Cursos de Deep Learning for Finance (with Python)

Código do Curso

dlforfinancewithpython

Duração

28 horas (usualmente 4 dias incluindo pausas)

Requisitos

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

Visão geral

O aprendizado de máquina é um ramo da inteligência artificial em que os computadores têm a capacidade de aprender sem serem explicitamente programados. O aprendizado profundo é um subcampo do aprendizado de máquina que utiliza métodos baseados em representações e estruturas de dados de aprendizado, como redes neurais. Python é uma linguagem de programação de alto nível famosa por sua clara sintaxe e legibilidade do código.

Neste treinamento ao vivo, conduzido por instrutor, os participantes aprenderão como implementar modelos de aprendizado profundo para finanças usando o Python medida que avançam na criação de um modelo de previsão de preço de ações de aprendizado profundo.

Ao final deste treinamento, os participantes serão capazes de:

  • Compreender os conceitos fundamentais da aprendizagem profunda
  • Aprenda as aplicações e usos do aprendizado profundo em finanças
  • Use Python , Keras e TensorFlow para criar modelos de aprendizado profundo para finanças
  • Crie seu próprio modelo de previsão de preço das ações de aprendizado profundo usando Python

Público

  • Desenvolvedores
  • Cientistas de dados

Formato do curso

  • Parte palestra, parte discussão, exercícios e prática prática pesada

Machine Translated

Programa do Curso

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 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

Declaração de Clientes

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★★★★★

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