The Complete Convolutional Neural Network with Python 2022

Explore the basics of convolutional neural networks, including the input layer, convolution layer, and how filters produce feature maps over the receptive field.

Learn how pooling layers reduce the spatial size and volume of feature maps, cut computation and parameters, and improve robustness to spatial variations by using max or average pooling.

Implement a basic convolutional neural network on the greyscale NIST digits dataset, loading via Carus library, reshaping to four dimensions, normalizing by 255, and training with batch size and epochs.

Extend a simple cnn in Python by building a feature extractor with two convolutional and pooling layers, flattening, and a fully connected classifier with softmax activation for 10 classes.

Implement a simple cnn by compiling with the adam optimizer and categorical cross-entropy loss, train the seven-layer model for hundreds of epochs, monitor accuracy, and plot loss and accuracy curves.

Build and evaluate a simple cnn model, plot predictions versus actuals on the test set, and demonstrate good accuracy and loss.

Explore how a simple convolutional neural network model uses convolutions and max pooling to form a feature extractor, followed by a fully connected classifier, achieving 97% accuracy on the dataset.

Load the CIFAR-10 dataset, split into training and testing sets, normalize pixel values, and verify data is loaded correctly by plotting the first image of each class.

Builds a cifar-10 project in tensorflow with a cnn using 2d convolution, kernel and stride, max pooling, batch normalization, relu activations, flattening, and dense layers with final sigmoid.

Train a cnn on cifar-10 using tensorflow with a learning rate of 0.001, cross-entropy loss, and early stopping while validating over up to 100 epochs.

Analyze the CIFAR-10 project progress, report an accuracy around 83.15% and non-balanced metrics, and review the training curve, loss, classification reports, and confusion matrices for test performance.

Explore the final part of the CIFAR-10 project by visualizing the first convolutional layer of a color-image CNN with 64 filters and analyzing its activations.

Explore a clothing image project with fashion mnist, using 60,000 training and 10,000 test grayscale 28x28 images across ten classes, including data visualization and normalization for neural networks.

Form a cnn model with a feature extractor of convolutional and pooling layers and a classify backend, reshaping grayscale 28x28 images for a TensorFlow sequential model.

Extend the clothing image CNN by adding a second 64-filter 3x3 convolutional layer with an activation, followed by pooling, a third convolutional layer, flattening, and a 64-node softmax classifier.

Train the model for ten epochs and evaluate it on the test set, achieving 91% test accuracy and 95% training accuracy. Visualize predictions with labeled samples to illustrate model performance.

Visualize the convolutional filters from the trained model, normalize the view to 0-1 range, and display the first 16 filters in a 4x4 grid to inspect learned features.

Explore data augmentation and data monetization in a clothing image project, using TensorFlow image data generator to create horizontally and vertically flipped images and expand the training set.

Apply data augmentation to clothing images by rotating images from -30 to 30 degrees and translating them horizontally to generate diverse training samples.

Build a clothing image classifier with a compact CNN featuring two convolutional layers, 32 and 64 filters, max pooling, and a dense output, and apply horizontal flips for data augmentation.

Explore a high level cnn method to combine two images, transferring content from one image to a style image while preserving the second image’s structure.

Explore the Visual Geometry Group's VGG architectures, including VGG16 and VGG19, detailing convolutional layers, pooling, and large fully connected blocks, and explain batch normalization.

Addressing objects at multiple scales, Inception networks split the input into parallel paths with convolutional layers of different filter sizes, then concatenate the outputs.

Explore neural style transfer using a pre-trained VGG19 model to blend content and style from two images, leveraging CNNs, Gram matrices, and feature maps for content and style representations.

Apply python-based optimization to combine a content image with a style image, using a style-content target structure and total loss comprising style, content, and total variation terms for style transfer.

Learn to implement a frame using convolutional neural networks to enhance image patterns, using an Inception model with two mixed feature layers and a loss function.

Apply gradient-based optimization with inception preprocessing to refine an image and reduce loss. Then introduce octave-based multi-scale processing to reduce noise and enhance pattern clarity.

Explore PyTorch, an open-source deep learning framework, for building and training neural networks with a dynamic computation graph, tensor operations, autograd, and Pythonic deployment with ONNX.

Explore YOLO, the you only look once real-time object detection algorithm that predicts bounding boxes, confidence scores, and class probabilities in a single pass using grid-based prediction and non-maximum suppression.

Understand image segmentation, including semantic, instance, and panoptic approaches, with applications in autonomous vehicles and editing, and methods from thresholding to deep learning models like cnn and mask r-cnn.

Explore OpenCV, a versatile open-source library for computer vision and image processing. Learn about image transformations, feature detection, object and gesture recognition, camera calibration, deep learning integration, and real-time applications.

Explore OpenCV as an open source computer vision library for image and video processing, feature detection, object detection, filtering, geometric transformations, and real-time applications.

Explore faster R-CNN, a two-stage region-based object detector using region proposal networks, anchor boxes, RoI pooling, and bounding box regression, with applications in autonomous vehicles, surveillance, and medical imaging.

Load a pre-trained faster R-CNN model with a ResNet-50 backbone and FPN v2 from torchvision. Load the image and set the model to evaluation mode for inference.

Implement faster r-cnn using opencv to read an image, convert it to a torch tensor with torchvision transforms, run detection, and draw bounding boxes with labels and scores.

Explain how Mask R-CNN extends Faster R-CNN with a mask head for instance segmentation, enabling object detection, pixel-level masks, and two-stage region proposals for precise localization.

Learn how to implement mask r-cnn in colab using a pre-trained model, convert images to tensors, run detection, and overlay raw segmentation masks with random colors on detected objects.

Practice hard to master deep learning, set personal goals and challenges, and download the Cargo and UCI respiratory datasets to develop and share a model.