SmartLearn

Learn how Gemini thinking models use additional reasoning to handle complex tasks such as coding, debugging, mathematics, data analysis, planning, and AI agent workflows.

for Access full course

https://www.udemy.com/course/master-llm-apis-with-c-net-build-ai-apps-using-openai/?couponCode=KEEPLEARNING
https://tinyurl.com/4wtmvu2a

In this beginner-friendly lesson, we explain:

• What Gemini thinking means
• Standard responses vs thinking responses
• Dynamic thinking
• Minimal, low, medium, and high thinking levels
• How to choose the correct thinking level
• Thinking level vs thinking budget
• Thinking tokens and output tokens
• The effect on quality, latency, token usage, and cost
• Thinking in tool-based and AI agent workflows
• Using Gemini thinking in C # and ASP.NET Core applications

You will also learn why high thinking is not always the best choice. Simple tasks may work well with lower thinking, while complex debugging, architecture analysis, and multi-step problems may benefit from deeper reasoning.

The main guideline is simple:

Use low thinking for simple tasks, medium thinking for balanced reasoning, and high thinking for difficult problems.

Learn how Gemini API Safety Settings help developers control potentially harmful content in AI applications.
for Access full course

https://www.udemy.com/course/master-llm-apis-with-c-net-build-ai-apps-using-openai/?couponCode=KEEPLEARNING
https://tinyurl.com/4wtmvu2a

In this beginner-friendly lesson, we explore Gemini’s adjustable safety categories, probability levels, blocking thresholds, and safety feedback. You will also learn how to manage safety settings in a C # and ASP.NET Core application.

Topics covered:

• Why Gemini safety settings are important
• Harassment, hate speech, sexually explicit, and dangerous-content categories
• Negligible, low, medium, and high probability levels
• The difference between probability and severity
• Block Few, Block Some, and Block Most thresholds
• OFF and BLOCK_NONE options
• Adjustable filters and built-in core protections
• Category-level and per-request configurations
• Testing settings in Google AI Studio
• Adding safety settings to an API request
• Prompt blocking versus response blocking
• Handling safety feedback correctly
• Centralizing safety configuration in a C # application
• Authentication, validation, testing, logging, and monitoring

This lesson is part of the Gemini API module in the course:

Master LLM APIs with C # and .NET — Build Real AI Applications Using OpenAI, Gemini, ASP.NET Core, and Blazor.

Learn how to use image understanding with the Gemini API.

for Access full course

https://www.udemy.com/course/master-llm-apis-with-c-net-build-ai-apps-using-openai/?couponCode=KEEPLEARNING
https://tinyurl.com/4wtmvu2a

In this beginner-friendly lesson, you will discover how Gemini’s multimodal AI models can analyse images together with text prompts and return useful text or structured JSON responses.

Topics covered include:

• What image understanding means
• How Gemini processes images and prompts
• Image understanding vs image generation
• Image captioning and detailed descriptions
• Visual question answering
• Image classification
• Object detection and segmentation
• Comparing multiple images
• Public URLs, inline image data, and the Files API
• Base64 image encoding
• Combining images with JSON output
• Deserializing JSON into C # objects
• Gemini image understanding with Blazor and ASP.NET Core
• Protecting your Gemini API key
• Validating important AI-generated results

This lesson is part of the course:

“Master LLM APIs with C # and .NET — Build Real AI Applications Using OpenAI, Gemini, and Blazor.”

Learn how to generate structured JSON responses using the Gemini API and process them inside a C # application.
for Access full course

https://www.udemy.com/course/master-llm-apis-with-c-net-build-ai-apps-using-openai/?couponCode=KEEPLEARNING
https://tinyurl.com/4wtmvu2a

In this beginner-friendly lesson, you will learn:

✅ What JSON output means
✅ Normal text versus structured JSON
✅ Why applications need structured data
✅ How JSON Schema controls the output
✅ JSON objects, arrays and nested structures
✅ How Gemini can generate quiz questions
✅ How to deserialize JSON using System.Text.Json
✅ How to convert Gemini JSON into C # objects
✅ Schema validation versus business validation
✅ How to use structured data in a Blazor application

We also build a practical Gemini quiz-generation workflow:

Teacher Prompt → Gemini API → Structured JSON → C # Object → Blazor Quiz

This lesson is part of Module 4 of the LLM APIs for .NET Developers course.

Subscribe for more lessons about Gemini API, OpenAI API, C #, ASP.NET Core, Blazor and AI application development.

How does sound travel, and how can a computer store it?

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https://tinyurl.com/3euw3ebv
https://www.udemy.com/course/edexcel-international-gcse-9-to-1-computer-science/?referralCode=5D51B68656EB613EFB58

In this beginner-friendly Pearson Edexcel International GCSE Computer Science lesson, you will learn how vibrations create sound waves and why natural sound is an analogue signal.

Topics covered:

How vibrations produce sound
How pressure changes travel through the air
High-pressure and low-pressure regions
How to read a sound waveform
Amplitude and loudness
Frequency and pitch
What an analogue signal is
Why analogue sound must be converted into digital data
How a microphone helps record your voice

By the end of the lesson, you will understand why real sound is continuous, while computers store digital data using binary.

Next lesson: Sampling Sound

Learn how resolution and colour depth affect bitmap image quality and raw file size.

for Access full course
https://tinyurl.com/3euw3ebv
https://www.udemy.com/course/edexcel-international-gcse-9-to-1-computer-science/?referralCode=5D51B68656EB613EFB58

In this beginner-friendly Pearson Edexcel International GCSE Computer Science lesson, we explain the important trade-off between improving image quality and increasing data requirements.

You will learn:

• How resolution affects image detail
• Why low-resolution images can appear pixelated
• How colour depth affects the number of possible colours
• Why higher resolution creates more raw image data
• Why higher colour depth requires more bits per pixel
• How larger images affect storage, memory and transmission
• How to select suitable image settings for different purposes
• How to explain the quality and file-size trade-off in an exam

Important formula:

Raw bitmap data in bits
= Width × Height × Colour depth

This lesson is suitable for Pearson Edexcel International GCSE Computer Science students, beginners and anyone learning about bitmap image representation.

Learn how to calculate the raw, uncompressed file size of a bitmap image using image resolution and colour depth

for Access full course
https://tinyurl.com/3euw3ebv
https://www.udemy.com/course/edexcel-international-gcse-9-to-1-computer-science/?referralCode=5D51B68656EB613EFB58

In this beginner-friendly lesson, we use the formula:

Raw bitmap data in bits = Width × Height × Colour depth

You will learn how to:

• Calculate the total number of pixels
• Use bits per pixel in file-size calculations
• Convert bits into bytes
• Convert bytes into KB and MB
• Use decimal and binary unit conventions
• Calculate a missing colour depth
• Compare 1-bit, 8-bit and 24-bit images
• Explain how resolution affects file size
• Explain how colour depth affects file size
• Avoid common exam mistakes
• Understand why actual file sizes may differ because of metadata and compression

This lesson is designed for Pearson Edexcel International GCSE Computer Science students, but it is also useful for anyone learning about bitmap images and data representation.

Main formula:

Width × Height × Colour depth = Raw bitmap size in bits

Remember:

Bits to bytes → Divide by 8
Bytes to bits → Multiply by 8

Subscribe for more beginner-friendly Computer Science lessons.

Learn how text generation works with the Google Gemini API.

for Access full course

https://www.udemy.com/course/master-llm-apis-with-c-net-build-ai-apps-using-openai/?couponCode=KEEPLEARNING
https://tinyurl.com/4wtmvu2a

In this beginner-friendly lesson, we explore how Gemini receives an input, processes it with an AI model, and produces a useful text response. You will also learn how to connect this capability to C #, ASP.NET Core, and Blazor applications.

Topics covered:

✅ What is text generation?
✅ Basic Gemini API workflow
✅ Writing effective prompts
✅ Using system instructions
✅ Temperature and generation settings
✅ Token-by-token text generation
✅ Text, image, audio, and video inputs
✅ Single-turn and multi-turn conversations
✅ Streaming versus non-streaming responses
✅ Gemini integration with Blazor and ASP.NET Core
✅ Code generation, summarization, translation, and classification
✅ Validating AI-generated responses
✅ Protecting your Gemini API key

This lesson is part of Module 4—Gemini API in the LLM APIs for .NET Developers course.

Subscribe for more lessons about Gemini, OpenAI, C #, ASP.NET Core, Blazor, and AI application development.

Learn how to choose the right Gemini model for your AI application.

for Access full course

https://www.udemy.com/course/master-llm-apis-with-c-net-build-ai-apps-using-openai/?couponCode=KEEPLEARNING
https://tinyurl.com/4wtmvu2a

In this beginner-friendly lesson, we explore the main Gemini model categories and explain how different models are designed for different requirements, including complex reasoning, fast responses, lower costs, multimodal understanding, and specialized AI tasks.

You will learn about:

What a Gemini model is
Why Google provides different models
Pro vs Flash vs Flash-Lite
Capability, speed, cost, and latency
Multimodal understanding
Image, speech, video, and transcription models
Real-time interaction
Embedding models
Model names and API model IDs
Stable, preview, and experimental versions
Keeping model selection configurable in C # and .NET
Choosing the right model for your application

This lesson is part of the Gemini API module in the course:

Master LLM APIs with C # and .NET

By the end of this lesson, you will understand how to balance model capability, response speed, operating cost, supported inputs and outputs, and your application’s requirements.

The next lessons will cover:

Gemini Flash vs Pro vs Flash-Lite
Making Your First Gemini API Request with C #

Learn how to create, secure, and use a Gemini API key in C # and .NET applications.

for Access full course

https://www.udemy.com/course/master-llm-apis-with-c-net-build-ai-apps-using-openai/?couponCode=KEEPLEARNING
https://tinyurl.com/4wtmvu2a

In this beginner-friendly lesson, we cover:

What a Gemini API key is
Why the Gemini API requires authentication
How to create a key using Google AI Studio
How API keys connect to Google Cloud projects
How to use the x-goog-api-key HTTP header
Why you should never hardcode an API key
How to use the GEMINI_API_KEY environment variable
How to protect keys in Blazor applications
Why Gemini API keys should remain on the backend
How to use separate keys for development, testing, and production

Recommended architecture:

Blazor UI → ASP.NET Core Backend → Gemini API

Always treat your Gemini API key like a password. Never expose it in browser code, course videos, screenshots, or public GitHub repositories.

This video is part of Module 4 — Gemini API in the LLM APIs for C # and .NET Developers course.

Next lesson: Gemini Models Overview