I had considered building a Hackintosh, but decided to pick up a used iMac to render Apple Motion videos.
I’m glad I went this route and Linus Tech Tips explains why.
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
Adding GPU compute support to Windows Subsystem for Linux (WSL) has been the #1 most requested feature since the first WSL release.
Learn how Windows and WSL 2 now support GPU Accelerated Machine Learning (GPU compute) using NVIDIA CUDA, including TensorFlow and PyTorch, as well as all the Docker and NVIDIA Container Toolkit support available in a native Linux environment.
Clark Rahig will explain a bit about what it means to accelerate your GPU to help with training Machine Learning (ML) models, introducing concepts like parallelism, and then showing how to set up and run your full ML workflow (including GPU acceleration) with NVIDIA CUDA and TensorFlow in WSL 2.
Additionally, Clarke will demonstrate how students and beginners can start building knowledge in the Machine Learning (ML) space on their existing hardware by using the TensorFlow with DirectML package.
Learn more:
- Related Microsoft Windows Blog Posts: https://aka.ms/GPUinWSL
- GPU-Accelerated ML Training Docs: https://aka.ms/GPUinWSLdocs
- NVIDIA Docs: https://developer.nvidia.com/cuda/wsl
- DirectML repo (Get started, Samples, etc): https://aka.ms/DirectML
- Follow Clark Rahig on Twitter: @crahrig
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.
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