CS 20SI: Tensorflow for Deep Learning Research-斯坦福大学 TensorFlow 深度学习研究课程

Tensorflow是Google Brain研究人员开发的强大的机器学习开源软件库。它具有许多预构建的功能,以缓解构建不同神经网络的任务。 Tensorflow允许在不同的计算机以及单个机器中的多个CPU和GPU之间分配计算。 TensorFlow提供了一个Python API,以及较少记录的C ++ API。对于本课程,我们将使用Python。

本课程将涵盖Tensorflow 深度学习的基础库和当代应用。我们的目标是帮助学生了解Tensorflow的图形计算模型,探索其提供的功能,并学习如何构建和构建最适合深度学习项目的模型。通过课程,学生将使用Tensorflow构建不同复杂性的模型,从简单的线性/逻辑回归到卷积神经网络和具有LSTM的循环神经网络来解决诸如字嵌入,翻译,光学字符识别等任务。学生还将学习构建模型并管理研究实验的最佳实践。

Course Description
Tensorflow is a powerful open-source software library for machine learning developed by researchers at Google Brain. It has many pre-built functions to ease the task of building different neural networks. Tensorflow allows distribution of computation across different computers, as well as multiple CPUs and GPUs within a single machine. TensorFlow provides a Python API, as well as a less documented C++ API. For this course, we will be using Python.

This course will cover the fundamentals and contemporary usage of the Tensorflow library for deep learning research. We aim to help students understand the graphical computational model of Tensorflow, explore the functions it has to offer, and learn how to build and structure models best suited for a deep learning project. Through the course, students will use Tensorflow to build models of different complexity, from simple linear/logistic regression to convolutional neural network and recurrent neural networks with LSTM to solve tasks such as word embeddings, translation, optical character recognition. Students will also learn best practices to structure a model and manage research experiments.


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