Introduction to Deep Learning Image Classification with Keras - 2021
Channel: KAUST Visualization Core Lab
Date: 03/05/2021

Introductory tutorial on Deep Learning Image Classification with Keras. 

Introduces the elegant Keras deep learning programming interface to build and train TensorFlow models for image classification tasks on the CIFAR-10 / MNIST datasets. 

The interactive tutorial demonstrates a variety of image classification CNNs (convolutional neural networks) making use of transfer learning, skip connections, data augmentation, sequential & functional APIs, and a variety of visualization techniques to create models and better understand them. The tutorial uses various Python-based tools inside a Jupyter notebook environment.

The final exercise has participants apply what they learned to another dataset (MNIST), create their own model, and submit prediction results to Kaggle (an online community for data-scientists and machine learning practitioners) for evaluation.

Training materials: https://github.com/kaust-vislab/keras-tutorials-lite

Chapters:
0:00:00​ Introduction
0:04:32 Launch Notebook in Google Colab
0:04:46 Jupyter Notebook Review
0:08:31​ Setup Accelerated Colab
0:10:43​ Download CIFAR10 Data
0:12:25 Module Imports and Initialization
0:14:14 Load Data
0:20:43 Explore Data
0:48:39 Data Conversion
0:53:46 Pre-Trained Model Network
1:03:42 Explore Convolutional Layers
1:11:26 CNN Base + Classifier: Model
1:21:54 CNN Base + Classifier: Training
1:39:25 CNN Base + Classifier: Evaluate Model
1:46:40 CNN Classifier: Model
1:49:06 CNN Classifier: Evaluate Model
1:57:43 Combined Models
2:02:10 Combined Models: Evaluate
2:03:45 Skip Connections
2:10:55 Skip Connections: Evaluate
2:12:05 MNIST Exercise Review
2:21:47 CNN Model Exercise
2:33:55 Kaggle Digit Recognizer Competition
2:48:28 Concluding