Scaling NumPy Applications from 1 CPU to Thousands of GPUs - IPAM at UCLA
Discover cuNumeric: a NumPy replacement enabling Python applications to scale across multiple nodes and leverage GPU acceleration for enhanced performance and dataset handling.
Build Your Own AI Chatbot with OpenAI API and Linode - A Step-by-Step Guide From Code With Harry
Step-by-step guide to building an AI chatbot using OpenAI API and Django, covering project setup, frontend development, API integration, and deployment on Linode.
Introduction to NLP and Topic Modeling
Explore NLP basics, advanced techniques like word vectors, and topic modeling. Learn pre-processing, tools, and methodologies to start NLP modeling and understand topic analysis.
What Are Decision Trees and How Do They Work - From Scratch
Comprehensive guide to decision trees in machine learning, covering basics, implementation, and advanced concepts using real-world examples and scikit-learn comparisons.
Natural Language Processing - Tokenization
Learn the fundamentals of tokenization in NLP, covering simple techniques and NLTK implementation. Gain practical coding skills for text processing and analysis.
How to Convert Almost Any PyTorch Model to ONNX and Serve It Using Flask
Learn to convert PyTorch models to ONNX format and deploy them using Flask, focusing on a BERT sentiment model. Gain practical skills in model conversion and API development.
Build a Web-App to Serve a Deep Learning Model for Skin Cancer Detection
Learn to build a Flask web app that serves a deep learning model for skin cancer detection, covering file uploads, predictions, and UI design with Bootstrap.
Simple and Basic Evaluation Metrics for Regression
Learn essential evaluation metrics for regression problems, including MAE, MSE, RMSE, RMSLE, and R2 score, with practical implementation in Python.
Spark NLP - State-of-the-Art Natural Language Processing at Scale
Discover scalable NLP techniques using Apache Spark, covering core algorithms, BERT embeddings, and multi-lingual challenges. Learn to build efficient pipelines for various NLP tasks through practical demonstrations.
ML Production Pipelines: Building and Deploying a Classification Model
Learn to build and deploy production-ready machine learning pipelines using Python, Databricks, and MLflow. Explore training and prediction workflows, model registration, and deployment options for both batch and on-demand processing.
Deep Learning for Price Action Prediction Using Databricks and AWS
Explore deep learning techniques to enhance price action prediction using Databricks and AWS. Learn to optimize technical indicators and train models with Amazon SageMaker for improved financial forecasting.
Tuning Machine Learning Models - Scaling, Workflows, and Architecture
Automating and scaling ML model tuning using Hyperopt and Apache Spark. Best practices for workflows, architecture, and optimization in hyperparameter tuning for improved performance and accuracy.
Neural Networks with Ensembles for TensorFlow and Keras
Learn to create and evaluate ensemble models using random forests and deep neural networks with TensorFlow, Keras, and scikit-learn. Explore feature importance and perturbation ranking techniques.
Transfer Learning Using Keras
Learn to classify car brands using transfer learning with Keras and ResNet50. Hands-on tutorial covers dataset preparation, model configuration, training, and prediction visualization.
Convolutional Neural Networks - Deep Learning Basics with Python, TensorFlow and Keras
Learn to build and train a convolutional neural network for chess piece image classification using Python, TensorFlow, and Keras. Covers dataset preparation, model creation, and prediction testing.
How to Classify Natural Scenes Using Tensorflow, Python, and CNN
Learn to classify natural scenes using TensorFlow and CNN. Build, train, and test a model for image classification with Python, covering dataset preparation, model creation, and prediction.
Transfer Learn in Weather Images
Learn to classify weather images using transfer learning with Python, TensorFlow, and VGG19. Covers dataset preparation, model implementation, and image prediction.
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.
How to Classify Flowers Using Deep Learning
Learn to build a convolutional neural network for flower image classification using TensorFlow and Keras, from dataset preparation to model testing and prediction.
Installing Miniforge and Anaconda/Miniconda Side-by-Side on Mac M1 Monterey
Learn to install and run Anaconda and Miniforge simultaneously on Mac M1, including TensorFlow and Keras setup, with step-by-step guidance and troubleshooting tips.
TensorFlow 2.0 GPU, Keras, and Python 3.7 Installation Guide for Windows 10 - 2020 Update
Comprehensive guide for setting up TensorFlow 2.0 with GPU support on Windows 10, covering installation of necessary drivers, CUDA, cuDNN, and Anaconda environment configuration.