Jon Wood shows us how to use the new image classification command in the ML.NET CLI.
deeplizard demonstrates how to use data augmentation on images using TensorFlow’s Keras API.
VIDEO SECTIONS
- 00:00 Welcome to DEEPLIZARD – Go to deeplizard.com for learning resources
- 00:17 Introduction to Data Augmentation
- 01:32 Image Augmentation with Keras
- 08:16 Collective Intelligence and the DEEPLIZARD HIVEMIND
In this episode, Mandy from deeplizard will be building on what we’ve learned about MobileNet combined with the techniques we’ve used for fine-tuning to fine-tune MobileNet for a custom image data set using TensorFlow’s Keras API.
In this deeplizard episode, learn how to prepare and process our own custom data set of sign language digits, which will be used to train our fine-tuned MobileNet model in a future episode.
VIDEO SECTIONS
- 00:00 Welcome to DEEPLIZARD – Go to deeplizard.com for learning resources
- 00:40 Obtain the Data
- 01:30 Organize the Data
- 09:42 Process the Data
- 13:11 Collective Intelligence and the DEEPLIZARD HIVEMIND
deeplizard introduces MobileNets, a class of light weight deep convolutional neural networks that are vastly smaller in size and faster in performance than many other popular models.
VIDEO SECTIONS
- 00:00 Welcome to DEEPLIZARD – Go to deeplizard.com for learning resources
- 00:17 Intro to MobileNets
- 02:56 Accessing MobileNet with Keras
- 07:25 Getting Predictions from MobileNet
- 13:32 Collective Intelligence and the DEEPLIZARD HIVEMIND
In this video, Mandy from deeplizard demonstrates how to use the fine-tuned VGG16 Keras model that we trained in the last episode to predict on images of cats and dogs in our test set.
Index:
- 00:00 Welcome to DEEPLIZARD – Go to deeplizard.com for learning resources
- 00:17 Predict with a Fine-tuned Model
- 05:40 Plot Predictions With A Confusion Matrix
- 05:16 Collective Intelligence and the DEEPLIZARD HIVEMIND
In this video, Mandy from deeplizard demonstrate how to fine-tune a pre-trained model called VGG16 to classify images as cats and dogs.
With applications ranging from classifying objects in self driving cars to identifying blood cells in healthcare industry to identifying defective items in manufacturing industry, image classification is one of the most important applications of computer vision.
How does it work? Which framework should you use?
Here’s a great tutorial.
In this article, we will understand how to build a basic image classification model in PyTorch and TensorFlow. We will start with a brief overview of both PyTorch and TensorFlow. And then we will take the benchmark MNIST handwritten digit classification dataset and build an image classification model using CNN (Convolutional Neural Network) in PyTorch and TensorFlow.
In this episode, deeplizard demonstrates the various ways of saving and loading a Sequential model using TensorFlow’s Keras API.
deeplizard demonstrates how to create a confusion matrix, which will aid us in being able to visually observe how well a neural network is predicting during inference.
VIDEO SECTIONS
- 00:00 Welcome to DEEPLIZARD – Go to deeplizard.com for learning resources
- 00:34 Plotting a Confusion Matrix
- 02:48 Reading a Confusion Matrix
- 04:56 Collective Intelligence and the DEEPLIZARD HIVEMIND