Deep Learning
[AI 2]
Full Course
课程套餐
课程介绍:
学习最新的监督式学习技巧， 该方法应用于面部识别、语音识别和自动驾驶汽车等常见应用 。本课程还将为学生提供具有GPU加速功能的Linux服务器来运行算法。主题包括回归，测试分类，卷积图像识别等等。
特点：
- 本课程为学生提供专业研究级硬件，包括计算服务器， 如具有128GB随机访问存储器的16核服务器， 数TB的快速存储和研究级图形处理器, 如Titan X Pascal GPU.
- 本课程还会教授学生如何使用linux工具和CUDA + GPU 加速Python研究环境， 使用Tensorflow-GPU 1.4, Keras 2.1.4等使用lubcudnn 6和7编译的工具。
- 课程材料来自最近2-5年发表的最新学术研究， 包括深度网络，单发检测，卷积或矢量化语言模型， 以及（如果时间允许）一些项目展示， 比如AlphaZero Go和GAN引发的序列到序列学习.
本课程不再使用Theano，学生将主要使用Tensorflow支持的Keras深度学习库进行建模。Research from KTBYTE students and alumni
入班要求:
修完[CORE 5b]或AP计算机科学，或获得导师同意。还需掌握代数II数学。建议修[AI 1]但不强制。
相关课程
项目示例
These are examples of projects that students create as they grow their skills in [AI 2]
Syllabus
Introduction to Neural Networks
In this class we'll learn about the Perceptron as the building block for neural networks and deep learning
Introduction to TensorFlow
In this class we'll start exploring how to use the TensorFlow library.
More TensorFlow, Intro to Keras
In this class we'll keep working with TensorFlow and start learning to use Keras.
Working with Images
In this lesson we'll start learning how to process images with our ML architectures, including running a model for image ID or generation.
Convolutional Neural Networks (CNN)
Today we'll start exploring a new type of neural network and learn about regularizations
Transfer Learning
Transfer learning allows us to copy effective parts of existing models. Today we will also introduce the midterm project.
Midterm Project: Image Classification
Project Day
Finish Midterm Project, presentations
Students will present their work from the midterm project. Class discussion on topics to cover in Unit 2 in order to meet student goals.
Recurrent Neural Networks (RNNs)
RNNs can be used to make predictions about time series data, like words in a sentence. Topic may change depending on student goals for Unit 2.
所有课程时间
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Summer Semester: Twice Per Week
** KTBYTE可以自行决定更改目前安排的授课老师
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