Convolutional Neural Networks for Visual Recognition
This lecture collection is a deep dive into details of the deep learning architectures with a focus on learning end-to-end models for these tasks, particularly image classification.
How Convolutional Neural Networks Work
Explore the inner workings of Convolutional Neural Networks, from basic concepts to advanced techniques, in this comprehensive guided tour of deep learning architecture.
CAP5415 - Introduction to Convolutional Neural Networks Part 2 - Lecture 6
Explore convolutional neural networks' architecture, layers, and features. Learn about AlexNet, pooling, normalization, and the progression from low-level to complex features in deep learning.
Deep Learning With TensorFlow
Learn TensorFlow basics, deep learning concepts, and practical applications through comprehensive tutorials and hands-on projects. Explore neural networks, computer vision, and natural language processing.
Complete Deep Learning
Comprehensive deep learning course covering neural networks, CNN, RNN, LSTM, and advanced topics with hands-on tutorials and practical implementations.
Convolutional Neural Networks with ML5.js
Explore Convolutional Neural Networks using ml5.js, covering setup, algorithms, max pooling, and architectures. Gain hands-on coding experience in this comprehensive tutorial.
Convolutional Neural Networks with ml5.js
Explore convolutional neural networks using ml5.js, covering setup, coding, model training, and classifier testing in this hands-on tutorial for machine learning enthusiasts.
Neural Networks Examples in MATLAB
Explore practical applications of neural networks in MATLAB, including image processing, classification, and prediction tasks using various deep learning architectures.
Mask R-CNN - Practical Deep Learning Instance Segmentation Tutorials
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.
Introduction to Convolutional Neural Networks with TensorFlow
Learn to build and implement convolutional neural networks for image classification using TensorFlow. Gain hands-on experience in deep learning techniques for computer vision tasks.
TensorFlow 2 Beginner Course
Comprehensive introduction to TensorFlow 2.0 and Keras, covering key concepts and practical applications in deep learning through hands-on projects and real-world examples.
Malarial Cell Classification Using CNN
Learn to classify malarial cells using CNN in Python. Covers dataset acquisition, TensorFlow setup, network design, and result analysis for detecting parasitized and healthy cells.
Convolutional Neural Networks
Explore convolutional neural networks for computer vision, covering feature extraction, network architecture, and real-world applications like self-driving cars in this comprehensive lecture.
Convolutional Neural Networks
Explore convolutional neural networks for computer vision tasks, covering feature extraction, convolution operations, and applications in classification, segmentation, and image captioning.
Convolutional Neural Networks
Explore deep learning for computer vision, covering CNNs, object detection, and image segmentation. Learn about architectures, applications, and impacts in various fields.
How Convolution Works
Guided tour of 2D convolution for CNNs and image processing, covering element-wise operations, feature detection, kernels, and practical applications.
How Convolutional Neural Networks Work, in Depth
Comprehensive exploration of convolutional neural networks, covering their structure, operation, and training process, including key concepts like filtering, pooling, and backpropagation.
Activation Analysis of a Byte-Based Deep Neural Network for Malware Classification
Explores deep learning models for malware classification, analyzing learned features from byte-level to end-to-end, connecting activations to binary semantics, and examining impacts of training data and regularization.
Predictive Coding Approximates Backprop Along Arbitrary Computation Graphs - Paper Explained
Explores how predictive coding can approximate backpropagation in neural networks, bridging neuroscience and machine learning. Discusses implications for biologically plausible deep learning algorithms.
Fluid Mechanics-Informed Machine Learning - Successes and Failures
Explore machine learning approaches for fluid mechanics simulations, including physics-based features and CNNs, with insights on successes and challenges in speeding up predictions.
Train Mask R-CNN for Image Segmentation
Learn to train and implement Mask R-CNN for image segmentation using free online GPU resources. Covers dataset preparation, model training, and practical application for object detection and segmentation.