~ Technology tutorials and blogs written by GPT Tutor ~
~ Technology tutorials and blogs written by GPT Tutor ~
🧩 Part 8/8
✔ What is Pruning and Why is it Important?
✔ Pruning Methods
✔ Pruning Criteria
✔ Pruning Recurrent Neural Networks
✔ Challenges and Benefits
✔ Pruning Techniques for RNNs
✔ Experimental Results and Analysis
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🧩 Part 7/8
✔ What is Pruning and Why is it Important?
✔ Pruning Strategies for Convolutional Neural Networks
✔ Filter Pruning
✔ Channel Pruning
✔ Layer Pruning
✔ Pruning Algorithms and Tools
✔ Magnitude-based Pruning
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🧩 Part 10/10
✔ What is Quantum Machine Learning?
✔ Quantum Computing Basics
✔ Quantum Algorithms for Machine Learning
✔ What is TensorFlow Quantum?
✔ TensorFlow Quantum Architecture
✔ TensorFlow Quantum Features
✔ How to Install and Use TensorFlow Quantum?
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🧩 Part 9/10
✔ What is Quantum Machine Learning?
✔ Quantum Computing Basics
✔ Quantum Machine Learning Algorithms
✔ What is PennyLane?
✔ PennyLane Features
✔ PennyLane Installation and Setup
✔ Quantum Machine Learning with PennyLane
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🧩 Part 8/10
✔ Quantum Machine Learning Concepts
✔ Quantum Computing Basics
✔ Quantum Machine Learning Models
✔ Quantum Machine Learning Algorithms
✔ Qiskit Overview
✔ Qiskit Elements
✔ Qiskit Installation and Setup
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🧩 Part 7/10
✔ Quantum Computing Basics
✔ Qubits and Quantum Gates
✔ Quantum Circuits and Algorithms
✔ Python for Quantum Computing
✔ Installing and Using Python
✔ Python Libraries for Quantum Computing
✔ Quantum Machine Learning Concepts
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🧩 Part 8/8
✔ Summary of the Tutorial Series
✔ F1 Score: Definition and Interpretation
✔ F1 Score: Calculation and Optimization
✔ F1 Score: Applications and Limitations
✔ Future Work and Recommendations
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🧩 Part 7/8
✔ What is F1 Score and Why is it Important?
✔ What is Feature Selection and How Does it Help?
✔ Common Feature Selection Methods
✔ Filter Methods
✔ Wrapper Methods
✔ Embedded Methods
✔ Comparing Feature Selection Methods on a Sample Dataset
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🧩 Part 12/12
✔ Active Learning: Definition, Benefits, and Challenges
✔ Active Learning Research Trends and Open Problems
✔ Query Strategies and Sampling Methods
✔ Active Learning for Complex and Structured Data
✔ Active Learning with Human-in-the-Loop and Explainability
✔ Active Learning Resources: Datasets, Tools, and Papers
✔ Datasets for Active Learning Experiments and Benchmarks
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🧩 Part 11/12
✔ What is Active Learning and Why is it Useful?
✔ Active Learning Challenges
✔ Labeling Cost
✔ Labeling Quality
✔ Cold Start
✔ Diversity
✔ Active Learning Limitations
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🧩 Part 10/12
✔ Background and Related Work
✔ Active Learning
✔ Meta-Learning
✔ Learning to Learn
✔ Methodology
✔ Problem Formulation
✔ Active Learning for Meta-Learning Algorithm
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🧩 Part 8/8
✔ Reinforcement Learning Basics
✔ Bayesian Methods for Reinforcement Learning
✔ Bayesian Exploration-Exploitation Trade-off
✔ Bayesian Optimization for Hyperparameter Tuning
✔ Thompson Sampling for Bandit Problems
✔ Applications and Examples
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🧩 Part 7/8
✔ What are Generative Adversarial Networks?
✔ The Generator
✔ The Discriminator
✔ The Adversarial Loss
✔ Applications of Generative Adversarial Networks
✔ Image Generation
✔ Data Augmentation
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🧩 Part 6/8
✔ Probabilistic Modeling and Density Estimation
✔ Normalizing Flows: Definition and Properties
✔ Change of Variables Formula and Invertible Transformations
✔ Types of Normalizing Flows and Examples
✔ Applications of Normalizing Flows in Deep Learning
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🧩 Part 5/8
✔ What are Variational Autoencoders?
✔ The Encoder-Decoder Architecture
✔ The Latent Variable Model
✔ The Reparameterization Trick
✔ How to Train Variational Autoencoders?
✔ The Evidence Lower Bound
✔ The Kullback-Leibler Divergence
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🧩 Part 4/8
✔ Bayesian Inference and Variational Inference
✔ Bayesian Inference
✔ Variational Inference
✔ Variational Lower Bound and Kullback-Leibler Divergence
✔ Variational Lower Bound
✔ Kullback-Leibler Divergence
✔ Mean-Field Approximation and Coordinate Ascent
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🧩 Part 12/12
✔ What is Predictive Maintenance and Why is it Important?
✔ How Machine Learning Enables Predictive Maintenance
✔ Case Study 1: Predicting Aircraft Engine Failures
✔ Case Study 2: Optimizing Wind Turbine Maintenance
✔ Case Study 3: Improving Manufacturing Quality and Efficiency
✔ Case Study 4: Enhancing Railway Safety and Reliability
✔ Challenges and Opportunities for Predictive Maintenance with Machine Learning
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🧩 Part 11/12
✔ What is Predictive Maintenance?
✔ Why Use Machine Learning for Predictive Maintenance?
✔ Challenges of Predictive Maintenance with Machine Learning
✔ Data Quality and Availability
✔ Model Selection and Evaluation
✔ Deployment and Maintenance
✔ Best Practices for Predictive Maintenance with Machine Learning
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🧩 Part 10/12
✔ Predictive Maintenance with Machine Learning: Concepts and Challenges
✔ What is Predictive Maintenance and Why is it Important?
✔ How Machine Learning Can Enhance Predictive Maintenance?
✔ What are the Main Challenges of Applying Machine Learning to Predictive Maintenance?
✔ Deployment of Machine Learning Models for Predictive Maintenance
✔ Choosing the Right Deployment Strategy and Platform
✔ Preparing the Data and the Model for Deployment
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🧩 Part 8/8
✔ What is Model Deployment and Integration?
✔ Why Use Matlab for Machine Learning Model Deployment and Integration?
✔ How to Deploy and Integrate Machine Learning Models Using Matlab
✔ MATLAB Compiler
✔ MATLAB Production Server
✔ MATLAB Coder
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🧩 Part 7/8
✔ What is Model Optimization and Validation?
✔ Model Optimization
✔ Model Validation
✔ How to Optimize and Validate Machine Learning Models in Matlab?
✔ Cross-Validation
✔ Grid Search
✔ Performance Metrics
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🧩 Part 8/8
✔ Summary of the AWS AutoML Project
✔ Evaluation of the Machine Learning Model
✔ Performance Metrics and Confusion Matrix
✔ Feature Importance and Partial Dependence Plots
✔ Recommendations for Future Work
✔ Data Collection and Preprocessing
✔ Model Selection and Tuning
✔ Deployment and Monitoring
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🧩 Part 7/8
✔ Model Optimization with AWS AutoML
✔ Hyperparameter Tuning
✔ Feature Engineering
✔ Model Automation with AWS Step Functions
✔ Creating a State Machine
✔ Integrating AWS AutoML Services
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🧩 Part 6/8
✔ Why Model Monitoring and Maintenance is Important
✔ How AWS AutoML Supports Model Monitoring and Maintenance
✔ How to Set Up AWS SageMaker Model Monitor
✔ How to Monitor Model Performance and Detect Model Drift
✔ How to Update or Retrain Your Model Using AWS AutoML
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🧩 Part 5/8
✔ Creating a Model Endpoint
✔ Invoking the Model Endpoint
✔ Invoking from AWS Console
✔ Invoking from AWS CLI
✔ Invoking from Python SDK
✔ Monitoring the Model Endpoint
✔ Viewing Metrics and Logs
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🧩 Part 4/8
✔ Model Evaluation
✔ Model Metrics
✔ Model Performance
✔ Model Interpretation
✔ Feature Importance
✔ SHAP Values
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🧩 Part 12/12
✔ Ethical Challenges of Financial Machine Learning
✔ Privacy and Data Protection
✔ Security and Robustness
✔ Fairness and Bias
✔ Accountability and Transparency
✔ Regulatory Frameworks for Financial Machine Learning
✔ Global and Regional Initiatives
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🧩 Part 11/12
✔ What is Algorithmic Trading?
✔ Definition and Benefits
✔ Challenges and Risks
✔ How to Design Trading Strategies?
✔ Data Sources and Preprocessing
✔ Feature Engineering and Selection
✔ Trading Signal Generation and Optimization
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🧩 Part 10/12
✔ Portfolio Optimization: Concepts and Methods
✔ What is Portfolio Optimization?
✔ How to Measure Portfolio Performance?
✔ What are the Common Portfolio Optimization Models?
✔ Portfolio Optimization for Financial Machine Learning
✔ How to Apply Machine Learning to Portfolio Optimization?
✔ What are the Benefits and Challenges of Machine Learning for Portfolio Optimization?
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🧩 Part 10/10
✔ What is Robust Machine Learning and Why is it Important?
✔ Robust Machine Learning Applications in Image Processing
✔ Face Recognition and Verification
✔ Object Detection and Segmentation
✔ Robust Machine Learning Applications in Natural Language Processing
✔ Sentiment Analysis and Text Classification
✔ Machine Translation and Text Summarization
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🧩 Part 9/10
✔ Why Robust Model Evaluation and Validation Matters
✔ Common Metrics for Model Evaluation
✔ Accuracy, Precision, Recall, and F1-score
✔ Confusion Matrix, ROC Curve, and AUC
✔ Mean Squared Error, Root Mean Squared Error, and R-squared
✔ Techniques for Model Validation
✔ Train-Test Split
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🧩 Part 8/10
✔ What is Dimensionality Reduction and Why is it Important?
✔ Common Methods for Dimensionality Reduction
✔ Principal Component Analysis (PCA)
✔ t-Distributed Stochastic Neighbor Embedding (t-SNE)
✔ Uniform Manifold Approximation and Projection (UMAP)
✔ How to Choose the Best Method for Your Data
✔ How to Visualize the Reduced Data Using Python
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🧩 Part 7/10
✔ What is Robust Clustering and Why is it Important?
✔ How to Perform Robust Clustering using K-Means
✔ How to Perform Robust Clustering using DBSCAN
✔ What is Outlier Detection and Why is it Important?
✔ How to Perform Outlier Detection using Isolation Forest
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🧩 Part 12/12
✔ Sources and Types of Uncertainty in Machine Learning
✔ Aleatoric and Epistemic Uncertainty
✔ Model and Data Uncertainty
✔ Methods for Quantifying and Propagating Uncertainty
✔ Bayesian Methods
✔ Frequentist Methods
✔ Ensemble Methods
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🧩 Part 11/12
✔ What is uncertainty in data science and why does it matter?
✔ Sources and types of uncertainty
✔ Challenges and opportunities of uncertainty
✔ How can we measure and communicate uncertainty in data science?
✔ Quantitative methods and tools for uncertainty estimation
✔ Qualitative methods and tools for uncertainty communication
✔ What are the ethical and social implications of uncertainty in data science?
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🧩 Part 10/12
✔ What is Uncertainty and Why is it Important for Computer Vision?
✔ Sources and Types of Uncertainty
✔ Methods and Metrics for Quantifying and Evaluating Uncertainty
✔ Object Detection: A Key Task in Computer Vision
✔ Challenges and Opportunities of Uncertainty in Object Detection
✔ State-of-the-Art Approaches for Uncertainty-Aware Object Detection
✔ Face Recognition: Another Key Task in Computer Vision
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🧩 Part 9/12
✔ What is Uncertainty in Natural Language Processing?
✔ Sources and Types of Uncertainty
✔ Methods and Measures of Uncertainty
✔ Sentiment Analysis: A Task with High Uncertainty
✔ Challenges and Approaches of Sentiment Analysis
✔ Uncertainty Modeling and Evaluation in Sentiment Analysis
✔ Text Generation: A Task with High Creativity
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🧩 Part 8/12
✔ Uncertainty in Reinforcement Learning
✔ Sources and Types of Uncertainty
✔ Measures and Models of Uncertainty
✔ Exploration and Exploitation Trade-off
✔ Exploration Strategies
✔ Exploitation Strategies
✔ Balancing Exploration and Exploitation
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🧩 Part 7/12
✔ Sources and Types of Uncertainty in Deep Learning
✔ Aleatoric Uncertainty
✔ Epistemic Uncertainty
✔ Bayesian Neural Networks
✔ Bayesian Inference and Learning
✔ Variational Inference and Approximate Posterior
✔ Practical Methods for Bayesian Deep Learning
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🧩 Part 8/8
✔ What is Web Scraping and Why is it Useful?
✔ How to Use BeautifulSoup4 for Web Scraping in Python
✔ Installing and Importing BeautifulSoup4
✔ Parsing HTML with BeautifulSoup4
✔ Navigating and Extracting Data with BeautifulSoup4
✔ Best Practices for Web Scraping
✔ Respect the Robots.txt File
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🧩 Part 7/8
✔ Web Scraping Basics
✔ Data Cleaning with BeautifulSoup4
✔ Parsing HTML
✔ Extracting Data Elements
✔ Handling Missing Values
✔ Data Storage with Pandas
✔ Creating DataFrames
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🧩 Part 10/10
✔ Designing Data Factory Pipelines
✔ Use Parameters and Variables
✔ Use Linked Services and Datasets
✔ Use Naming Conventions and Annotations
✔ Optimizing Data Factory Performance
✔ Choose the Right Integration Runtime
✔ Use Parallel Execution and Partitioning
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🧩 Part 9/10
✔ Testing Data Pipelines
✔ Data Flow Debug Session
✔ Pipeline Validation
✔ Trigger Runs and Monitor Activity
✔ Deploying Data Pipelines
✔ Publish Changes to Data Factory
✔ Export and Import ARM Templates
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🧩 Part 8/10
✔ Azure Data Factory Security Features
✔ Role-Based Access Control (RBAC)
✔ Azure Key Vault Integration
✔ Data Encryption and Masking
✔ Azure Data Factory Management Features
✔ Monitoring and Alerting
✔ Data Flow Debugging and Testing
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🧩 Part 7/10
✔ Monitoring Data Pipelines in Azure Data Factory
✔ Monitoring Dashboard
✔ Monitoring Alerts
✔ Troubleshooting Data Pipelines in Azure Data Factory
✔ Troubleshooting Activity Runs
✔ Troubleshooting Pipeline Runs
✔ Troubleshooting Trigger Runs
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🧩 Part 6/10
✔ What is Azure Databricks?
✔ Features and Benefits of Azure Databricks
✔ How Azure Databricks Works with Azure Data Factory
✔ How to Create and Configure an Azure Databricks Linked Service in Azure Data Factory
✔ How to Use Azure Databricks Notebooks for Data Transformation in Azure Data Factory
✔ Creating and Running a Notebook in Azure Databricks
✔ Using Spark APIs for Data Transformation in a Notebook
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🧩 Part 5/10
✔ What is Wrangling Data Flow?
✔ How to Create a Wrangling Data Flow in Azure Data Factory
✔ How to Use the Spreadsheet-like Interface to Transform Data
✔ How to Write and Debug Power Query M Scripts
✔ How to Preview and Validate Data Transformation Results
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🧩 Part 4/10
✔ Prerequisites
✔ Creating a Mapping Data Flow
✔ Configuring the Source and Sink
✔ Adding and Editing Transformations
✔ Using the Expression Builder
✔ Using the Debug Mode
✔ Publishing and Executing the Data Flow
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🧩 Part 10/10
✔ Setting Up the Project
✔ Designing the API Schema
✔ Implementing the API Endpoints
✔ Using Path and Query Parameters
✔ Validating and Parsing Request Data
✔ Handling Errors and Exceptions
✔ Adding Authentication and Authorization
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🧩 Part 9/10
✔ What is FastAPI?
✔ What is Docker and why use it?
✔ Creating a Dockerfile for FastAPI
✔ Building and running the Docker image
✔ What is NGINX and why use it?
✔ Configuring NGINX as a reverse proxy
✔ What is Gunicorn and why use it?
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🧩 Part 8/10
✔ What is Asynchronous Programming?
✔ The Difference Between Synchronous and Asynchronous Code
✔ The Benefits of Asynchronous Code
✔ How to Use Async and Await in Python
✔ The async and await Keywords
✔ The asyncio Module
✔ The AsyncIO Event Loop
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🧩 Part 7/10
✔ What is FastAPI?
✔ What is a database?
✔ What is SQL?
✔ What is an ORM?
✔ What is CRUD?
✔ What is SQLAlchemy?
✔ How to set up a FastAPI project with SQLAlchemy?
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🧩 Part 8/8
✔ Why Closing the MongoDB Connection is Important
✔ How to Close the MongoDB Connection in Java
✔ Using the close() Method
✔ Using the try-with-resources Statement
✔ Best Practices for Closing the MongoDB Connection
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🧩 Part 7/8
✔ Setting Up the MongoDB Java Driver
✔ Connecting to a MongoDB Database and Collection
✔ Deleting a Single Document from a MongoDB Collection
✔ Using the deleteOne Method
✔ Using the Filters Class
✔ Handling the DeleteResult Object
✔ Deleting Multiple Documents from a MongoDB Collection
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🧩 Part 8/8
✔ How to publish Postman collections using web
✔ Create a public workspace
✔ Publish your collection to the workspace
✔ Share the collection link or embed code
✔ How to export Postman collections using web
✔ Select the collection to export
✔ Choose the export format and version
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🧩 Part 7/8
✔ What are Postman collections and why are they useful?
✔ How to create and manage Postman workspaces
✔ How to invite and join Postman teams
✔ How to share Postman collections with your team members
✔ How to collaborate on Postman collections using comments, forks, and merges
✔ How to sync Postman collections across devices and platforms
✔ How to use Postman collection runner and monitors for automation and testing
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🧩 Part 12/12
✔ Why Statistical Tests are Important for Machine Learning Evaluation
✔ Types of Statistical Tests for Model Comparison and Evaluation
✔ Parametric Tests
✔ Nonparametric Tests
✔ How to Choose the Appropriate Statistical Test for Your Data and Models
✔ How to Perform Statistical Tests in Python with Examples
✔ T-test for Comparing Two Models
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🧩 Part 11/12
✔ What is Bootstrap and Why is it Useful for Machine Learning?
✔ The Bootstrap Method
✔ Bootstrap Applications in Machine Learning
✔ How to Perform Bootstrap for Model Evaluation and Comparison
✔ Bootstrap Resampling
✔ Bootstrap Estimation
✔ Bootstrap Hypothesis Testing
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🧩 Part 10/12
✔ What is Cross-Validation and Why is it Important?
✔ The Bias-Variance Tradeoff
✔ The Overfitting and Underfitting Problem
✔ How to Perform Cross-Validation in Python
✔ The Scikit-Learn Library
✔ The KFold Class
✔ The cross_val_score Function
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🧩 Part 15/15
✔ Current Challenges of Deep Learning
✔ Data Quality and Availability
✔ Explainability and Interpretability
✔ Scalability and Efficiency
✔ Future Trends of Deep Learning
✔ Self-Supervised Learning
✔ Generative Adversarial Networks
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🧩 Part 14/15
✔ What is Meta-Learning?
✔ Types of Meta-Learning
✔ Benefits and Challenges of Meta-Learning
✔ What is Few-Shot Learning?
✔ Types of Few-Shot Learning
✔ Metrics and Benchmarks for Few-Shot Learning
✔ How to Implement Meta-Learning with TensorFlow
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🧩 Part 13/15
✔ What are Graphs and Graph Neural Networks?
✔ Graphs and their properties
✔ Graph Neural Networks and their applications
✔ How to Implement a Graph Neural Network with TensorFlow
✔ Installing and importing TensorFlow and other libraries
✔ Loading and preprocessing a graph dataset
✔ Defining and creating a graph convolution layer
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🧩 Part 12/15
✔ Transformer Model Architecture
✔ Encoder and Decoder
✔ Self-Attention and Multi-Head Attention
✔ Positional Encoding and Feed-Forward Network
✔ TensorFlow Implementation
✔ Building the Model
✔ Preparing the Data
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🧩 Part 11/15
✔ What is Attention Mechanism?
✔ Types of Attention Mechanism
✔ Benefits of Attention Mechanism
✔ How to Implement Attention Mechanism with TensorFlow
✔ Encoder-Decoder Architecture
✔ Attention Layer
✔ Attention Vector
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🧩 Part 10/15
✔ What is Transfer Learning?
✔ Types of Transfer Learning
✔ Benefits and Challenges of Transfer Learning
✔ How to Use Transfer Learning with TensorFlow
✔ Load and Preprocess the Data
✔ Choose a Pretrained Model
✔ Fine-Tune the Model
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🧩 Part 15/15
✔ What is Kafka Case Studies?
✔ How to Install and Use Kafka Case Studies with Python
✔ Case Study 1: Event-Driven Architecture with Kafka and Python
✔ Case Study 2: Microservices with Kafka and Python
✔ Case Study 3: Streaming Analytics with Kafka and Python
✔ Case Study 4: IoT with Kafka and Python
✔ How to Learn from Kafka Case Studies
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🧩 Part 14/15
✔ What are Kafka Advanced Features?
✔ How to Use Transactions and Idempotence in Kafka with Python
✔ How to Use Exactly-Once Semantics in Kafka with Python
✔ How to Use KSQL in Kafka with Python
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🧩 Part 13/15
✔ What is Kafka and Why Use It with Python?
✔ Kafka Best Practices for Performance and Reliability
✔ Partitioning
✔ Batching
✔ Compression
✔ Replication
✔ How to Implement Kafka Best Practices with Python
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🧩 Part 12/15
✔ What is Kafka Testing and Why Use It?
✔ How to Set Up Kafka Testing with Python
✔ How to Write Unit Tests with Kafka Testing and Pytest
✔ How to Write Integration Tests with Kafka Testing and Pytest
✔ How to Write Load Tests with Kafka Testing and Locust
✔ How to Analyze Test Results and Improve Your Code
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🧩 Part 11/15
✔ What are Kafka Metrics and Why are They Important?
✔ How to Collect Kafka Metrics with JMX and Python
✔ How to Store Kafka Metrics with Prometheus
✔ How to Visualize Kafka Metrics with Grafana
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🧩 Part 10/15
✔ What is Kafka Security and Why You Need It
✔ How to Set Up Kafka Security with Python
✔ Generating SSL Certificates and Keys
✔ Configuring Kafka Brokers and Clients for SSL
✔ Using SASL for Authentication
✔ Using ACLs for Authorization
✔ How to Test and Monitor Kafka Security with Python
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🧩 Part 9/15
✔ What is Kafka Admin API?
✔ How to Install and Import Kafka Admin API in Python
✔ How to Create a Kafka Admin Client Object
✔ How to Use Kafka Admin API Methods and Parameters
✔ How to Create and Delete Topics
✔ How to Alter and Describe Configurations
✔ How to Describe Cluster Status and Metadata
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🧩 Part 8/15
✔ What is Kafka REST Proxy?
✔ How to Install and Run Kafka REST Proxy
✔ How to Use Python to Interact with Kafka REST Proxy
✔ Producing Messages to Kafka Topics
✔ Consuming Messages from Kafka Topics
✔ Managing Kafka Topics
✔ Querying Kafka Metadata
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🧩 Part 7/15
✔ What is Kafka Schema Registry?
✔ How to Install and Run Kafka Schema Registry
✔ How to Use Kafka Schema Registry with Python
✔ How to Produce and Consume Data with Avro Schema
✔ How to Produce and Consume Data with JSON Schema
✔ How to Produce and Consume Data with Protobuf Schema
✔ How to Perform Schema Evolution and Compatibility Checks
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🧩 Part 8/8
✔ Transformer Architecture and Memory Bottleneck
✔ Encoder-Decoder Structure
✔ Self-Attention Mechanism
✔ Memory Complexity and Limitations
✔ Reformer: The Efficient Transformer
✔ Locality-Sensitive Hashing for Approximate Attention
✔ Reversible Residual Layers for Reduced Memory Footprint
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🧩 Part 7/8
✔ Transformer Architecture and BERT
✔ Transformer Encoder and Decoder
✔ BERT Model and Pre-training
✔ ALBERT and Parameter Reduction Techniques
✔ Factorized Embedding Parameterization
✔ Cross-Layer Parameter Sharing
✔ ALBERT and Training Speed Improvement
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🧩 Part 10/10
✔ Best Practices for Implementing Embedded Machine Learning
✔ Model Selection and Optimization
✔ Memory and Power Constraints
✔ Edge Device Deployment
✔ Tips for Efficient Embedded Machine Learning
✔ Quantization Techniques
✔ Pruning and Compression
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🧩 Part 9/10
✔ Threats to Machine Learning Models on Embedded Devices
✔ Model Security
✔ Model Protection
✔ Authentication Mechanisms
✔ Role of Authentication in Model Security
✔ Implementing Secure Authentication
✔ Encryption Techniques for Model Protection
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🧩 Part 8/10
✔ Model Monitoring
✔ Logging for Model Monitoring
✔ Profiling Techniques
✔ Model Debugging
✔ Visualizing Model Behavior
✔ Debugging Inference Errors
✔ Real-world Challenges
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🧩 Part 7/10
✔ Model Deployment Methods
✔ Over-the-Air (OTA) Updates
✔ Firmware Flashing
✔ Model Compression Techniques
✔ Quantization
✔ Pruning
✔ Model Encryption
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🧩 Part 15/15
✔ What You Have Learned
✔ Data Structures
✔ Algorithms
✔ How to Apply Your Knowledge
✔ More Resources to Learn Java
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🧩 Part 14/15
✔ What are Divide and Conquer Algorithms?
✔ The Basic Idea of Divide and Conquer
✔ The Benefits and Challenges of Divide and Conquer
✔ How to Implement Divide and Conquer Algorithms in Java?
✔ The General Steps of Divide and Conquer
✔ The Recursive Method and the Base Case
✔ Merge Sort: A Classic Example of Divide and Conquer
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🧩 Part 13/15
✔ What are Greedy Algorithms?
✔ Greedy Algorithm Examples
✔ Advantages and Disadvantages of Greedy Algorithms
✔ What are Approximation Algorithms?
✔ Approximation Algorithm Examples
✔ Performance Guarantees and Approximation Ratios
✔ How to Implement Greedy and Approximation Algorithms in Java
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🧩 Part 12/15
✔ What is Dynamic Programming?
✔ The Principle of Optimality
✔ The Characteristics of Dynamic Programming Problems
✔ What is Memoization?
✔ The Benefits of Memoization
✔ The Drawbacks of Memoization
✔ How to Implement Dynamic Programming and Memoization in Java?
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🧩 Part 11/15
✔ Recursion in Java
✔ How Recursion Works
✔ Writing Recursive Methods
✔ Examples of Recursive Problems
✔ Backtracking in Java
✔ What is Backtracking
✔ Backtracking Algorithm
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🧩 Part 10/15
✔ Linear Search
✔ Binary Search
✔ Iterative Binary Search
✔ Recursive Binary Search
✔ Interpolation Search
✔ Comparison of Searching Algorithms
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🧩 Part 12/12
✔ Choosing the Right Framework
✔ Keras vs TensorFlow
✔ TensorFlow 2.0 and Keras Integration
✔ Optimizing Data Processing and Loading
✔ Using tf.data API
✔ Applying Data Augmentation
✔ Building and Training Models
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🧩 Part 11/12
✔ Testing and Debugging with Keras
✔ Using Callbacks and Checkpoints
✔ Handling Errors and Exceptions
✔ Testing and Debugging with TensorFlow Debugger
✔ Installing and Running TensorFlow Debugger
✔ Debugging Common Issues with TensorFlow Debugger
✔ Testing and Debugging with TensorFlow Profiler
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🧩 Part 10/12
✔ What is Model Deployment and Serving?
✔ How to Deploy and Serve Models with TensorFlow Serving
✔ How to Deploy and Serve Models with TensorFlow Lite
✔ How to Deploy and Serve Models with TensorFlow.js
✔ Comparison and Best Practices of Different Deployment and Serving Options
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🧩 Part 9/12
✔ Reinforcement Learning Basics
✔ What is Reinforcement Learning?
✔ The Reinforcement Learning Problem
✔ Types of Reinforcement Learning Algorithms
✔ Q-Learning: A Simple but Powerful Algorithm
✔ What is Q-Learning?
✔ How Q-Learning Works
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🧩 Part 8/12
✔ Generative Models and Adversarial Networks: Concepts and Applications
✔ What are Generative Models and Adversarial Networks?
✔ Why are they useful and what are some examples?
✔ Setting up the Environment: Installing Keras and TensorFlow
✔ Building a Generative Model: Variational Autoencoder (VAE)
✔ What is a VAE and how does it work?
✔ How to implement a VAE in Keras and TensorFlow?
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🧩 Part 7/12
✔ What is Time Series and Why is it Important?
✔ How to Prepare Data for Time Series Analysis
✔ How to Build and Train Time Series Models with Keras and TensorFlow
✔ Linear Regression
✔ Recurrent Neural Networks
✔ Convolutional Neural Networks
✔ Transformer Networks
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🧩 Part 8/8
✔ Summary of the blog series
✔ Benefits of using pandas and dataframes for data analysis
✔ How to access the official pandas documentation and tutorials
✔ Some recommended books, courses, and websites to learn more about pandas and dataframes
✔ Some practical examples and exercises to practice your skills
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🧩 Part 7/8
✔ Grouping dataframes with groupby
✔ Basic syntax and examples of groupby
✔ Applying multiple functions with agg
✔ Grouping by multiple columns and levels
✔ Aggregating dataframes with pivot_table
✔ Basic syntax and examples of pivot_table
✔ Specifying values, index, and columns
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🧩 Part 8/8
✔ Review of Spring MVC and Swagger Integration
✔ Benefits of Using Swagger for Spring MVC Applications
✔ Challenges and Limitations of Swagger
✔ Resources and Further Reading
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🧩 Part 7/8
✔ Setting Up Spring MVC Project with Swagger
✔ Creating a Spring Boot Application
✔ Adding Swagger Dependencies and Configuration
✔ Testing the Swagger UI and Documentation
✔ Preparing for Deployment to Heroku
✔ Creating a Heroku Account and App
✔ Configuring the Application Properties and Procfile
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🧩 Part 8/8
✔ Why Golang and Docker for Machine Learning?
✔ The Benefits of Golang
✔ The Advantages of Docker
✔ How to Build a Machine Learning Model in Golang
✔ How to Create a REST API for Your Model in Golang
✔ How to Containerize Your Model and API with Docker
✔ How to Deploy Your Dockerized Model and API to the Cloud
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🧩 Part 7/8
✔ Golang and Gorgonia: A Brief Overview
✔ Reinforcement Learning: Concepts and Algorithms
✔ Markov Decision Processes and Bellman Equations
✔ Q-learning and SARSA
✔ Policy Gradient Methods and Actor-Critic Models
✔ Generative Models: Concepts and Algorithms
✔ Variational Autoencoders and Latent Variable Models
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🧩 Part 6/8
✔ What are loops and conditionals?
✔ Loops
✔ Conditionals
✔ How to use loops and conditionals in Carbon Programming
✔ Syntax and examples of loops
✔ Syntax and examples of conditionals
✔ How to combine loops and conditionals in Carbon Programming
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🧩 Part 5/8
✔ What are functions and why are they useful?
✔ Function definition
✔ Function call
✔ Function parameters and arguments
✔ Function return value
✔ How to define and call functions in Carbon Programming?
✔ Syntax of function definition
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