This module provides all the concepts and practical knowledge you need to get started with TensorFlow. We explore Keras, a high-level API released as part of TensorFlow, and use it to build a simple neural network for image classification.
In this module, you will get an introduction to Computer Vision using TensorFlow. We'll use image classification to learn about convolutional neural networks, and then see how pre-trained networks and transfer learning can improve our models and solve real-world problems.
In this module, we'll explore different neural network architectures for processing natural language texts. Natural Language Processing (NLP) has experienced fast growth and advancement primarily because the performance of the language models depends on their overall ability to "understand" text and can be trained using an unsupervised technique on large text corpora. Additionally, pre-trained text models (such as BERT) simplified many NLP tasks and has dramatically improved the performance. We'll learn more about these techniques and the basics of NLP in this learning module.
In this learn module we will be learning how to do audio classification with TensorFlow. There are multiple ways to build an audio classification model. You can use the waveform, tag sections of a wave file, or even use computer vision on the spectrogram image. In this tutorial we will first break down how to understand audio data, from analog to digital representations, then we will build the model using computer vision on the spectrogram images. That's right, you can turn audio into an image representation and then do computer vision to classify the word spoken!
If you've completed the first module and realized that you need extra flexibility to build or debug your model, then this module is for you. We'll show how you can create a simple neural network for image classification, but this time we'll use lower-level TensorFlow code and explain the foundational concepts needed to understand it.