Convolutional Neural Networks: Deep Learning

Explore the perceptron network, a fully connected feedforward model that maps inputs to outputs by learning weight-based relations between input, hidden, and output layers.

Explore how feed forward networks compute outputs using weighted sums and activation functions in a simple two-input, no hidden layer perceptron with a single output neuron.

Explore why activation functions inject non-linearity into neural networks, enabling learning real world non-linear data by applying thresholds to outputs and preventing linear junk values.

Learn how the rectified linear unit, or ReLU, uses y = max(x, 0) to turn negative neuron outputs into zero while passing through positive values, enabling efficient deep CNNs.

Explore convolution as a mathematical operation used in convolutional neural networks to extract features from images, via learnable 1d and 2d filters (kernels) that adaptively detect edges and other patterns.

Explore one-dimensional convolution by folding, shifting, multiplying, and adding an input with a filter to produce an output. Show that a high-pass filter extracts high-frequency variations and suppresses low-frequency components.

View images as two-dimensional signals of pixel intensities in binary, grayscale, and color formats. See RGB channels and eight-bit values (0–255), and how CNNs learn filters to detect edges.

Understand two-dimensional convolution: padding, sliding a filter, and multiplying and summing to produce a filtered image; learn how convolutional neural networks learn the best filter to extract features.

See convolution in action by applying a 2D kernel to a binary image in Python with OpenCV, NumPy, and Matplotlib, producing an output feature map.

Observe that a canny edge detector is applied to a color image and how convolutional neural networks learn filters to extract edges and information for object detection.

Explains convolution dimension rules in CNNs, showing how input size, filter size, stride, and padding determine output width via (w_in - w_f + 2p)/stride + 1, with integers.

Discover why convolutional neural networks excel at image processing by automatically extracting and learning features with convolutional layers and fully connected layers, enabling adaptive filters and efficient image understanding.

Explore CNN architecture, where convolutional layers automatically learn image features, followed by fully connected layers, with applications in object detection, facial recognition, vehicle tracking, and NLP.

Explore the cnn architecture, featuring feature learning and classification through convolutional layers with activation and pooling, followed by flattening, fully connected layers, and softmax output.

Apply max pooling or average pooling to downsample feature maps after convolution, summarizing regions into single values. Flatten the pooled maps to feed fully connected layers for learning.

Accelerate and stabilize learning in convolutional and fully connected networks with batch normalization, converting activations to zero mean and unit variance using batch mean and standard deviation plus epsilon.

Import libraries to build a convolutional neural network fruit classifier using a Keras model, including convolutional, pooling, activation, dropout, flatten, and dense layers with fruits 360 dataset and data augmentation.

Learn to download and organize the fruits 360 dataset from Kaggle into a Kaggle folder in Google Drive, then mount it in Google Colab and create training and testing folders.

Use the globe function on the training path to count folders as classes in the classification space. The example returns 131 classes with multiple images per fruit class.

Build a sequential CNN with conv and pooling layers, followed by flattening, dense layers, and dropout for 131-class classification; train with data augmentation and categorical cross-entropy.

Train a convolutional neural network with an image data generator, fitting on train data for 20 epochs and using a validation generator to monitor accuracy and loss, reaching 83% accuracy.

Load an image during the testing phase, then run model.predict and interpret the class to classify fruits; train with a data generator to augment images and tune hyperparameters, then test.