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Explore security vulnerabilities in LLM applications, focusing on browser and coding agents, with practical demonstrations of prompt injection attacks and essential mitigation strategies.
Explore advanced techniques for exploiting vulnerabilities in multi-chain LLM applications, including prompt injection methods, security testing approaches, and essential defensive strategies.
Master techniques for evaluating and ensuring LLM response accuracy in RAG pipelines using tools like gpt4o-mini, Llama-index, RAGAS, and Azure's Groundedness API for reliable AI applications.
Explore OpenAI CLIP's capabilities for zero-shot image classification and similarity, from local implementation using HuggingFace to deployment on Azure AI Studio with practical examples.
Master building LLM agents using PAL and ReAct paradigms, implement custom solutions and Langchain framework, and explore various language models while learning function calling and operational loops.
Delve into critical security vulnerabilities in Microsoft 365 Copilot, exploring prompt injection attacks, potential data breaches, and essential mitigation strategies for enterprise LLM applications.
Explore the architecture and implementation of multi-modal LLMs, focusing on Vision Transformers, contrastive learning techniques, and hands-on experience with Google's PaliGemma.
Explore security risks and mitigation strategies for LLM applications and autonomous agents, covering prompt injection vulnerabilities, AI safety misconceptions, and practical defense mechanisms.
Dive into practical implementation of Gemma 2B and Llama-2 7B models, exploring quantization techniques, prompting strategies, and hands-on deployment in Google Colab with free T4 GPU resources.
Master sentiment analysis through hands-on fine-tuning of DistilBERT, using PyTorch and HuggingFace to build a restaurant review classifier with practical implementation steps.
Master PyTorch implementation of attention mechanisms in neural machine translation, from theory to practice. Build and visualize a Bahdanau-style attention model for enhanced translation performance.
Master PyTorch implementation of encoder/decoder RNN for English-Italian translation, covering LSTM cells, bi-directional networks, and practical model evaluation based on Sutskever's research.
Dive into advanced RAG techniques including self-query retrieval, parent document strategies, and HyDE implementation, with hands-on labs using Langchain to enhance your LLM development skills.
Dive into Retrieval Augmented Generation (RAG) fundamentals and build a practical system using Langchain, GPT-3.5, and ChromaDB to enhance LLM capabilities with external knowledge retrieval.
Master PyTorch implementation of MNIST digit recognition through hands-on training, including data preparation, model development, and performance optimization techniques.
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