
Learn to build a real-time face mask detector using TensorFlow and Python. Master image labeling, custom object detection, transfer learning, and implement a practical workflow for training and deployment.

Learn to build a real-time gesture recognition app using TensorFlow.js, React.js, and Fingerpose. Control web applications with hand gestures through deep learning-powered computer vision.

Learn to analyze Twitter sentiment for presidential debates using Python, NLTK, and TextBlob. Extract polarity and subjectivity from tweets to gauge public opinion on candidates quickly and efficiently.

Build a real-time hand pose estimation app using Javascript, TensorFlow.js, and React.js. Learn to set up a pre-trained model, use webcam input, and detect hand landmarks for gesture recognition applications.

Build a real-time facial landmark detection app using Tensorflow.JS and React. Learn to install models, create the app, and detect face points via webcam in just 20 minutes.

Learn to create a face mask detector using Watson Visual Recognition, Python, and OpenCV. Capture images, visualize detections, and train custom object detection models in this hands-on tutorial.

Learn to calculate and implement Mean Average Precision (mAP) for evaluating object detection models, with a detailed explanation and hands-on PyTorch implementation from scratch.

Comprehensive guide to Intersection over Union (IoU) in object detection, covering theory and practical implementation in PyTorch. Ideal for understanding this crucial metric.

Learn to build flexible TensorFlow models using Keras subclassing, including a ResNet-like model with skip connections. Gain advanced model creation skills beyond Sequential and Functional APIs.

Learn to build a character-level LSTM text generator in PyTorch, focusing on generating new baby names. Explore practical implementation of RNNs for creative text generation tasks.

Implement a neural network using Python and numpy, focusing on the coding aspects while building upon previously explained mathematical concepts.

Learn to implement GoogLeNet/InceptionNet from scratch in PyTorch, with explanations of network architecture and step-by-step coding guidance.

Implement a neural network using Numpy, learning to build deep learning models from the ground up without relying on high-level frameworks.

Explore a comparison of three cutting-edge AI image captioning models, analyzing their performance across diverse images and understanding their unique approaches to vision-language tasks.

Fine-tune Stable Diffusion 2.0 with DreamBooth on free Google Colab. Create custom AI avatars and profile pictures. Learn data collection, processing, model training, inference, and conversion for Automatic1111 compatibility.

Learn to create a multilingual video subtitle generator using OpenAI Whisper, Python, and Gradio. Build, deploy, and use this free tool for automatic captioning in various languages.

Build ML-powered web apps using Gradio API on Hugging Face Spaces. Learn to integrate machine learning capabilities into vanilla JavaScript or React applications for free.

Learn to create an image captioning app using ViT and GPT2 models from Hugging Face. Build a Gradio interface and deploy it on the Model Hub for easy access and sharing.

Learn to convert English numbers to numeric values using Numerizer, build a Streamlit app, and deploy it on Hugging Face Spaces. Gain practical NLP skills through hands-on Python coding and web app development.

Explore Partial Dependence Plots for interpretable machine learning. Learn to visualize, build, and interpret PDPs, including 2D plots, to enhance model explainability and insights.

Explore OpenAI's CLIP model for zero-shot image classification, demonstrating its ability to predict relevant text for images using natural language instructions without task-specific training.