Artificial Neural Networks, Machine Learning, Deep Thinking Advanced

Who needs to attend

Who needs to attend?

what you will learn

What you will learn



Artificial Neural Networks, Machine Learning, Deep Thinking
Course outline

Course Outline

# Recap from previous course – Basic ML

* Machine Learning
* Neural Networks
* Deep Learning

# Image Fundamentals

* Pixels
* Image Channels and channel ordering
* Scale
* OpenCV library
* Reading, writing images
* Opening/writing a video stream (Webcam, etc)

# Convolutional Neural Networks

* Recap
* Batch Normalization
* Drop out
* Saving, loading a model
* Restarting training
* TensorBoard

# Transfer Learning

* Popular datasests (ImageNet, etc)
* Existing CNNs for Image Classification (Inception, ResNet, etc)
* Freezing layers
* Transfer Learning on our own classification problem

# Object Localization

* Problem definition
* Popular datasets (Coco, etc)
* Existing frameworks (YOLO, etc)

# Deploying ML

* Protobuf, flatbuffers, JSON, gRPC
* Remote Procedure Call
* TensorFlow RESTful API

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