recall basics of image classification using neural networks
  • define the role of convolutional and pooling layers in a convolutional neural network
  • Course Number:
    it_sdaidt_03_enus

    Expertise Level
    Intermediate