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Deep Learning with TensorFlow

List Tuition: $250.00

Course Overview

Duration: 20 Hours
Access: 1 Year
Format: Self-Paced Learning

This course introduces Deep Learning concepts and TensorFlow library to students. Students will work hands-on with TensorFlow technologies to learn to build their own Deep Learning solutions.

The abundance of data and affordable cloud scale has led to an explosion of interest in Deep Learning. Google has open sourced a library called TensorFlow which has become the de-facto standard, allowing state-of-the-art machine learning done at scale, complete with GPU-based acceleration.

    Audience

    This is a technical, hands-on course where students will work with the latest technologies in a series of labs to learn how to create their own solutions. To be successful in this course you need to be a seasonsed software developer who is comfortable and fluent in the Python development language.

    This course also assumes students have a proficient level of knowledge regarding Jupyter notebooks as well. Students are also expected to have a strong knowledge of the concepts which underlie Machine Learning - this course will NOT cover these concepts in-depth!

      Course Outline

      • Chapter 1: Introduction to Machine Learning
        • Understanding Machine Learning
          • How does ML relate to AI?
          • How does DL relate to ML/AI?
        • Supervised versus Unsupervised Learning
        • Regression
        • Classification
        • Clustering
      • Chapter 2: Introducing TensorFlow
        • TensorFlow intro
        • TensorFlow features
        • TensorFlow versions
        • GPU and TPU scalability
        • Lab: Setting up and Running TensorFlow
      • Chapter 3: The Tensor: The Basic Unit of TensorFlow
        • Introducing Tensors
        • TensorFlow Execution Model
        • Lab: Learning about Tensors
      • Chapter 4: 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
      • Chapter 5: 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
      • Chapter 6: High-level TensorFlow: tf.learn
        • Using high-level TensorFlow
        • Developing a model with tf.learn
        • Lab: Developing a tf.learn model
      • Chapter 7: Convolutional Neural Networks in TensorFlow
        • Introducing CNNs
        • CNNs in TensorFlow
        • Lab: CNN apps
      • Chapter 8: Introducing Keras
        • What is Keras?
        • Using Keras with a TensorFlow Backend
        • Lab: Example with a Keras
      • Chapter 9: Recurrent Neural Networks in TensorFlow
        • Introducing RNNs
        • RNNs in TensorFlow
        • Lab: RNN
      • Chapter 10: Long Short Term Memory (LSTM) in TensorFlow
        • Introducing RNNs
        • RNNs in TensorFlow
        • Lab: RNN
      • Chapter 11: Conclusion
        • Summarize features and advantages of TensorFlow
        • Summarize Deep Learning and How TensorFlow can help

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      Deep Learning with TensorFlow

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