Deep Learning For Finance (with R)

Langue : UKRéférence : DLFDurée : 4 jours
Formation à distance ou en vos locaux.Prix : A définir
Date non disponible actuellement :

Cette formation est organisée uniquement à la demande d'un client et sera adaptée à ses besoins spécifiques.
Le coût de celle-ci sera donc déterminé par un devis personnalisé (avec This email address is being protected from spambots. You need JavaScript enabled to view it. ou This email address is being protected from spambots. You need JavaScript enabled to view it. au 53 28 20 1).

Pendant la pandémie, le Telindus Training Institute n'effectue ses formations qu'au travers de la formation à distance ou en vos locaux.
Ainsi, un grand nombre de nos formations propose une réduction tarifaire.
Veuillez nous contacter pour plus de renseignements. This email address is being protected from spambots. You need JavaScript enabled to view it.

Contenu :

Machine learning is a branch of Artificial Intelligence wherein computers have the ability to learn without being explicitly programmed. Deep learning is a subfield of machine learning which uses methods based on learning data representations and structures such as neural networks. Python is a high-level programming language famous for its clear syntax and code readability.

In this instructor-led, live training, participants will learn how to implement deep learning models for finance using Python as they step through the creation of a deep learning stock price prediction model.

By the end of this training, participants will be able to:
- Understand the fundamental concepts of deep learning
- Learn the applications and uses of deep learning in finance
- Use Python, Keras, and TensorFlow to create deep learning models for finance
- Build their own deep learning stock price prediction model using Python

Format of the course:
Part lecture, part discussion, exercises and heavy hands-on practice

Course Outline:


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

Pré-requis :

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

- Developers
- Data scientists

Telindus Training Institute utilise des cookies pour améliorer l'expérience client et l'utilisation de son site. En continuant à surfer sur, vous acceptez les conditions d’utilisation de ces cookies.