
Learn essential machine learning and deep learning concepts using PyTorch, covering linear models, neural networks, CNNs, and RNNs in a comprehensive series of lectures.

Learn PyTorch fundamentals, from installation to advanced applications. Master tensors, backpropagation, and deep learning techniques for real-world data science projects.

Learn PyTorch fundamentals, from linear models to advanced CNNs. Master gradient descent, back-propagation, regression, and neural network architectures for efficient deep learning implementation.

Comprehensive webinar series for ArcGIS developers, covering tools, techniques, and APIs for building powerful geospatial applications across various platforms and technologies.

Explore advanced image analysis techniques using ArcGIS Pro and arcgis.learn, including feature classification, object extraction, and point cloud processing for geospatial applications.

Comprehensive bootcamp covering machine learning and deep learning, from data extraction to model evaluation, using Python, Scikit-learn, and Spark MLlib.

Comprehensive tutorials on deep learning and neural networks, covering key concepts, architectures, and practical implementation using TensorFlow and Google Colab.

Hands-on projects in machine learning and deep learning, covering chatbots, sentiment analysis, image captioning, speech recognition, customer segmentation, and facial expression detection.

Comprehensive introduction to machine learning algorithms, from regression to neural networks, with practical Python implementations and engaging whiteboard explanations for beginners and enthusiasts.

Learn to implement YOLO v3 for robust object detection, from web scraping and dataset annotation to model training and deployment, using OpenCV and Python.

Learn to implement and train Mask R-CNN for instance segmentation using OpenCV and Python, covering installation, real-time applications, and custom object detection like potholes.

Learn to implement pose estimation using OpenPose, covering installation, people counting, fall detection, yoga pose recognition, and more. Gain practical skills in computer vision and deep learning applications.

Explore transformers and BERT in NLP, covering their architecture, applications, and hands-on implementation using PyTorch. Learn about sequence-to-sequence models, text summarization, and practical use cases.

Learn to build and apply Artificial Neural Networks for predicting bank defaulters using Keras, covering ANN architecture, activation functions, backpropagation, and practical demonstrations.

Explore AI's transformative impact on healthcare, from diagnosis to drug discovery. Learn key concepts and applications, with hands-on demos illustrating AI's potential in improving patient outcomes.

Explore essential matrix and vector operations for machine learning and deep learning using Python, covering creation, manipulation, and mathematical computations.

Develop a mobile app to classify cats and dogs using Flutter and TensorFlow Lite, incorporating machine learning and deep learning techniques for Android and iOS platforms.

Learn to build a Flutter app for iOS and Android that detects objects using TensorFlow Lite, combining mobile development with AI and machine learning techniques.

Build an image-to-text converter app using Flutter and Firebase ML Kit, incorporating OCR and image labeling for both Android and iOS platforms.

Develop AI-powered mobile apps using Flutter, TensorFlow Lite, and Google ML Kit. Learn image classification, face detection, and optical character recognition for Android and iOS.

Build a Marvel Avengers character recognition app using Flutter, GetX, and TensorFlow Lite. Learn AI, machine learning, and image classification techniques for mobile development.