Deep Learning for Banking (using Python) Training Course

From: $7,000.00


Deep Learning for Banking (using Python) Training Course

Duration: 28h block (4x7h - 4 working days)
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 banking using Python as they step through the creation of a deep learning credit risk 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 banking
  • Use Python, Keras, and TensorFlow to create deep learning models for banking
  • Build their own deep learning credit risk model using Python
  • Developers
  • Data scientists
Format of the course
  • Part lecture, part discussion, exercises and heavy hands-on practice

 Course Outline

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 Banking
  • Neural Networks
  • Natural Language Processing
  • Image Recognition
  • Speech Recognition
  • Sentimental Analysis
Exploring Deep Learning Case Studies for Banking
  • Anti-Money Laundering Programs
  • Know-Your-Customer (KYC) Checks
  • Sanctions List Monitoring
  • Billing Fraud Oversight
  • Risk Management
  • Fraud Detection
  • Product and Customer Segmentation
  • Performance Evaluation
  • General Compliance Functions
Understanding the Benefits of Deep Learning for Banking 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 Banking
  • 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 Credit Risk Model 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

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