How to Transform and Improve Your Deep Learning Code to a Visual Neural Network
Learn to visualize and improve deep learning neural networks, making them easier to understand and explain through code transformation and network visualization techniques.
A Deep Dive into Machine Learning and MLOps Pipeline with Model Registry and Feature Store
Explore key components of modern ML pipelines: AutoML, observability, model registry, transformation, and feature store. Learn to build scalable, reliable MLOps systems for enterprise-level machine learning.
A Visual Introduction to Modern and Scalable Machine Learning Pipeline
Visual guide to modern, scalable machine learning pipelines: from data prep to deployment. Covers key stages like training, validation, inference, feature stores, and model registry. Ideal for understanding ML infrastructure evolution.
Build a Neural Network for Classification from Scratch with PyTorch
Learn to build a neural network for classification using PyTorch, covering data preparation, network structure, and implementation. Gain hands-on experience in deep learning fundamentals.
Chatbot with LangChain Agents + GPT 3.5
Learn to build a conversational agent using LangChain and GPT-3.5, leveraging vector search retrieval to provide context from Lex Fridman's podcast for intelligent responses.
Managing the Complete Machine Learning Lifecycle with MLflow - Part 2
Explore MLflow's capabilities for managing machine learning lifecycles, from data preparation to model deployment, with practical insights on hyperparameter optimization and production workflows.
Topic Modeling for Spice Girls Lyrics
Explore topic modeling using R to analyze Spice Girls lyrics, uncovering hidden themes and patterns in their music through unsupervised machine learning techniques.
Model Canadian Wind Turbine Capacity With Decision Trees and Tidymodels
Learn to predict Canadian wind turbine capacity using decision trees and tidymodels, with hands-on data preparation, model tuning, and result interpretation techniques.
GPT 3.5 Fine Tuning Just Announced - Everything You Need to Know
Comprehensive guide to GPT-3.5 fine-tuning: process, data preparation, costs, API usage, and practical examples for leveraging this powerful AI model customization technique.
Preparing Data for LLMs and Generative AI Workflows
Explore data preparation essentials for LLMs and Gen-AI, covering Lakehouse AI, vector databases, model challenges, and building custom solutions with ML Flow AI Gateway.
Building MLM Training Input Pipeline - Transformers From Scratch
Learn to create a complex input pipeline for Masked Language Modeling, transforming raw OSCAR data into a DataLoader ready for training transformer models from scratch.
Build a Transformer for Language Classification in TensorFlow
Build a transformer-based sentiment classifier using HuggingFace's library in TensorFlow. Learn to download data, tokenize inputs, prepare datasets, define the model architecture, and train for multi-class sentiment analysis.
Using Semantic Search to Find GIFs
Learn to build a GIF search engine using semantic search, exploring data preparation, vector databases, and querying techniques for more accurate and context-aware results.
Convolutional Neural Networks with TensorFlow 2022
Comprehensive live-coding tutorial on Convolutional Neural Networks using TensorFlow, covering data preparation, model architecture, optimization techniques, and practical applications in image recognition and object detection.
Building Scalable AI Computer Vision Applications
Standardized workflow for building scalable AI computer vision applications, covering data preparation, annotation, model selection, benchmarking, deployment, and monitoring for efficient development.
Towards an Easy-to-use AI Platform - Data, Algorithms, and Systems - Wei Wang
Explore the development of user-friendly AI platforms, covering data preparation, model construction, debugging, and deployment, with a focus on enhancing efficiency and accessibility.
Time Series Forecasting with XGBoost - Use Python and Machine Learning to Predict Energy Consumption
Learn to predict energy consumption using XGBoost and Python in this hands-on tutorial. Covers data preparation, feature engineering, model building, and forecasting techniques for time series analysis.
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.
Technical Overview of Machine Learning Life Cycle
Comprehensive technical overview of the machine learning lifecycle, covering data preparation, model training, deployment, and inference. Ideal for AI engineers seeking in-depth understanding of ML processes.
An Improved and Functional Design of End-to-End Machine Learning Life Cycle
Comprehensive overview of the machine learning lifecycle, covering data preparation, model training, deployment, and inference, with practical insights for effective implementation and monitoring.
Scaling Data Preparation for Your Data-Centric AI Development
Explore scaling data preparation for AI development, covering DataOps, versioning, labeling, and automating workflows into robust pipelines for efficient ML operationalization.