Deep Learning with .NET 10 for Developers

Master Neural Networks, Transformers & Production AI

Want to understand Deep Learning without leaving your .NET ecosystem?

Deep Learning with .NET 10 for Developers is a practical, developer-focused ebook designed to take you from the fundamentals of neural networks to modern AI application architecture using C#, .NET 10, ASP.NET Core 10, ML.NET, ONNX, and ONNX Runtime.

This is not just a collection of theoretical definitions. The ebook connects deep-learning concepts with real-world software engineering, helping you understand how models work, how they are evaluated, how pretrained models are integrated, and how AI systems are designed for production.

What You’ll Learn

Deep Learning Fundamentals

• AI, Machine Learning & Deep Learning

• Neural networks and artificial neurons

• Activation functions

• Forward propagation

• Loss functions

• Backpropagation

• Gradient descent

• Optimizers

• Learning rates

• Regularization

• Overfitting and generalization

• Training and evaluation

Computer Vision

• Image tensors

• CNN architecture

• Convolution and filters

• Padding and stride

• Pooling

• Feature extraction

• Transfer learning

• Fine-tuning

• Pretrained vision models

Sequence Models

• Sequence modeling

• RNN architecture

• LSTM

• GRU

• Sequence-to-sequence models

• Encoder-decoder architecture

• Attention mechanisms

Transformers & Modern AI

• Transformer architecture

• Tokenization

• Embeddings

• Positional encoding

• Multi-head attention

• Feed-forward networks

• Residual connections

• Layer normalization

• Pretrained models

• Foundation models

.NET AI Engineering

• C# implementations and examples

• ML.NET

• ONNX

• ONNX Runtime

• Model inference

• Model contracts

• Tensor shapes

• Preprocessing and postprocessing

• Model versioning

• Model optimization

• Quantization

• Batching

• Caching

• Production inference APIs

• ASP.NET Core AI architecture

Modern Generative AI Foundations

The ebook also connects Deep Learning with the technologies that have become central to modern AI engineering:

• Embeddings

• Semantic search

• Vector databases

• Retrieval-Augmented Generation (RAG)

• Tool calling

• AI agents

• MCP

• Generative AI

• Enterprise AI architecture

Built for .NET Developers

The entire learning journey is designed around the Microsoft/.NET ecosystem.

You’ll work conceptually with:

C# 14 | .NET 10 | ASP.NET Core 10 | ML.NET | ONNX | ONNX Runtime

The focus is on understanding AI while applying the software-engineering principles you already know.

No Python is required for the learning approach presented in this ebook.

Why This Ebook?

Instead of learning isolated AI concepts, you’ll understand how the pieces connect:

Neural Networks → Deep Learning → CNN/RNN → Attention → Transformers → Foundation Models → Embeddings → RAG → AI Agents → Production AI

You’ll also learn how to think about AI systems from a production perspective:

Model + Data + Application + Security + Evaluation + Observability + Infrastructure

Who Is This For?

This ebook is ideal for:

• .NET Developers

• C# Developers

• ASP.NET Core Developers

• Full Stack Developers

• Software Engineers

• AI Engineers

• Machine Learning Engineers

• Solution Architects

• Technical Leads

• Developers transitioning into AI

• Developers preparing for AI engineering interviews

You do not need to be an advanced mathematician or machine-learning researcher. The concepts are introduced progressively and connected to practical software-engineering scenarios.

What Makes This Different?

This ebook focuses on the intersection of:

Deep Learning + .NET + Software Engineering + Production AI

You’ll learn not only how models work, but also how to think about:

• model selection

• inference architecture

• performance

• scalability

• security

• evaluation

• observability

• governance

• real-world AI application design

By the end, you should have a much clearer understanding of how modern AI systems are built and where your existing .NET skills fit into the AI ecosystem.

Your Learning Journey

AI Fundamentals
      ↓
Neural Networks
      ↓
Deep Learning
      ↓
CNN / RNN / LSTM / GRU
      ↓
Attention
      ↓
Transformers
      ↓
Pretrained Models
      ↓
Embeddings
      ↓
ONNX & Model Inference
      ↓
Generative AI
      ↓
RAG
      ↓
AI Agents
      ↓
Enterprise AI Architecture

If you're a .NET developer who wants to move beyond simply consuming AI APIs and learn how modern AI systems actually work and are engineered, this ebook gives you a structured path from deep-learning fundamentals to production AI architecture.

Learn the fundamentals. Understand the models. Build with .NET. Think like an AI engineer.