Applied Machine Learning for Startups - Arshak Navruzyan
Applied ML strategies for startups: practitioner training, team structures, guidelines, and cutting-edge techniques in NLP and deep learning.
Introduction to Neural Networks in Python - Tensorflow-Keras
Learn neural network basics, architecture, hyperparameters, and implementation in Python using TensorFlow/Keras. Includes hands-on coding examples for various classification tasks, from simple to complex datasets.
Machine Learning for Beginners - Data Scientists and Analysts
Comprehensive introduction to machine learning for data professionals, covering key concepts, techniques, and practical applications in business contexts, with hands-on coding examples.
Training and Testing Data in Machine Learning and Neural Networks - Day 4
Explore training and testing data concepts in machine learning and neural networks, enhancing your understanding of model evaluation and performance optimization.
Machine Learning for Prediction of Terrestrial Climate and Weather
Explore cutting-edge applications of machine learning in predicting terrestrial climate and weather patterns, presented by a leading expert from the University of Maryland.
DALL-E Mini - Generate Images From a Text Prompt
Learn about DALL·E mini, a smaller version of OpenAI's DALL·E that generates images from text prompts. Explore its architecture, training process, and capabilities in this comprehensive overview.
Testing ML Models in Production - Detecting Data and Concept Drift
Explore robust ML model testing in production using MLflow, SciPy, and statsmodels. Learn key statistical tests, metrics for drift detection, and gain practical insights for maintaining model effectiveness.
Deep Learning in Weather Images - Using TensorFlow and CNN
Learn to build a CNN model using TensorFlow and Keras to predict weather scenes from images. Covers dataset preparation, model creation, training, and testing.
Zephyr 7B Beta - Comparing a 7B LLM with 70B Models
Explore Zephyr 7B Beta's performance against larger models through paper review and hands-on Google Colab tutorial. Learn setup, prompting, and practical applications in math, coding, and data tasks.
ChatGPT 3.5 Turbo Fine-Tuning Tutorial - Specific Tasks with Synthetic Data
Step-by-step guide to fine-tuning ChatGPT 3.5 Turbo for specific tasks using synthetic data, covering when and why to fine-tune, data creation, dataset cleaning, and model testing.
End-to-End Deep Learning Project: Kidney Disease Classification with MLflow, DVC, and Deployment
Comprehensive deep learning project on kidney disease classification, covering data ingestion, model preparation, training, evaluation, MLflow integration, DVC pipeline, and AWS deployment.
CNN Confusion Matrix with PyTorch - Neural Network Programming
Learn to build, plot, and interpret a confusion matrix using PyTorch for neural network evaluation, including techniques for disabling gradient tracking and analyzing model performance across different classes.
MLSecOps - Automated Online and Offline ML Model Evaluations on Kubernetes
Explore MLSecOps on Kubernetes: Automate real-time and offline ML model evaluations using KServe, Knative, Apache Kafka, and Trusted-AI tools for enhanced security and monitoring in production environments.
Fine-Tuning ChatGPT 3.5 with Synthetic Data from GPT-4 - Step-by-Step Guide
Explore step-by-step fine-tuning of ChatGPT 3.5 using GPT-4 synthetic data. Learn dataset selection, generation, model training, and evaluation for improved LLM results.
Introduction to Machine Learning - Lecture 2
Explore key machine learning concepts and their applications in healthcare, bridging the gap between computational modeling and clinical problem-solving.
GT - F Your Machine Learning Model
Explore techniques to manipulate machine learning models, focusing on ground truth manipulation and its implications for AI security and reliability.
Introduction to XGBoost - Machine Learning Tutorial
Comprehensive tutorial on XGBoost: from basic principles to hands-on implementation. Learn gradient boosting, parameter tuning, and model evaluation for real-world machine learning applications.
Training and Evaluating LLaMA2 Models with Argo Workflows and Hera
Comprehensive guide to train and evaluate LLaMA2 models using Argo Workflows and Hera SDK. Covers setup, data preprocessing, model training, and performance assessment with cloud-native tools.
Chatbot Arena: An Open Crowdsourced Platform for Human Feedback on LLMs
Explore Chatbot Arena, a platform for evaluating language models through human feedback. Learn about its Elo rating system, datasets, and applications in content moderation and safety benchmarking.
Reducing Hallucinations and Evaluating LLMs for Production
Explore techniques to reduce hallucinations and evaluate large language models for production use, covering traditional and modern evaluation methods, underlying causes, and best practices.
Mastering Large Language Model Evaluations - Techniques for Ensuring Generative AI Reliability
Explore techniques for evaluating Large Language Models, addressing challenges like hallucinations and toxicity, and learn essential metrics and tools for assessing AI reliability in generative applications.