Do Vision Transformers See Like Convolutional Neural Networks - Paper Explained
Explore the differences between Vision Transformers and CNNs, examining feature learning, receptive fields, data impact, and architectural elements for a deeper understanding of these AI models.
ImageNet Classification with Deep Convolutional Neural Networks - Paper Explained
Detailed explanation of AlexNet, the groundbreaking deep convolutional neural network that revolutionized computer vision and kickstarted the deep learning era in image classification.
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.
Implementing a Multi-Class CNN Image Classifier in Pytorch - Computer Vision Basics Ep. 3 CIFAR10 CNN
Learn to build a CNN image classifier using PyTorch and CIFAR10 dataset. Covers data loading, model architecture, training, and validation, with practical coding examples for computer vision enthusiasts.
Real-World Python Neural Nets Tutorial - Image Classification with CNN - Tensorflow & Keras
Learn to build and train a CNN for image classification using TensorFlow and Keras. Covers data preparation, model improvement, hyperparameter tuning, and practical applications in Python.
Convolutional Neural Nets Explained and Implemented in Python - PyTorch
Comprehensive guide to Convolutional Neural Networks: theory, implementation, and practical applications using Python and PyTorch for computer vision tasks like image classification.
CNN Explainer - Learning Convolutional Neural Networks with Interactive Visualization
Interactive visualization tool for understanding convolutional neural networks, covering layers, filters, padding, activation, and pooling. Enhances learning through hands-on exploration.
Alexnet Architecture In-Depth Discussion Along With Code - Deep Learning Advanced CNN
In-depth exploration of AlexNet architecture, including mathematical operations, layers, and implementation code for advanced deep learning and convolutional neural networks.
Neural Nets for NLP - Structured Prediction with Local Independence Assumptions
Explore structured prediction in NLP using local independence assumptions and conditional random fields. Learn about sequence labeling, globally normalized models, and advanced training techniques.
Deep Learning for Natural Language Processing
Explore NLP architectures, transformers, language models, and advanced techniques for text generation and embedding learning in this comprehensive deep learning lecture.
Convolutional Neural Networks
Explore convolutional neural networks, from basic concepts to modern implementations. Learn about kernels, biological inspirations, CNN evolution, and their advantages in image processing and feature extraction.
Exploring the Origins and Prevalence of Texture Bias in Convolutional Neural Networks
Explore texture bias in CNNs: origins, prevalence, and implications. Examine dataset influence, training objectives, and model architectures to understand and mitigate this phenomenon in computer vision.
Captcha Recognition Using PyTorch - Convolutional-RNN + CTC Loss
Learn to implement a Convolutional-RNN model for captcha recognition using PyTorch. Covers data preparation, model architecture, CTC loss, and training/inference processes.
Benchmarks and How-Tos for Convolutional Neural Networks on HorovodRunner-Enabled Apache Spark Clusters
Explore benchmarks and implementation techniques for distributed deep learning using HorovodRunner on Apache Spark clusters, focusing on CNN tasks and scaling efficiency for both GPU and CPU clusters.
Convolutional Neural Networks - Deep Learning Basics with Python, TensorFlow and Keras
Learn to build and train a convolutional neural network for chess piece image classification using Python, TensorFlow, and Keras. Covers dataset preparation, model creation, and prediction testing.
How to Classify Natural Scenes Using Tensorflow, Python, and CNN
Learn to classify natural scenes using TensorFlow and CNN. Build, train, and test a model for image classification with Python, covering dataset preparation, model creation, and prediction.
Deep Learning in Weather Images - Using TensorFlow and CNN
Learn to build a CNN model using TensorFlow and Keras to predict weather scenes from images. Covers dataset preparation, model creation, training, and testing.
Cloud-Based Convolution Neural Network Ensembles for Computer Vision Counting
Explore ensemble CNN techniques for computer vision counting, featuring cloud computing evaluation and strategies for achieving low RMSE scores in competitive settings.
Keras Convolutional Neural Networks for MNIST and Fashion MNIST
Explore convolutional neural networks for image processing, focusing on MNIST and Fashion MNIST datasets. Learn about CNN architecture, training, and evaluation for advanced deep learning applications.
Networks Within Networks - Novel CNN Design Space Exploration for Resource-Limited Devices
Explore novel CNN design for resource-limited devices using "network within network" paradigm. Learn automated method for compressing neural networks while preserving accuracy and meeting size constraints.