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Intro to Deep Learning With TensorFlow

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3 Days
DCSK-120
Classroom Training, Online Training
Other

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Course Description

Overview

This Intro to Deep Learning With TensorFlow course is designed to introduces Deep Learning concepts and Tensorflow library to students.

The abundance of data and affordable cloud scale has led to an explosion of interest in Deep Learning. Google has released an excellent library called Tensorflow to open-source, allowing state-of-the-art machine learning done at scale, complete with GPU-based acceleration.
 

Objectives

Upon completion of the Intro to Deep Learning With TensorFlow course, students will be able to:
  • Introduction to Machine Learning
  • Deep Learning concepts
  • Tensorflow library
  • Writing Tensorflow applications (CNN, RNN)
  • Using TF tools
  • High level libraries : Keras

Audience

  • Developers, Data analysts, data scientists

Prerequisites

    • Basic knowledge of Python language and Jupyter notebooks is assumed.
    • Basic knowledge of Linux environment would be beneficial
    • Some Machine Learning familiarity would be nice, but not necessary.

Topics

Introduction to Machine Learning
  • Understanding Machine Learning
  • Supervised versus Unsupervised Learning
  • Regression
  • Classification
  • Clustering
Introducing Tensorflow
  • Tensorflow intro
  • Tensorflow Features
  • Tensorflow Versions
  • GPU and TPU scalability
  • Lab: Setting up and Running Tensorflow
The Tensor: The Basic Unit of Tensorflow
  • Introducing Tensors
  • Tensorflow Execution Model
  • Lab: Learning about Tensors
Single Layer Linear Perceptron Classifier With Tensorflow
  • Introducing Perceptrons
  • Linear Separability and Xor Problem
  • Activation Functions
  • Softmax output
  • Backpropagation, loss functions, and Gradient Descent
  • Lab: Single-Layer Perceptron in Tensorflow
Hidden Layers: Intro to Deep Learning
  • Hidden Layers as a solution to XOR problem
  • Distributed Training with Tensorflow
  • Vanishing Gradient Problem and ReLU
  • Loss Functions
  • Lab: Feedforward Neural Network Classifier in Tensorflow
High level Tensorflow: tf.learn
  • Using high level tensorflow
  • Developing a model with tf.learn
  • Lab: Developing a tf.learn model
Convolutional Neural Networks in Tensorflow
  • Introducing CNNs
  • CNNs in Tensorflow
  • Lab : CNN apps
Introducing Keras
  • What is Keras?
  • Using Keras with a Tensorflow Backend
  • Lab: Example with a Keras
Recurrent Neural Networks in Tensorflow
  • Introducing RNNs
  • RNNs in Tensorflow
  • Lab: RNN
Long Short Term Memory (LSTM) in Tensorflow
  • Introducing RNNs
  • RNNs in Tensorflow
  • Lab: RNN
Conclusion
  • Summarize features and advantages of Tensorflow
  • Summarize Deep Learning and How Tensorflow can help
  • Next steps
  • Spark V2 for Developers

    DCSK-100
    • Duration: 3 Days
    • Delivery Format: Classroom Training, Online Training
    • Price: 2,100.00 USD
  • Solr for Developers

    SESR-100
    • Duration: 3 Days
    • Delivery Format: Classroom Training, Online Training
    • Price: 2,100.00 USD
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