Deep Learning for Finance (with R)培训

课程编码

dlfinancewithr

课程时长

28 小时 通常来说是4天,包括中间休息。

要求

  • Experience with R programming
  • General familiarity with finance concepts
  • Basic familiarity with statistics and mathematical concepts

课程概览

机器学习是人工智能的一个分支,其中计算机具有学习能力而无需明确编程。深度学习是机器学习的一个子领域,它使用基于学习数据表示和结构(如神经网络)的方法。 R是金融行业中流行的编程语言。它用于从核心交易程序到风险管理系统的金融应用程序。

在这个以讲师为主导的现场培训中,参与者将学习如何使用R实施深度学习模型,因为他们逐步创建深度学习股票价格预测模型。

在培训结束时,参与者将能够:

  • 理解深度学习的基本概念
  • 了解深度学习在金融领域的应用和用途
  • 使用R为财务创建深度学习模型
  • 使用R建立自己的深度学习股票价格预测模型

听众

  • 开发商
  • 数据科学家

课程形式

  • 部分讲座,部分讨论,练习和繁重的实践练习

Machine Translated

课程大纲

Introduction

Understanding the Fundamentals of Artificial Intelligence and Machine Learning

Understanding Deep Learning

  • Overview of the Basic Concepts of Deep Learning
  • Differentiating Between Machine Learning and Deep Learning
  • Overview of Applications for Deep Learning

Overview of Neural Networks

  • What are Neural Networks
  • Neural Networks vs Regression Models
  • Understanding Mathematical Foundations and Learning Mechanisms
  • Constructing an Artificial Neural Network
  • Understanding Neural Nodes and Connections
  • Working with Neurons, Layers, and Input and Output Data
  • Understanding Single Layer Perceptrons
  • Differences Between Supervised and Unsupervised Learning
  • Learning Feedforward and Feedback Neural Networks
  • Understanding Forward Propagation and Back Propagation
  • Understanding Long Short-Term Memory (LSTM)
  • Exploring Recurrent Neural Networks in Practice
  • Exploring Convolutional Neural Networks in practice
  • Improving the Way Neural Networks Learn

Overview of Deep Learning Techniques Used in Finance

  • Neural Networks
  • Natural Language Processing
  • Image Recognition
  • Speech Recognition
  • Sentimental Analysis

Exploring Deep Learning Case Studies for Finance

  • Pricing
  • Portfolio Construction
  • Risk Management
  • High Frequency Trading
  • Return Prediction

Understanding the Benefits of Deep Learning for Finance

Exploring the Different Deep Learning Packages for R

Deep Learning in R with Keras and RStudio

  • Overview of the Keras Package for R
  • Installing the Keras Package for R
  • Loading the Data
    • Using Built-in Datasets
    • Using Data from Files
    • Using Dummy Data
  • Exploring the Data
  • Preprocessing the Data
    • Cleaning the Data
    • Normalizing the Data
    • Splitting the Data into Training and Test Sets
  • Implementing One Hot Encoding (OHE)
  • Defining the Architecture of Your Model
  • Compiling and Fitting Your Model to the Data
  • Training Your Model
  • Visualizing the Model Training History
  • Using Your Model to Predict Labels of New Data
  • Evaluating Your Model
  • Fine-Tuning Your Model
  • Saving and Exporting Your Model

Hands-on: Building a Deep Learning Model for Stock Price Prediction Using R

Extending your Company's Capabilities

  • Developing Models in the Cloud
  • Using GPUs to Accelerate Deep Learning
  • Applying Deep Learning Neural Networks for Computer Vision, Voice Recognition, and Text Analysis

Summary and Conclusion

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