deeplizard demonstrates how to use data augmentation on images using TensorFlow’s Keras API.
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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.
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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.
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vcubingx provides a visual introduction to the structure of an artificial neural network.
The Neural Network, A Visual Introduction | Visualizing Deep Learning, Chapter 1
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.
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In this video, Mandy from deeplizard demonstrate how to fine-tune a pre-trained model called VGG16 to classify images as cats and dogs.
GPT-3 has 175 billion parameters/synapses and produces astonishing results.
Human brain has 100 trillion synapses. How much will it cost to train a language model the size of the human brain?
Lex Fridman works through the math.
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.
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