Deep Learning & Machine Learning with TensorFlow

SkillsFuture Credit Approved Course

Introduction

Deep learning (deep structured learning, hierarchical learning or deep machine learning) is a branch of machine learning based on a set of algorithms that attempt to model high level abstractions in data.

TensorFlow is a popular, new and comprehensive library for deep learning. Students will learn to build deep learning applications using Tensorflow in this course.

Course Content

  1. Getting Started 
  • Introduction to TensorFlow
  • Installing and Running TensorFlow
  1. Basic Tensorflow Operations
  • Constant
  • Graph Operation
  • Math
  • Matrix
  • Placeholder
  • Variable
  1. Datasets
  • Iris Flower Dataset
  • MNIST Handwritten Digits Dataset
  • CIFAR Image Dataset
  • One Hot Encoding/Decoding
  • Split Dataset to Training/Testing
  1. Machine Learning on TF
  • TF Graph Model
  • Loss Function
  • Optimizer
  • Training
  • Metrics
  1. Neural Network (NN)
  • What is Neural Network
  • Activation Functions
  • Create a Deep Neural Network on TF
  • TensorFlow Playground
  1. Tensorboard
  • What is Tensorboard?
  • Visualize a Tensorboard Graph
  • Output Data to Tensorboard
  1. Convolutional Neural Network (CNN)
  • What is CNN?
  • CNN Architecture
  • Convolution
  • Pooling and Stride
  • Dropout
  1. Recurrent Neural Network (RNN)
  • What is RNN?
  • How to train a RNN
  • Long Term Dependencies
  • LSTM Cell
  • GRU Cell
  1. Keras
  • What is Keras?
  • Install Keras
  • Neutral Network with Keras
  • Inception V3 Transfer Learning
  1. TFLearn
  • What is TFLearn?
  • Install TFLearn
  • Neutral Network with TFLearn