Build & Train RNNs: Implementing Recurrent Neural Networks

Neural Networks    |    Intermediate
  • 10 videos | 48m 23s
  • Includes Assessment
  • Earns a Badge
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Learners will examine the concepts of perception, layers of perception, and backpropagation, and discover how to implement recurrent neural network by using Python, TensorFlow, and Caffe2 in this 10-video course. Begin by taking a look at the essential features and processes of implementing perception and backpropagation in machine learning neural networks. Next, you will compare single-layer perception and multilayer perception and describe the need for layer management. You will learn about the steps involved in building recurrent neural network models; building recurrent neural networks with Python and TensorFlow; implementing long short-term memory (LSTM) by using TensorFlow, and building recurrent neural networks with Caffe2. Caffe is a deep learning framework. Building deep learning language models using Keras-an open source neural network library-will be explored in the final tutorial of the course. The concluding exercise entails implementing recurrent neural networks by using TensorFlow and Caffe2 and building deep learning language models by using Keras.

WHAT YOU WILL LEARN

  • Identify the essential features and processes of implementing perception and backpropagation
    Compare single and multilayer perception and describe the need for layer management
    Describe the steps involved in building recurrent neural network models
    Implement recurrent neural network using python and tensorflow
    Implement long short-term memory using tensorflow
  • Recognize the capabilities provided by caffe2 for implementing neural networks
    Implement recurrent neural network using caffe2
    Build deep learning language models using keras
    Implement rnn using tensorflow and caffe2 and build deep learning language models using keras

IN THIS COURSE

  • 2m 2s
  • 3m 21s
    Learn how to identify the essential features and processes of implementing perception and backpropagation. FREE ACCESS
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    3.  Single and Multilayer Perception
    3m 16s
    In this video, find out how to compare single and multilayer perception and describe the need for layer management. FREE ACCESS
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    4.  Building Recurrent Neural Network Models
    2m 40s
    After completing this video, you will be able to describe the steps involved in building recurrent neural network models. FREE ACCESS
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    5.  RNN with Python and TensorFlow
    8m 22s
    In this video, you will learn how to implement a recurrent neural network using Python and TensorFlow. FREE ACCESS
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    6.  LSTM with TensorFlow
    9m 53s
    In this video, you will implement long short-term memory using TensorFlow. FREE ACCESS
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    7.  Caffe2 and Neural Network
    3m 20s
    After completing this video, you will be able to recognize the capabilities provided by Caffe2 for implementing neural networks. FREE ACCESS
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    8.  Implement RNN with Caffe2
    6m 4s
    In this video, learn how to implement a recurrent neural network using Caffe2. FREE ACCESS
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    9.  Deep Learning Language Model with Keras
    5m 26s
    In this video, you will learn how to build deep learning language models using Keras. FREE ACCESS
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    10.  Exercise: Implement RNN Using TensorFlow and Caffe2
    3m 59s
    Find out how to implement RNN using TensorFlow and Caffe2, and build deep learning language models using Keras. FREE ACCESS

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