deeplizard teaches us how to normalize a dataset. We’ll see how dataset normalization is carried out in code, and we’ll see how normalization affects the neural network training process.
Content index:
deeplizard demonstrates how to process numerical data that we’ll later use to train our very first artificial neural network in this video.
In this video, deeplizard discusses GPU support for TensorFlow and the integrated Keras API and how to get your code running with a GPU.
deeplizard demonstrates how we can use the CUDA capabilities of PyTorch to run code on the GPU in this episode.
In this episode, deeplizard discusses the Keras integration with TensorFlow (tf.keras), compatibility requirements, and how to get Keras installed on your machine!
deeplizard has put together a course on how to use Keras.
In this course, we will learn how to use Keras, a neural network API written in Python! In this episode, we discuss the prerequisites required to start working with Keras, why Keras is a good library to learn, and what resources you’ll have available to ensure your success in this course.
Siraj Raval explores why does a computer algorithm classify an image the way that it does? This is a question that is critical when it comes to AI applied to diagnostics, driving, or any other form of critical decision making.
In this video, he raises awareness around one technique in particular that I found called “Grad-Cam” or Gradient Class Activation Mappings.
In this video, deeplizard will see how we can experiment with large numbers of hyperparameter values easily while still keeping the training loop and results organized.
Here’s a great article on R-CNN, object detection, and the ins and outs of computer vision.
After exploring CNN for a while, I decided to try another crucial area in Computer Vision, object detection. There are several methods popular in this area, including Faster R-CNN, RetinaNet, YOLOv3, SSD and etc. I tried Faster R-CNN in this article. Here, I want to summarise what I have learned and maybe give you a little inspiration if you are interested in this topic.
In this video from deeplizard, learn how to build, plot, and interpret a confusion matrix using PyTorch. They also cover about locally disabling PyTorch gradient tracking or computational graph generation.
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