Python and TensorFlow Applied to Deep Learning and Neural Networks

Python and TensorFlow Applied to Deep Learning and Neural Networks
Python and TensorFlow Applied to Deep Learning and Neural Networks
English | MP4 | AVC 1280×720 | AAC 44KHz 2ch | 0h 56m | 253 MB

This video focuses on Python and its useful libraries including TensorFlow. You will learn how to set up a Python environment and apply Python to deep learning and neural nets. There are six clips in this video:

  • About Python. This clip introduces the objectives and prerequisites of the video, and explains why Python is ideal for deep learning. Go through all of the steps to set up the Python environment. An overview to all of the major Python libraries including TensorFlow is provided.
  • About Deep Learning. This clip explores learning in general, deep learning, and the drivers and enablers for deep learning. Examples are provided including Handwritten Digit Recognition. Learn how to launch the IPython environment and use Jupyter and MNIST.
  • About TensorFlow. This clip provides instruction on how to install TensorFlow. Learn what TensorFlow is and why it is so powerful. The components of Rank, Shape, and Data Type are explained.
  • Hands-On with TensorFlow and MNIST. This clip illustrates a series of exercises on TensorFlow and MNIST. Learn how to import and export data, and all about variables, graphs, and sessions.
  • About Neural Networks. This clip explores neural networks and reveals the variations including Deep Feed Forward (DFF) networks. Learn how to initialize, activate, and optimize neural networks with TensorFlow. Walk through a TensorFlow Neural Network in detail. Learn about training, testing, and inference in TensorFlow, and explore the APIs built upon TensorFlow including keras and tflearn.
  • Table of Contents

    1 About Python
    2 About Deep Learning
    3 About TensorFlow
    4 Hands-On with TensorFlow and MNIST
    5 About Neural Networks