Q Learning Tutorial - Experience Replay
Explore Experience Replay in Q-Learning, focusing on data formatting and implementation. Learn key techniques for improving reinforcement learning algorithms in this hands-on tutorial.
Forecasting Weather with Neural Prophet and Python
Learn to forecast weather using Neural Prophet and Python. Preprocess data, train models, and generate accurate temperature predictions with this hands-on tutorial.
Build a Neural Network with Python Tutorial - Deep Learning with PyTorch
Learn to build, train, and evaluate a neural network for weather prediction using PyTorch. Covers data preprocessing, model architecture, GPU acceleration, and practical applications.
Build a Deep CNN Image Classifier with Any Images
Learn to build a custom deep CNN image classifier from scratch using Python, TensorFlow, and Keras. Covers data pipeline, preprocessing, network building, training, evaluation, and model saving.
Build a Deep Audio Classifier with Python and Tensorflow
Learn to build a deep audio classifier using Python and TensorFlow, covering data loading, preprocessing, model creation, and prediction for audio classification tasks.
Build a Mario AI Model with Python - Gaming Reinforcement Learning
Learn to create an AI model that plays Super Mario using Python and reinforcement learning. Set up the environment, preprocess data, build and train the model, and see the results in action.
Preprocessing Data for Machine Learning - Deep Dive
Comprehensive guide to preprocessing data for logistic regression, covering standardization, encoding, data imbalance, correlation, multicollinearity, and missing data handling techniques.
Fast Zero Shot Object Detection with OpenAI CLIP
Learn zero-shot object detection and localization using OpenAI CLIP, a multi-modal deep learning model. Implement practical techniques for efficient, domain-flexible computer vision tasks without fine-tuning.
AlexNet and ImageNet Explained
Explore the revolutionary AlexNet CNN and ImageNet dataset, their impact on deep learning, and practical implementation using PyTorch for image classification tasks.
Fine-Tune High Performance Sentence Transformers With Multiple Negatives Ranking
Learn to fine-tune sentence transformers using multiple negatives ranking loss, improving embedding quality for similarity prediction and outperforming SBERT. Includes PyTorch and sentence-transformers library implementations.
Fine-Tune Sentence Transformers the OG Way - With NLI Softmax Loss
Explore fine-tuning sentence transformers using NLI softmax loss. Learn preprocessing, PyTorch implementation, and Sentence-Transformers library usage for improved sentence embeddings in NLP applications.
Support Vector Machines in Python from Start to Finish
Comprehensive guide to implementing Support Vector Machines in Python, covering data preparation, model building, and optimization techniques for machine learning practitioners.
TorchVision Quick Start - Building an Image Classifier in 10 Minutes
Build an image classifier for orangutans and komodo dragons using PyTorch's torchvision library and a pretrained ResNet model. Learn to leverage pre-trained models for quick and effective computer vision tasks.
Creating an Animal Segmentation Model with U-Net and TensorFlow Keras
Learn to implement and train a U-Net model for animal segmentation using TensorFlow/Keras. Covers data preprocessing, model architecture, training, and evaluation with practical code examples.
Build a Movie Recommendation System Using Machine Learning
Learn to create a movie recommendation system using machine learning techniques. Explore data-driven approaches to personalize film suggestions based on user preferences and viewing history.
Reinforcement Learning for Gaming - Full Python Course
Comprehensive guide to applying reinforcement learning in gaming using Python and Stable Baselines 3, covering Mario, Doom, Street Fighter, and Chrome Dino with practical missions and best practices.
AI Learns What to Do Next Using TensorFlow and Python
Learn to predict future events using TensorFlow and Python. Build LSTM models, preprocess data, and generate next best actions for practical applications like sales progression.
Scikit-Learn Pipeline and Cross-Validation - Day 13 of 30 Days of ML
Learn advanced machine learning techniques with Scikit-Learn, focusing on pipelines for organized code and cross-validation for improved model performance evaluation. Part of a 30-day ML challenge.
Kaggle 30 Days of ML - Day 12 - Missing Values and Categorical Variables
Explore missing values and categorical variables in machine learning, learning practical approaches to handle data imperfections and enhance model performance.
PyTorch Dataset Normalization Using torchvision.transforms.Normalize()
Learn to normalize datasets in PyTorch using torchvision.transforms.Normalize(). Explore feature scaling, standardization, and color channel normalization. Understand the impact on neural network training.
Topic Modeling with R and Tidy Data Principles
Explore topic modeling in R using tidytext and stm packages, applied to Sherlock Holmes stories. Learn data preparation, text tidying, and model implementation techniques.