TensorFlow: Deep Neural Networks & Image Classification Using Estimators

TensorFlow    |    Intermediate
  • 15 videos | 1h 11m 50s
  • Includes Assessment
  • Earns a Badge
Rating 4.3 of 3 users Rating 4.3 of 3 users (3)
Discover how to apply deep learning techniques to images, and how to leverage TensorFlow estimators in building image classification models.

WHAT YOU WILL LEARN

  • 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

IN THIS COURSE

  • 1m 52s
  • 5m 38s
    In this video, you will learn how to distinguish between traditional machine learning and deep learning. FREE ACCESS
  • Locked
    3.  Basic Structure of a Neural Network
    4m 45s
    After completing this video, you will be able to recognize the architecture and design of a neural network. FREE ACCESS
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    4.  The Mathematical Function Applied By a Neuron
    3m 28s
    In this video, you will identify what is meant by model weights or model parameters. FREE ACCESS
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    5.  Linear Transformation and Activation Functions
    6m 51s
    In this video, find out how to identify the precise operations performed by a neuron. FREE ACCESS
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    6.  Training a Neural Network Using Gradient Descent
    5m 44s
    Upon completion of this video, you will be able to recognize gradient descent as the training process for a neural network. FREE ACCESS
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    7.  Forward Pass and Backward Pass
    3m 28s
    During this video, you will learn how to distinguish between the operations in the forward and backward passes during training. FREE ACCESS
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    8.  Image Representations in Machine Learning
    6m 50s
    After completing this video, you will be able to describe how images are fed into a machine learning algorithm. FREE ACCESS
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    9.  Set Up TensorFlow and Use Jupyter Notebooks
    3m 17s
    In this video, you will learn how to configure TensorFlow and use Jupyter notebooks. FREE ACCESS
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    10.  The MNIST Dataset
    5m 21s
    During this video, you will learn how to load and explore the MNIST dataset for image classification. FREE ACCESS
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    11.  Training an Estimator for Image Classification
    7m 27s
    In this video, find out how to train a deep neural network for image classification. FREE ACCESS
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    12.  Predicting Image Labels
    4m 30s
    In this video, learn how to use an estimator to predict image labels. FREE ACCESS
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    13.  Drawbacks of Deep Neural Networks for Images
    2m 24s
    Upon completion of this video, you will be able to describe why deep neural networks are not effective with images. FREE ACCESS
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    14.  Exercise: Working with Neural Networks
    6m 27s
    In this video, you will "define how neural networks work". FREE ACCESS
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    15.  Exercise: Working with Image Classification
    3m 49s
    After completing this video, you will be able to recall the basics of image classification using neural networks. FREE ACCESS

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