Unit 1 – Introduction to Deep Learning

• What is Deep Learning
• Trends driving the rise of Deep Learning
• Practical Applications of Deep Learning
• What is a Neural Network?
• Supervised Learning with Neural Networks
• Why is Deep Learning taking off?
• Practical Project: Introduction to Deep Learning

Unit 2 – Neural Networks Basics

• Binary Classification
• Logistic Regression
• Logistic Regression Cost Function
• Gradient Descent
• Derivatives
• Computation graph
• Derivatives with a Computation Graph
• Vectorization
• Broadcasting in Python
• Practical Project: Creating a Logistic Regression Image Classifier

Unit 3 – Shallow Neural Networks

• Neural Network Representation
• Computing a Neural Network’s Output
• Vectorizing across Multiple Examples
• Activation Functions
• Non-linear Activation Functions
• Derivatives of Activation Functions
• Gradient Descent for Neural Networks
• Neural Network Back-propagation
• Random Parameter Initialization
• Practical Project: Data Classification with one Hidden Layer

Unit 4 – Deep Neural Networks

• Deep L-layer Neural Network
• Forward Propagation in a Deep Network
• Motivation behind Deep Representations
• Building Blocks of Deep Neural Networks
• Back-propagation in a Deep Neural Network
• Understanding Parameters and Hyper-parameters
• Practical Project: Building a Deep Neural Network Application

Unit 5 – TensorFlow Basics

• Introduction to TensorFlow
• TensorFlow Basic Syntax
• TensorFlow Graphs
• Variables and Placeholders
• TensorFlow Regression Example
• TensorFlow Classification Example
• Saving and Restoring Models
• Practical Project: Developing an Image Classifier with TensorFlow
  

Unit 6 – Convolutional Neural Networks (CNN)

• Convolutional neural networks structure
• Different layers of a CNN 
• Arrangement of spatial parameters
• How and when to use stride and zero-padding
• CNN model training process
• How to build a CNN
• How to make predictions and calculate the loss function
• How to build an image classification using CNN

• Professionals or students who are interested in computer vision, deep learning, and already have Python Programming and machine learning experience.
• Future Computer Vision Professionals and Engineers
• Intermediate Python programmers interested in further developing  their skills in Computer Vision and Deep Learning

Python Programming Experience
Python for Data Science and Machine Learning

The participants who have successfully completed this course will be able to develop leading edge deep learning and computer vision applications.