Deep Learning for Finance (with Python)培訓




28 時間: 同常來說包括休息是 4天


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


機器學習是人工智能的一個分支,其中計算機有能力學習而不被明確編程。深度學習是機器學習的一個子領域,它使用基于學習數據表示和結構(如神經網絡)的方法。 Python是一種高級編程語言,因其清晰的語法和代碼可讀性而聞名。 在這種有指導性的現場培訓中,參與者將學習如何通過創建深度學習股票價格預測模型來實現使用Python進行融資的深度學習模型。 在培訓結束後,參與者將能夠: 理解深度學習的基本概念了解金融深度學習的應用和使用使用Python,Keras和TensorFlow爲財務創建深度學習模型使用Python構建自己的深度學習股票價格預測模型 聽衆 開發商數據科學家 課程的格式 部分講座,部分討論,練習和沈重的練習

Machine Translated



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 Libraries for Python

  • TensorFlow
  • Keras

Setting Up Python with the TensorFlow for Deep Learning

  • Installing the TensorFlow Python API
  • Testing the TensorFlow Installation
  • Setting Up TensorFlow for Development
  • Training Your First TensorFlow Neural Net Model

Setting Up Python with Keras for Deep Learning

Building Simple Deep Learning Models with Keras

  • Creating a Keras Model
  • Understanding Your Data
  • Specifying Your Deep Learning Model
  • Compiling Your Model
  • Fitting Your Model
  • Working with Your Classification Data
  • Working with Classification Models
  • Using Your Models

Working with TensorFlow for Deep Learning for Finance

  • Preparing the Data
    • Downloading the Data
    • Preparing Training Data
    • Preparing Test Data
    • Scaling Inputs
    • Using Placeholders and Variables
  • Specifying the Network Architecture
  • Using the Cost Function
  • Using the Optimizer
  • Using Initializers
  • Fitting the Neural Network
  • Building the Graph
    • Inference
    • Loss
    • Training
  • Training the Model
    • The Graph
    • The Session
    • Train Loop
  • Evaluating the Model
    • Building the Eval Graph
    • Evaluating with Eval Output
  • Training Models at Scale
  • Visualizing and Evaluating Models with TensorBoard

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

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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