• Duración:
    7 semanas
  • Dedicación:
    4–6 horas por semana
  • Precio:
    $799 USD
  • Institución
  • Tema:
  • Nivel:
    Intermediate
  • Idioma:
    English
  • Transcripción de video:
    English
  • Tipo de curso:
    A tu ritmo

Programas asociados:

Prerrequisitos

The course is intended for financial professionals (analysts, portfolio managers, traders, quants, advisers) and other practitioners with an interest in finance. Solid programming skills are advised; knowledge of Python is an advantage. Students should also have knowledge of basic probability, statistical techniques (including linear regression), calculus; linear algebra. A background (perhaps through the first course of this series) in Classical Machine Learning is helpful but not mandatory.

Sobre este curso

Omitir Sobre este curso

Deep Learning ventures into territory associated with Artificial Intelligence. This course will demonstrate how neural networks can improve practice in various disciplines, with examples drawn primarily from financial engineering. Students will gain an understanding of deep learning techniques, including how alternate data sources such as images and text can advance practice within finance.

Lo que aprenderás

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By the end of this course students should be able to:

  • utilize neural network and deep learning techniques and apply them in many domains, including Finance

  • make predictions based on financial data

  • use alternate data sources such as images and text and associated techniques such as image recognition and natural language processing for prediction

  • use these techniques and data for

    • optimizing portfolios and portfolio management

    • managing risk

    • streamlining operations

Plan de estudios

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Week 0: Classical Machine Learning: Overview

  • Guided entry for students who have not taken the first course in the series

  • Notational conventions

  • Basic ideas: linear regression, classification

  • Recipe for Machine Learning

Week 1: Introduction to Neural Networks and Deep Learning

  • Neural Networks Overview

  • Coding Neural Networks: Tensorflow, Keras

  • Practical Colab

Week 2 : Convolutional Neural Networks

  • A neural network is a Universal Function Approximator

  • Convolutional Neural Networks (CNN): Introduction

  • CNN: Multiple input/output features

  • CNN: Space and time

Week 3: Recurrent Neural Networks

  • Recurrent Neural Networks (RNN): Introduction

  • RNN Overview

  • Generating text with an RNN

Week 4: Training Neural Networks

  • Back propagation

  • Vanishing and exploding gradients

  • Initializing and maintaining weights

  • Improving trainability

  • How big should my Neural Network be ?

Week 5: Interpretation and Transfer Learning

  • Interpretation: Preview

  • Transfer Learning

  • Tensors, Matrix Gradients

Week 6: Advanced Recurrent Architectures

  • Gradients of an RNN

  • RNN Gradients that vanish and explode

  • Residual connections

  • Neural Programming

  • LSTM

  • Attention: introduction

Week 7: Advanced topics

  • Neural Language Processing (NLP)

  • Interpretation: what is going on inside a Neural Network

  • Attention

  • Adversarial examples

  • Final words

Conoce a tus instructores

Ken Perry
Adjunct Professor
New York University Tandon School of Engineering

¿Quién puede hacer este curso?

Lamentablemente, las personas de uno o más de los siguientes países o regiones no podrán registrarse para este curso: Irán, Cuba y la región de Crimea en Ucrania. Si bien edX consiguió licencias de la Oficina de Control de Activos Extranjeros de los EE. UU. (U.S. Office of Foreign Assets Control, OFAC) para ofrecer nuestros cursos a personas en estos países y regiones, las licencias que hemos recibido no son lo suficientemente amplias como para permitirnos dictar este curso en todas las ubicaciones. edX lamenta profundamente que las sanciones estadounidenses impidan que ofrezcamos todos nuestros cursos a cualquier persona, sin importar dónde viva.