ETA Prediction with Graph Neural Networks in Google Maps - Paper Explained
Explore Graph Neural Networks for ETA prediction in Google Maps, covering graph formation, features, GNN architecture, loss functions, baselines, inference, and experimental results.
Neural Networks from Scratch in Python
Build and understand neural networks from the ground up, covering key concepts like neurons, layers, activation functions, and loss calculation in Python.
Gradient Boost
Explore the fundamentals and advanced concepts of Gradient Boost for regression and classification in machine learning.
Practical Machine Learning with Tensorflow
Learn essential machine learning concepts and apply them using TensorFlow, covering key topics from basic principles to deep learning and data visualization.
Consistency-Based Semi-Supervised Learning for Object Detection
Explore semi-supervised learning for object detection, focusing on consistency-based approaches. Learn about loss functions, background elimination, and experimental results in this concise presentation.
CAP5415 - Training Neural Networks Part 2 - Fall 2020 - Lecture 7
Explore advanced neural network training techniques, including loss functions, backpropagation, gradient descent optimization, and strategies to address common challenges in deep learning.
CAP5415 - Training Neural Networks Part 1 - Lecture 7 - Fall 2020
Explore neural network training techniques, including learning kernels, loss functions, and gradient descent, to enhance your understanding of deep learning fundamentals.
Statistical and Spatial Consensus Collection for Detector Adaption
Explore detector adaptation techniques for improved pedestrian detection across domains, focusing on statistical and spatial consensus methods to enhance accuracy and generalization.
Classification in Computer Vision - Part II - Lecture 19
Explore advanced classification techniques in computer vision, including SVMs, neural networks, and fully convolutional networks. Learn about activation functions, loss functions, and optimization for effective model training.
Training Neural Networks - Part II - Lecture 11
Explore advanced neural network training techniques, including loss functions, learning rates, momentum, and strategies to prevent overfitting for improved computer vision model performance.
Training Neural Networks for Computer Vision - Part I - Lecture 10
Explore neural network training techniques, including gradient descent, loss functions, and backpropagation, for effective computer vision model optimization and performance improvement.
Complete Deep Learning
Comprehensive deep learning course covering neural networks, CNN, RNN, LSTM, and advanced topics with hands-on tutorials and practical implementations.
How to Choose Model
Learn strategies for selecting the right machine learning model, including comparing models, handling noise, choosing loss functions, data splitting, and understanding assumptions.
Intro to PyTorch Tutorial - Building Fashion Recognizer
Learn to build a fashion recognizer using PyTorch in this hands-on tutorial. Covers core functionality, network architecture, loss functions, optimizers, and TensorBoard integration for effective model training.
Deep Learning Neural Network Tutorials
Comprehensive tutorials on deep learning and neural networks, covering key concepts, architectures, and practical implementation using TensorFlow and Google Colab.
Bank Defaulters Using ANN
Learn to build and apply Artificial Neural Networks for predicting bank defaulters using Keras, covering ANN architecture, activation functions, backpropagation, and practical demonstrations.
Deep Learning Indepth Tutorials With Krish Naik
Comprehensive deep learning tutorial covering key concepts, techniques, and practical implementations, from perceptrons to CNNs, with hands-on examples and expert insights.
Introduction to Deep Learning
Comprehensive introduction to deep learning fundamentals, covering perceptrons, neural networks, loss functions, gradient descent, backpropagation, and regularization techniques.
Keras ile Derin Öğrenme
Keras kullanarak derin öğrenme tekniklerini öğrenin. Kanser tespitinden görüntü işlemeye, oyun yapay zekasından LSTM'e kadar çeşitli uygulamalarla pratik yapın.
Bounding the Interleaving Distance for Mapper Graphs with a Loss Function
Exploring interleaving distance for Mapper graphs, introducing a loss function to measure assignment quality and enable polynomial-time computation for data comparison and clustering.
Math for Machine Learning - Exercises: Probability
Hands-on exercises exploring probability concepts in machine learning, including entropy, cross-entropy, and Gaussian distributions, with practical applications and insights.