Dive into Deep Learning with Scala

Übersicht

Explore deep learning fundamentals and implementation in Scala, covering neural networks, computer vision, and practical applications with hands-on examples and library comparisons.

Lehrplan

• Introduction to Deep Learning
Overview of Artificial Intelligence and Machine Learning
Evolution and significance of Deep Learning
Role of Scala in Deep Learning
• Basics of Neural Networks
Anatomy of a Neural Network
Activation Functions
Loss Functions and Optimization Algorithms
• Setting Up the Scala Environment
Installation and configuration of Scala
Overview of popular Scala libraries for Deep Learning (Breeze, DeepLearning.scala, Spark MLlib)
• Core Deep Learning Concepts
Multilayer Perceptrons
Backpropagation and Gradient Descent
Regularization Techniques
• Convolutional Neural Networks (CNNs)
Architecture and components of CNNs
CNNs for Image Classification and Object Detection
Practical implementation of CNNs in Scala
• Recurrent Neural Networks (RNNs)
Fundamentals of RNNs and LSTMs
Applications of RNNs in Time Series and NLP
Building RNN models with Scala
• Deep Learning Model Evaluation
Metrics for assessing model performance
Validation and Testing Strategies
Hyperparameter Tuning
• Practical Applications and Case Studies
Case Study: Implementing a Computer Vision task with Scala
Hands-on example: Building a Sentiment Analysis model
Comparison of Deep Learning Libraries in Scala
• Advanced Topics
Transfer Learning and Pretrained Models
Generative Adversarial Networks (GANs)
Ethical considerations in AI and Deep Learning
• Capstone Project
Develop and present a deep learning project using Scala
Peer review and feedback session
• Course Conclusion
Summary of key concepts
Future learning paths in Deep Learning and Scala

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Fachgebiete

Conference Talks