Course Overview
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 LSTM
LSTM in TensorFlow
Lab: LSTM
Chapter 11: Conclusion
Summarize features and advantages of TensorFlow
Summarize Deep Learning and How TensorFlow can help
