~ Technology tutorials and blogs written by GPT Tutor ~
~ Technology tutorials and blogs written by GPT Tutor ~
🧩 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
...
🧩 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
...
🧩 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
...
🧩 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
...
🧩 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
...
🧩 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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🧩 Part 4/8
✔ Variables and Data Types
✔ Declaring Variables
✔ Data Types and Type Inference
✔ Type Conversion and Casting
✔ Operators and Expressions
✔ Arithmetic Operators
✔ Comparison and Logical Operators
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🧩 Part 9/10
✔ What are Advanced NLP Techniques?
✔ Sentiment Analysis
✔ Entity Extraction
✔ Dialogue Generation
✔ How to Enhance Your Chatbot with Advanced NLP Techniques?
✔ Choosing the Right Tools and Frameworks
✔ Preparing the Data and Training the Models
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🧩 Part 8/10
✔ What is Rasa X and Why You Need It
✔ How to Install and Set Up Rasa X
✔ How to Connect Your Chatbot to Rasa X
✔ How to Use Rasa X Dashboard
✔ Conversations
✔ NLU Inbox
✔ Response Selector
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🧩 Part 7/10
✔ Choosing the Right Deployment Platform
✔ Web Application
✔ Mobile Application
✔ Connecting Your Chatbot with Different Channels
✔ Messaging Apps
✔ Social Media
✔ Voice Assistants
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🧩 Part 12/12
✔ Java Code Style
✔ Naming Conventions
✔ Formatting and Indentation
✔ Comments and Javadoc
✔ Java Documentation
✔ Why Document Your Code?
✔ How to Write Effective Documentation
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🧩 Part 11/12
✔ What is Java Testing?
✔ JUnit: The Most Popular Java Testing Framework
✔ What is JUnit?
✔ How to Use JUnit?
✔ JUnit Annotations and Assertions
✔ Mockito: The Best Java Mocking Framework
✔ What is Mockito?
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🧩 Part 10/12
✔ What is Java Database Connectivity (JDBC)?
✔ How to Set Up JDBC in Your Project
✔ How to Connect to a Database Using JDBC
✔ What is SQL and How to Use It in Java
✔ How to Perform CRUD Operations Using JDBC and SQL
✔ Best Practices for JDBC Programming
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🧩 Part 6/12
✔ Audio Processing Basics
✔ Sampling Rate and Bit Depth
✔ Waveform and Spectrogram
✔ Feature Extraction and Normalization
✔ Keras and TensorFlow for Audio and Speech Recognition
✔ Loading and Preprocessing Audio Data
✔ Building and Training Speech Recognition Models
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🧩 Part 5/12
✔ What are Recurrent Neural Networks?
✔ The Basic RNN Cell
✔ The Long Short-Term Memory (LSTM) Cell
✔ The Gated Recurrent Unit (GRU) Cell
✔ How to Use Keras and TensorFlow to Build and Train RNNs
✔ Preparing the Data
✔ Defining the Model Architecture
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🧩 Part 4/12
✔ What are Convolutional Neural Networks?
✔ Convolutional Layers
✔ Pooling Layers
✔ Fully Connected Layers
✔ How to Use Keras and TensorFlow for Image Processing?
✔ Loading and Preprocessing Images
✔ Building and Training a CNN Model
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🧩 Part 9/12
✔ What is Time Series Analysis?
✔ Why is Time Series Analysis Important for Financial Machine Learning?
✔ Types of Time Series Data
✔ Stationarity and Non-Stationarity
✔ Autocorrelation and Partial Autocorrelation
✔ ARIMA Models
✔ LSTM Models
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🧩 Part 8/12
✔ Computer Vision Basics
✔ Image Processing and Analysis
✔ Image Recognition and Classification
✔ Object Detection and Localization
✔ Face Recognition and Verification
✔ Financial Applications of Computer Vision
✔ Document Analysis and Extraction
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🧩 Part 7/12
✔ Natural Language Processing for Text Analysis
✔ Tokenization and Vectorization
✔ Topic Modeling and Clustering
✔ Named Entity Recognition and Relation Extraction
✔ Natural Language Processing for Text Generation
✔ Language Models and Transformers
✔ Text Summarization and Paraphrasing
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🧩 Part 6/8
✔ What is Deep Learning and Why Use Matlab?
✔ Neural Networks in Matlab
✔ Creating and Training a Neural Network
✔ Evaluating and Testing a Neural Network
✔ Convolutional Neural Networks in Matlab
✔ Creating and Training a Convolutional Neural Network
✔ Evaluating and Testing a Convolutional Neural Network
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🧩 Part 5/8
✔ What is Unsupervised Learning?
✔ Types of Unsupervised Learning
✔ Applications of Unsupervised Learning
✔ How to Perform Unsupervised Learning in Matlab
✔ Data Preparation and Visualization
✔ Clustering Algorithms
✔ Dimensionality Reduction Algorithms
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🧩 Part 4/8
✔ Matlab Basics for Machine Learning
✔ Data Import and Preprocessing
✔ Visualization and Exploration
✔ Regression Models
✔ Linear Regression
✔ Nonlinear Regression
✔ Regularization and Validation
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🧩 Part 9/12
✔ What is Anomaly Detection?
✔ Types of Anomalies
✔ Applications of Anomaly Detection
✔ Anomaly Detection Models
✔ Statistical Methods
✔ Machine Learning Methods
✔ Deep Learning Methods
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🧩 Part 8/12
✔ What is Predictive Maintenance?
✔ What are Clustering Models?
✔ K-Means Clustering
✔ Hierarchical Clustering
✔ Density-Based Clustering
✔ How to Use Clustering Models for Health State Identification?
✔ Data Preprocessing
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🧩 Part 7/12
✔ What is Predictive Maintenance?
✔ What are Classification Models?
✔ Logistic Regression
✔ Decision Trees
✔ Support Vector Machines
✔ Neural Networks
✔ How to Apply Classification Models to Predictive Maintenance?
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🧩 Part 9/10
✔ Exploratory Data Analysis (EDA)
✔ Understanding the Credit Card Fraud Dataset
✔ Data Preprocessing
✔ Feature Engineering
✔ Creating New Features
✔ Handling Imbalanced Classes
✔ Model Selection
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🧩 Part 8/10
✔ Model Deployment with Flask
✔ Building a Flask API
✔ Testing the Flask API
✔ Model Monitoring with Streamlit
✔ Creating a Streamlit Dashboard
✔ Visualizing Model Performance
✔ Model Deployment and Monitoring with Azure ML
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🧩 Part 7/10
✔ Fraud Detection Problem and Data
✔ Machine Learning Models for Fraud Detection
✔ Logistic Regression
✔ Decision Tree
✔ Random Forest
✔ Support Vector Machine
✔ Neural Network
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🧩 Part 12/12
✔ Building a NLP Model with PyTorch
✔ Loading and Preprocessing the Data
✔ Defining the Model Architecture
✔ Training and Evaluating the Model
✔ Creating a Web App with Flask
✔ Setting Up the Flask Environment
✔ Designing the Web Interface
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🧩 Part 11/12
✔ What is Text Summarization?
✔ Extractive Summarization
✔ Abstractive Summarization
✔ What is BART?
✔ How to Load BART with HuggingFace
✔ How to Fine-Tune BART for Text Summarization
✔ How to Evaluate BART for Text Summarization
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🧩 Part 10/12
✔ What is Question Answering?
✔ What is BERT and How Does It Work?
✔ How to Use HuggingFace Transformers Library for Question Answering
✔ How to Fine-Tune BERT on SQuAD Dataset
✔ How to Evaluate BERT on Question Answering Tasks
✔ How to Deploy BERT for Question Answering on Web Applications
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🧩 Part 10/10
✔ Transaction Management Concepts
✔ ACID Properties
✔ Transaction States and Operations
✔ Concurrency Control and Locking
✔ Recovery and Logging
✔ Transaction Management Design Principles
✔ Define Transaction Boundaries and Granularity
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🧩 Part 10/10
✔ What is Elasticsearch for ML?
✔ How does it work?
✔ What are the benefits?
✔ Image Recognition: A Challenging Problem
✔ How to Use Elasticsearch for ML for Image Recognition
✔ Preparing the Image Data
✔ Creating an Index and a Pipeline
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🧩 Part 10/10
✔ What is Dialogflow and Why Use It for Chatbots?
✔ Dialogflow Features and Benefits
✔ Dialogflow Concepts and Terminology
✔ How to Deploy Your Chatbot Using Dialogflow
✔ Setting Up Your Dialogflow Agent and Intents
✔ Integrating Your Chatbot with Different Platforms
✔ Testing and Debugging Your Chatbot
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🧩 Part 6/10
✔ What are Robust Neural Networks?
✔ Definition and Motivation
✔ Challenges and Solutions
✔ How to Use Deep Learning for Complex Data?
✔ Convolutional Neural Networks
✔ Recurrent Neural Networks
✔ How to Prevent Overfitting and Improve Generalization?
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🧩 Part 5/10
✔ What are Robust Tree-Based Models?
✔ Decision Trees
✔ Advantages and Disadvantages of Decision Trees
✔ What are Ensemble Methods?
✔ Bagging
✔ Random Forest
✔ Boosting
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🧩 Part 4/10
✔ What are Robust Linear Models?
✔ How to Perform Feature Selection?
✔ Filter Methods
✔ Wrapper Methods
✔ Embedded Methods
✔ Lasso, Ridge, and Elastic Net Regression
✔ Lasso Regression
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🧩 Part 9/12
✔ Background
✔ Reinforcement Learning
✔ Active Learning
✔ Sequential Decision Making
✔ Methodology
✔ Problem Formulation
✔ Active Learning for Reinforcement Learning
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🧩 Part 8/12
✔ What is Anomaly Detection and Why is it Important?
✔ What is Active Learning and How Does it Work?
✔ A Case Study: Outlier Detection on a Credit Card Fraud Dataset
✔ Data Exploration and Preprocessing
✔ Applying Outlier Detection Methods: Isolation Forest and Local Outlier Factor
✔ Evaluating the Results and Selecting the Most Informative Samples
✔ Iterating the Process and Improving the Model Performance
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🧩 Part 7/12
✔ Active Learning for Clustering
✔ What is Active Learning?
✔ Why Use Active Learning for Clustering?
✔ How to Implement Active Learning for Clustering?
✔ Case Study: Clustering Customer Segments
✔ Data Description and Visualization
✔ K-means Clustering
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🧩 Part 9/10
✔ What are Component-Based Events?
✔ The Concept of Components in React
✔ The Concept of Events in React
✔ How to Create Components in React
✔ Using JSX Syntax
✔ Using Functional Components
✔ Using Class Components
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🧩 Part 8/10
✔ What are Client-Side Events and Why are They Important?
✔ How to Select Elements using jQuery
✔ How to Manipulate the DOM using jQuery
✔ How to Create and Handle Custom Events using jQuery
✔ How to Use AJAX with jQuery
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🧩 Part 7/10
✔ What are Server-Side Events and Why Use Them?
✔ The Benefits of Server-Side Events
✔ The Challenges of Server-Side Events
✔ How Node.js Supports Server-Side Event-Driven Programming
✔ The Node.js Event Loop and Event Emitter
✔ How to Create and Listen to Custom Events in Node.js
✔ How to Use Streams and Sockets for Server-Side Events in Node.js
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🧩 Part 6/8
✔ What is F1 Score and Why is it Important?
✔ What is Hyperparameter Tuning and How Does it Work?
✔ Grid Search vs Random Search: Pros and Cons
✔ How to Implement Grid Search and Random Search in Python
✔ How to Evaluate and Compare the Results of Grid Search and Random Search
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🧩 Part 5/8
✔ What is F1 Score and Why is it Important?
✔ Sampling Techniques for Imbalanced Data
✔ Oversampling Methods
✔ Undersampling Methods
✔ How to Apply SMOTE and NearMiss in Python
✔ How to Evaluate F1 Score and Compare Different Models
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🧩 Part 4/8
✔ What is Class Imbalance and Why is it a Problem?
✔ What is F1 Score and Why is it a Good Metric for Imbalanced Data?
✔ How to Use Class Weights to Adjust the Loss Function
✔ How to Implement Class Weights in Sklearn
✔ How to Evaluate the Performance of the Model with F1 Score
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🧩 Part 6/8
✔ Golang and Gorgonia: A Brief Overview
✔ What is Golang and Why Use It for Machine Learning?
✔ What is Gorgonia and How Does It Work?
✔ Natural Language Processing with Golang and Gorgonia
✔ Preprocessing Text Data in Golang
✔ Creating and Training Word Embeddings with Gorgonia
✔ Building and Evaluating a Sentiment Analysis Model with Gorgonia
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🧩 Part 5/8
✔ Golang and Gorgonia: A Brief Overview
✔ Neural Networks: Concepts and Applications
✔ What are Neural Networks?
✔ How do Neural Networks Learn?
✔ Types of Neural Networks and Their Uses
✔ Deep Learning: The Next Level of Neural Networks
✔ What is Deep Learning?
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🧩 Part 4/8
✔ Golang and Gonum: A Brief Overview
✔ What is Golang?
✔ What is Gonum?
✔ Linear Regression with Golang and Gonum
✔ The Linear Regression Model
✔ Data Preparation and Visualization
✔ Model Fitting and Evaluation
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🧩 Part 6/8
✔ What is Pruning and Why is it Important?
✔ Challenges of Pruning Neural Networks
✔ Pruning Methods for Neural Networks
✔ Weight Pruning
✔ Unit Pruning
✔ Structured Pruning
✔ Lottery Ticket Hypothesis
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🧩 Part 5/8
✔ Decision Trees and Overfitting
✔ What are Decision Trees?
✔ How to Measure Overfitting?
✔ Reduced Error Pruning
✔ How does Reduced Error Pruning Work?
✔ Advantages and Disadvantages of Reduced Error Pruning
✔ Example of Reduced Error Pruning
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🧩 Part 4/8
✔ Decision Trees and Error Rate
✔ How to Build a Decision Tree
✔ How to Measure the Error Rate of a Decision Tree
✔ Minimum Error Pruning
✔ What is Minimum Error Pruning
✔ How to Apply Minimum Error Pruning to a Decision Tree
✔ Example of Minimum Error Pruning
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🧩 Part 6/10
✔ What is Asynchronous Programming?
✔ Synchronous vs Asynchronous
✔ Benefits and Challenges of Asynchronous Programming
✔ How Asyncio Works in Python
✔ The Event Loop
✔ Coroutines
✔ Futures and Tasks
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🧩 Part 5/10
✔ What is Flask and Why Use It?
✔ Setting Up Flask and Creating a Basic Web Application
✔ Handling Web Events with Routes and Views
✔ Using Templates to Render Dynamic Web Pages
✔ Working with Forms and User Input
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🧩 Part 4/10
✔ Installing and Importing PyGame
✔ Creating a Game Window and a Game Loop
✔ Handling Game Events with PyGame
✔ Drawing and Moving Sprites with PyGame
✔ Detecting Collisions and Adding Sound Effects with PyGame
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🧩 Part 6/8
✔ The apply method
✔ Applying a function to each row or column of a dataframe
✔ Applying a function to each element of a dataframe
✔ The map method
✔ Mapping a dictionary or a function to a series
✔ Mapping a series to another series
✔ The lambda function
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🧩 Part 5/8
✔ Filtering dataframes
✔ Filtering by column values
✔ Filtering by multiple conditions
✔ Filtering by string methods
✔ Filtering by query method
✔ Sorting dataframes
✔ Sorting by column values
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🧩 Part 4/8
✔ Basic indexing and slicing
✔ Label-based indexing with loc
✔ Position-based indexing with iloc
✔ Boolean indexing with conditions
✔ Summary and exercises
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🧩 Part 9/12
✔ What is GPT-2 and why is it important for NLP?
✔ How to install and import PyTorch and HuggingFace transformers
✔ How to load and use a pre-trained GPT-2 model
✔ How to fine-tune GPT-2 on your own text data
✔ How to generate text with GPT-2 using different decoding strategies
✔ How to evaluate the quality and diversity of the generated text
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🧩 Part 8/12
✔ What are transformers and why are they important for NLP?
✔ The transformer architecture
✔ The attention mechanism
✔ What is BERT and how does it work?
✔ The BERT pre-training process
✔ The BERT fine-tuning process
✔ How to use PyTorch and HuggingFace to implement transformers and BERT
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🧩 Part 7/12
✔ Sequence to Sequence Models
✔ Encoder-Decoder Architecture
✔ Teacher Forcing and Scheduled Sampling
✔ Attention Mechanisms
✔ Dot-Product Attention
✔ Multi-Head Attention
✔ Machine Translation with PyTorch
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🧩 Part 6/12
✔ What is Predictive Maintenance?
✔ What is Remaining Useful Life?
✔ How to Use Regression Models for RUL Prediction?
✔ Linear Regression
✔ Polynomial Regression
✔ Support Vector Regression
✔ Random Forest Regression
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🧩 Part 5/12
✔ Predictive Maintenance: Concepts and Challenges
✔ What is Predictive Maintenance?
✔ Why is Predictive Maintenance Important?
✔ What are the Challenges of Predictive Maintenance?
✔ Machine Learning Models for Predictive Maintenance
✔ Supervised Learning Models
✔ Unsupervised Learning Models
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🧩 Part 4/12
✔ What is Predictive Maintenance and Why is it Important?
✔ The Challenges of Feature Engineering for Predictive Maintenance
✔ Data Quality and Availability
✔ Domain Knowledge and Expertise
✔ Computational Complexity and Scalability
✔ Feature Engineering Techniques for Predictive Maintenance
✔ Data Preprocessing and Transformation
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🧩 Part 3/10
✔ What are Robust Loss Functions?
✔ Huber Loss
✔ Log-Cosh Loss
✔ What is Regularization?
✔ L1 and L2 Regularization
✔ Dropout and Batch Normalization
✔ How to Apply Robust Loss Functions and Regularization in TensorFlow
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🧩 Part 2/10
✔ Data Cleaning
✔ Handling Missing Values
✔ Handling Outliers
✔ Handling Duplicates
✔ Data Transformation
✔ Scaling and Normalization
✔ Encoding Categorical Variables
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🧩 Part 1/10
✔ What is Robust Machine Learning?
✔ Why is Robust Machine Learning Important?
✔ Applications of Robust Machine Learning
✔ Benefits of Robust Machine Learning
✔ What are the Challenges of Robust Machine Learning?
✔ Data Quality and Diversity
✔ Model Uncertainty and Bias
✔ Adversarial Attacks and Defenses
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🧩 Part 6/12
✔ Active Learning for Regression
✔ What is Active Learning?
✔ Why Active Learning for Regression?
✔ How to Implement Active Learning for Regression?
✔ Case Study: Predicting House Prices
✔ Data Description and Preprocessing
✔ Linear Regression Model
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🧩 Part 5/12
✔ Active Learning: What, Why, and How
✔ What is Active Learning?
✔ Why Use Active Learning for Image Classification?
✔ How to Implement Active Learning?
✔ Image Classification: A Computer Vision Task
✔ What is Image Classification?
✔ How to Preprocess Images for Classification?
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🧩 Part 4/12
✔ Active Learning for Text Classification
✔ What is Active Learning?
✔ Why Use Active Learning for Text Classification?
✔ How to Implement Active Learning for Text Classification?
✔ A Case Study: Sentiment Analysis of Movie Reviews
✔ Data Collection and Preprocessing
✔ Feature Extraction using TF-IDF
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🧩 Part 6/10
✔ Model Testing
✔ Accuracy Metrics
✔ Latency Evaluation
✔ Power Consumption Analysis
✔ Model Evaluation
✔ Trade-offs and Considerations
✔ Benchmarking
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🧩 Part 5/10
✔ Data Collection
✔ Collecting Data from Embedded Devices
✔ Simulated Data Generation
✔ Data Preprocessing
✔ Data Augmentation Techniques
✔ Data Labeling
✔ Model Training
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