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LangChain Memory and State: Conversations, Chat History and Workflows

Manage conversation history, user context, and workflow state without creating unreliable or expensive prompts.

https://www.tutorialslogic.com/langchain/memory-state

LangChain Production: Streaming, Evaluation, Observability and Guardrails

Prepare LangChain applications for production with streaming, tests, evaluation datasets, tracing, cost controls, and safety guardrails.

https://www.tutorialslogic.com/langchain/production-evaluation

LangChain Prompts, Chat Models and Output Parsers

Build reliable prompt templates, call chat models, and parse model responses into typed structures that application code can trust.

https://www.tutorialslogic.com/langchain/prompts-models-output-parsers

LangChain RAG: Document Loading, Chunking, Embeddings and Retrieval

Build retrieval augmented generation systems that answer from private data with better context, citations, and evaluation.

https://www.tutorialslogic.com/langchain/rag-retrieval

LangChain Runnables and LCEL: Compose Reliable LLM Pipelines

Learn LangChain Expression Language and runnable composition for branching, parallel work, retries, streaming, and reusable chains.

https://www.tutorialslogic.com/langchain/runnables-lcel

LangChain Security and Guardrails: Prompt Injection, Data Boundaries and Safe Tools

Protect LangChain applications from prompt injection, unsafe tool calls, data leakage, schema failures, and untrusted retrieved content.

https://www.tutorialslogic.com/langchain/security-guardrails

LangChain Tutorial: Build Production LLM Apps with RAG, Agents and Tools

Learn LangChain with complete examples for prompts, chat models, LCEL runnables, RAG, agents, memory, streaming, evaluation, and production deployment.

https://www.tutorialslogic.com/langchain

PyTorch CNNs and Transfer Learning for Image Classification

Build convolutional neural networks, use pretrained models, freeze layers, replace classifier heads, and fine-tune safely.

https://www.tutorialslogic.com/pytorch/cnn-transfer-learning

PyTorch Capstone: Build, Train and Serve an Image Classifier

Build a complete PyTorch image classification project with datasets, transforms, a CNN or transfer learning model, training, validation, checkpoints, inference, and deployment-ready structure.

https://www.tutorialslogic.com/pytorch/capstone-image-classifier

PyTorch Datasets and DataLoaders: Batching, Shuffling and Transforms

Build custom datasets, dataloaders, transforms, collate functions, and efficient input pipelines for PyTorch training.

https://www.tutorialslogic.com/pytorch/datasets-dataloaders

PyTorch Introduction: Deep Learning Workflow and Core Concepts

Understand PyTorch, tensors, modules, autograd, optimizers, datasets, and how a complete deep learning project fits together.

https://www.tutorialslogic.com/pytorch/introduction

PyTorch Mixed Precision, torch.compile and Performance Tuning

Speed up PyTorch training and inference with AMP, GradScaler, torch.compile, DataLoader tuning, memory checks, and profiling habits.

https://www.tutorialslogic.com/pytorch/mixed-precision-performance

PyTorch Optimization and Debugging: Loss Curves, Overfitting and Speed

Debug PyTorch models with loss curves, overfit-one-batch tests, learning-rate tuning, regularization, mixed precision, and profiling.

https://www.tutorialslogic.com/pytorch/optimization-debugging

PyTorch Regularization, Learning Rate Schedulers and Metrics

Improve PyTorch generalization with dropout, weight decay, augmentation, early stopping, learning rate schedules, and validation metrics.

https://www.tutorialslogic.com/pytorch/regularization-schedulers

PyTorch Saving, Loading, Exporting and Deployment

Save PyTorch checkpoints, load models for inference, export to TorchScript or ONNX, and design deployment-safe prediction code.

https://www.tutorialslogic.com/pytorch/saving-deployment

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