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.
Tutorials
Colab
Using Google's Cloud Machine Learning Platform-Colaboratory
Tensors
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