Overview and Learning Objectives

Quick TensorFlow- Tutorial Series

Machine Learning with TensorFlow and Keras

Quickly Develop Skills to Master Deep Machine Learning

These supplemental tutorials are designed to supplement concepts you may encounter in this course or as a machine learning practitioner. They are keyed to the Quick TensorFlow course titled Image Classification with Transfer Learning using TensorFlow. The goal is to give you a meaningful introduction to the important concepts and tools of modern machine learning practitioners that use the deep learning framework TensorFlow to build image classification models for deployment. They should help those just getting started with machine learning (ML) and artificial neural networks (ANN) develop the skills and master the techniques required for building and deploying deep learning models for image classification.



Using Google's Cloud Machine Learning Platform-Colaboratory


Understanding Tensors and how to use them in TensorFlow

Convolutional Neural Networks(CNNs)

Creating and Using Convolutional Neural Networks

Custom Images

Using your own Image Datasets in Colab

Transfer Learning in a Nutshell

The Sequential Model and Transfer Learning

Some Historically Important Convolutional Neural Networks

LeNet-5, AlexNet and ResNet

Discussion of CNN Architectures from LeNet-5 to AlexNet to ResNet

LeNet-5, Inception, AlexNet and ResNet

Calculations Important to Convolutional Neural Networks

Tensor Sizes and Layer Outputs

Complete and Continue