Knowledge Center

We've compiled a collection of resources to help you navigate the complex world of AI implementation. From technical guides to strategic insights, these materials reflect our practical approach to delivering business value through AI.

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Books by Our Team

Our comprehensive training programs are built on practical implementation experience featured in bestselling books authored by our team

Generative AI with LangChain

Published: December 2023 | Amazon Bestseller in Programming

Build large language model (LLM) apps with Python, ChatGPT, and other LLMs

A practical guide to leveraging LangChain for enterprise GenAI implementation, with real-world examples ranging from customer support to data analysis. The 2024 edition features updated code examples and improved GitHub repository.

Focus areas include:

Enterprise-grade LLM application architecturePrompt engineering best practicesRAG implementation for knowledge augmentationCustom agent developmentProduction deployment strategies
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Machine Learning for Time Series

Published: October 2021 | Industry Standard Reference

Forecast, predict, and detect anomalies with state-of-the-art machine learning methods

Use Python to forecast, predict, and detect anomalies with state-of-the-art machine learning methods. This comprehensive guide covers everything from data preprocessing to advanced models for time-dependent data. The included tutorials range from simple forecasting to complex deep learning architectures for time series analysis.

Focus areas include:

Anomaly detection systemsForecasting methodologiesFeature engineering for time-seriesDeep learning approachesProduction deployment patternsTime series preprocessing techniquesLSTM and RNN architectures
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Artificial Intelligence with Python Cookbook

Published: October 2020 | BookAuthority Best-Seller

Proven recipes for applying AI algorithms and deep learning techniques

Proven recipes for applying AI algorithms and deep learning techniques using TensorFlow and PyTorch. The practical cookbook approach provides ready-to-use solutions for common AI challenges, from computer vision to natural language processing, with complete code examples and detailed explanations of implementation considerations.

Focus areas include:

Deep learning fundamentalsComputer vision applicationsNLP implementation techniquesReinforcement learningModel optimization strategiesTransfer learning approachesHyperparameter tuning
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