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- Learn how to build a TensorFlow machine learning model using the Keras API.
In this module you will:
- Learn to load and prepare data to be used in machine learning.
- Learn to specify the architecture of a deep learning neural network.
- Learn to train a neural network.
- Learn how to use a neural network to make a prediction.
- Learn how to perform different computer vision tasks using TensorFlow.
In this module you will:
- Learn how to build computer vision machine learning models
- Learn how to represent images as tensors
- Learn how to build Dense Neural Networks and Convolutional Neural Networks
- In this module, we explore different neural network architectures for processing natural language texts.
In this module you will:
- Understand how text is processed for natural language processing tasks
- Get introduced to Recurrent Neural Networks (RNNs) and generative networks
- Learn how to build text classification models
- Learn how to generate text with recurrent networks
- Learn how to prepare audio data, create spectrograms, and build a TensorFlow keyword classification model.
In this module you will:
- Describe how sample rate, amplitude, channels, and waveforms represent audio data.
- Convert audio waveforms into spectrogram tensors for model training.
- Build and evaluate a binary TensorFlow keyword classification model that recognizes "yes" and "no".
- Learn how to build a machine learning model using lower-level TensorFlow concepts.
In this module, you will:
- Learn basic TensorFlow topics, such as tensors, variables, and automatic differentiation.
- Learn the difference between eager and graph execution.
- Reimplement the train, test, and prediction phases of an existing Keras project using TensorFlow.
