Description

Introduction

Deep learning is revolutionizing industries by enabling machines to recognize patterns, automate complex tasks, and make data-driven decisions with minimal human intervention. The KNIME Analytics Platform provides a flexible, low-code environment for developing deep learning models, integrating powerful machine learning frameworks such as TensorFlow and Keras.

This training equips participants with the knowledge and hands-on experience required to build, train, optimize, and deploy deep learning models using KNIME. Through interactive exercises and real-world case studies, learners will explore key deep learning architectures, including feedforward neural networks, convolutional neural networks (CNNs), and recurrent neural networks (RNNs/LSTMs). The course also covers data preprocessing, model evaluation, and best practices for deploying deep learning solutions within KNIME.

Prerequisites

• Basic knowledge of machine learning concepts.
• Familiarity with the KNIME Analytics Platform (recommended).
• Understanding of Python and deep learning frameworks (optional but helpful).

Table of Contents

1. Introduction to Deep Learning in KNIME

1.1 Overview of deep learning and its applications
1.2 How KNIME integrates with deep learning frameworks
1.3 Setting up the KNIME Deep Learning extension
1.4 Overview of key deep learning architectures

2. Data Preparation for Deep Learning

2.1 Data collection and preprocessing techniques
2.2 Handling missing values and outliers
2.3 Feature scaling, normalization, and encoding
2.4 Splitting data into training, validation, and test sets

3. Fundamentals of Neural Networks

3.1 Understanding perceptrons and activation functions
3.2 Feedforward networks and backpropagation
3.3 Hyperparameter tuning: learning rate, epochs, and batch size
3.4 Avoiding overfitting with regularization techniques

4. Building Deep Learning Models in KNIME

4.1 Creating neural networks using KNIME Keras nodes(Ref: L2: Advanced Proficiency in KNIME Analytics Platform)
4.2 Configuring layers, activation functions, and loss functions
4.3 Selecting optimizers: Adam, SGD, RMSprop
4.4 Training and evaluating a simple deep learning model

5. Convolutional Neural Networks (CNNs) for Image Processing

5.1 Introduction to CNNs and their applications
5.2 Designing CNN architectures in KNIME
5.3 Training and fine-tuning CNN models
5.4 Transfer learning and model reuse in KNIME

6. Recurrent Neural Networks (RNNs) and LSTMs for Sequential Data

6.1 Understanding sequential data and time series forecasting
6.2 Implementing RNNs and LSTMs in KNIME
6.3 Handling text data for NLP applications
6.4 Evaluating sequence-based models

7. Autoencoders and Anomaly Detection

7.1 Concept of autoencoders for feature extraction
7.2 Training and fine-tuning autoencoders in KNIME
7.3 Applications in fraud detection and anomaly detection
7.4 Performance evaluation and interpretation

8. Model Deployment and Integration

8.1 Exporting trained deep learning models
8.2 Deploying models using KNIME Server
8.3 Integration with APIs and cloud services
8.4 Automating deep learning workflows

9. Real-World Use Cases and Case Studies

9.1 Image classification with CNNs
9.2 Time series forecasting with LSTMs
9.3 Fraud detection using autoencoders
9.4 NLP-based sentiment analysis

Conclusion

This training provides equipping participants with the skills to develop, train, and deploy neural networks for various applications. By leveraging KNIME’s deep learning extensions and seamless integration with TensorFlow and Keras, learners will be able to build scalable, production-ready AI solutions with minimal coding. Whether working on image recognition, time series analysis, or NLP, this course enables professionals to harness the full potential.

Reference