機器學習是人工智能的一個分支，其中計算機具有學習能力而無需明確編程。深度學習是機器學習的一個子領域，它使用基於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，以其清晰的語法和代碼可讀性而聞名。
在這個以講師為主導的現場培訓中，參與者將學習如何使用Python實施深度學習銀行模型，同時逐步創建深度學習信用風險模型。
在培訓結束時，參與者將能夠：
- 理解深度學習的基本概念
- 了解深度學習在銀行業務中的應用和用途
- 使用Python ， Keras和TensorFlow為銀行業務創建深度學習模型
- 使用Python構建自己的深度學習信用風險模型
聽眾
課程形式
Machine Translated
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
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
- 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
Summary and Conclusion
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