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Jhosimar - PeruC$42
C$42Private or group classes of Machine Learning and Deep Learning
Trusted teacher: Master in Computer Science from the State University of Campinas (Brazil) and university professor in Peru. He has participated in the most important Artificial Intelligence conferences including ACL, NeurIPS, ICML, ICLR, KDD, ICCV and CVPR, summer schools such as Machine Learning (MLSS), Deep Learning (DLRL) and Probabilistic ML (ProbAI). He has also participated in various programming contests and has experience preparing interviews for applications to companies such as Google, Meta, Microsoft, among others. He has extensive experience in the areas of Machine Learning and Deep Learning applied mainly to computer vision and natural language processing. He has experience in teaching, providing illustrative explanations for a better understanding of both the theoretical and practical parts. Some examples of presentations given: - He has also advised students from different countries in their graduation and master's theses, providing them with a theoretical and practical base with examples that they can then use to continue their development. Some of the things I can help you with: - Machine Learning: Linear regression, logistic regression, regularization, LDA, QDA, SVMs, decision trees, random forest, boosting, PCA, clustering (K-means, DBSCAN, hierarchical, GMM), neural networks, model selection, metrics evaluation, MLE, Bayesian learning, data preprocessing, etc. - Deep Learning: Multilayer Perceptron (MLP), backpropagation, activation functions, multiclass classification, optimizers (SGD, Adam, RMSProp, etc.), CNNs, architectures (ResNet, DenseNet, EfficientNet, Siamese, etc.), RNNs, LSTMs, Seq2seq, Attention, Transformers (BERT, GPT, ViT, etc.), autoencoders, generative models (VAE, GAN, Diffusion, etc.), etc. - Languages: Python, C++ - Libraries and frameworks: PyTorch, Tensorflow, Keras, Huggingface, numpy, pandas, scikit-learn, sympy, etc.
Computer programming · Python
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Mourad - CanadaC$39
C$39Mastering Large Language Models (LLMs) with Langchain: Building Chatbots and Document-based QA Systems
Trusted teacher: In today's rapidly evolving technological landscape, Large Language Models (LLMs) have emerged as a groundbreaking innovation, transforming the way we interact with machines and process vast amounts of textual information. This comprehensive course is designed to equip participants with the skills and knowledge to harness the power of LLMs for creating advanced chatbots and document-based question-answering (QA) systems. #### Course Objectives: - **Understanding LLMs:** Gain a deep understanding of Large Language Models, their architecture, and capabilities. - **Langchain Framework:** Learn how to use Langchain, a powerful framework for building LLM-powered applications. - **Building Chatbots:** Develop sophisticated chatbots capable of natural language understanding and generation. - **Document-based QA Systems:** Create robust QA systems that can accurately retrieve and process information from documents. - **Hands-On Projects:** Apply your learning through practical, hands-on projects and real-world scenarios. #### Course Outline: 1. **Introduction to Large Language Models:** - Overview of LLMs and their significance in AI - Key concepts and components of LLMs - Current trends and advancements in LLM technology 2. **Getting Started with Langchain:** - Introduction to the Langchain framework - Setting up the development environment - Understanding Langchain's core features and functionalities 3. **Building Your First Chatbot:** - Designing conversational interfaces - Implementing natural language understanding (NLU) and natural language generation (NLG) - Integrating LLMs into your chatbot 4. **Advanced Chatbot Development:** - Enhancing chatbot capabilities with context management - Handling multi-turn conversations - Deploying and maintaining chatbots in production environments 5. **Document-based Question Answering Systems:** - Understanding document processing and retrieval - Building QA systems using LLMs - Techniques for improving accuracy and relevance in QA systems 6. **Practical Applications and Case Studies:** - Real-world applications of chatbots and QA systems - Case studies highlighting successful implementations - Best practices and lessons learned 7. **Hands-On Projects:** - Developing a custom chatbot for a specific use case - Building a document-based QA system for a chosen domain - Integrating both systems into a cohesive application 8. **Future Directions and Advanced Topics:** - Exploring advanced features of LLMs and Langchain - Emerging trends and future developments in LLMs - Preparing for further learning and specialization #### Who Should Enroll: - Aspiring AI developers and data scientists - Professionals seeking to enhance their skills in LLMs and chatbot development - Enthusiasts interested in leveraging LLMs for innovative applications #### Prerequisites: - Basic understanding of programming concepts - Familiarity with Python is recommended but not required #### Course Outcomes: By the end of this course, you will be able to: - Understand and utilize Large Language Models effectively - Develop advanced chatbots using Langchain and LLMs - Build robust document-based QA systems - Apply your knowledge to real-world projects and scenarios - Stay abreast of the latest advancements in LLM technology Join us in "Mastering Large Language Models with Langchain: Building Chatbots and Document-based QA Systems" to unlock the potential of LLMs and create innovative AI solutions that transform how we interact with and process textual information.
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Our students from Gatineau evaluate their Python teacher.

To ensure the quality of our Python teachers, we ask our students from Gatineau to review them.
Only reviews of students are published and they are guaranteed by Apprentus. Rated 5.0 out of 5 based on 8 reviews.

Learn Python - from the basics to real world business cases (Amsterdam)
Joris
Joris is super friendly and was really flexible in tailoring the classes based on what my purpose for learning is. He is giving me the direction I need to continue this learning journey, and I would certainly recommend him to anyone interested in learning python to the next level.
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