Master Deep Learning using Case Studies : Beginner-Advance

Explore artificial neural networks from perceptron basics to feedforward structures. Learn backpropagation, optimization techniques, and hyperparameter tuning for tasks such as image and video recognition.

Understand deep learning basics, inspired by brain via artificial neural networks, and how data, training methods, and compute bottlenecks enable image recognition, speech recognition, and tasks with CNNs and RNNs.

Explore deep learning foundations by tracing neural networks from perceptrons to artificial neurons, using inputs, weights, dendrites, and activation functions to generate outputs and show how learning strengthens connections.

Explore how a perceptron serves as first step toward a neural network by summing weighted inputs plus bias and applying a step activation that outputs one or zero; w^T x.

Explore using a perceptron as a binary classifier, with input vectors X and weights W, bias, and sign activation to decide outcomes like heart disease risk or a restaurant choice.

Explore how a perceptron uses weights and bias to separate data with W^T X + B > 0 for positives and < 0 for negatives, defining a decision line.

Discover how homogeneous coordinates simplify perceptron training by augmenting input vectors with a bias term, turning W^T X plus bias into a simple vector operation.

Explore Rosenblatt's perceptron training algorithm, iteratively updating weights to correct misclassified points using a simple dot-product rule, with homogeneous coordinates handling the bias.

Explore multiclass classification with perceptrons and neural networks, using weights to determine polygon boundaries and region membership.

Explore neural networks as layers of artificial neurons with nonlinear activation functions, extending perceptron beyond step function and using input and output layers, weights, and bias for classification or regression.

Explore how neural networks handle input data: text in one-hot form and word vectors, and images as pixel values and color channels, to prepare data for the input layer.

Explore the output layer by comparing sigmoid and softmax activations for binary and multiclass classification. Learn how to compute class probabilities using e^(w^T x) and the sum across output neurons.

Explore the sigmoid function as a binary classification tool. Understand its relation to softmax with one output neuron and probabilistic outputs using exponentials.

Explore MNIST, a dataset of handwritten digits, and address noise, orientation, and non-uniform handwriting. Learn 28x28 images, 784-input networks, 10 outputs, and softmax-based prediction.

Six core neural network assumptions include layered architecture, dense interlayer connections, non interacting neurons within a layer, input enters the input layer, weights and biases, and uniform activation per layer.

Explore how neural networks train by learning weights and biases and using gradient descent, set hyperparameters like layers and neurons, and map input to a 10-class output via dense connections.

Master neural network notations, including X as input, Y as labels, W and B as weights and biases, and probability outputs with softmax and sigmoid across layers.

Explore how a neuron's output is computed by activation functions from cumulative input, emphasizing smoothness and nonlinearity, and compare sigmoid, tanh, and ReLU families with forward propagation.

Explore how information flows in a feedforward neural network, from input to output layers, using weight matrices, biases, dot products, and activation functions to perform forward propagation.

Explore how forward propagation processes a simple image: count ones in pixels, amplify counts through two hidden layers, and backtrack weights with a zero bias.

Practice bidirectional rnn fundamentals through hands-on exercises that explore pixel-based inputs read clockwise or anticlockwise, amplify outputs, and modify layer-one weights to encode on-pixel counts.

Explore dimensions in a multi-layer network by identifying input size, weight matrices, bias vectors, and hidden layer outputs, and compute the total number of learnable parameters.

Write pseudocode for a feed-forward neural network, detailing input, hidden layers, weights, biases, and activations; compute and normalize class probabilities with softmax.

Learn to implement batch feedforward with vectorized matrix operations, converting single-point code to batch processing, and understanding data dimensions, batch size, and efficient multi-point computation.

Explore vectorized methods for efficient matrix multiplication in the feed forward operation, including block matrix multiplication, representing matrices as blocks, and broadcasting to add biases across data batches.

Train a neural network from scratch by minimizing loss through back propagation and gradient descent, tuning weights and biases to reduce average loss across training data.

Minimize neural network loss by updating weights and biases with gradient descent using a learning rate alpha. Understand the cost function and its multivariate gradients.

Explore backpropagation in a three-layer network, focusing on updating weights and biases to minimize the average loss. Learn feedforward, loss, gradient calculation, and parameter updates for training neural networks.

Explore forward propagation and the cross-entropy loss in a multi-class network, defining gradients for backward propagation to minimize error and improve probability predictions.

Explore backward propagation notation and loss-driven computations, derive softmax outputs Q1–Q3 from cumulative inputs, and apply gradient descent to update weights in backpropagation.

The lecture explains computing gradients for backpropagation through a three-weight network, applying the chain rule to derive loss derivatives with respect to weights and outputs, and using gradient descent.

Demonstrate backpropagation by computing gradients for a multi-layer network, linking forward propagation to backward derivatives and updating weights through the gradient descent process.

Explore sigmoid backpropagation for binary classification using a single-output neuron, derive cross-entropy loss gradients, and apply the chain rule to compute backpropagation steps.

Master backpropagation by computing gradients and derivatives to update weights, with simplified softmax gradients and a two-neuron output layer.

Compute the gradient of the loss with respect to W1, W2, and W3 using the chain rule and intermediate variables, then update the weights with the learning rate alpha.

Follow the back propagation training part 8, using the chain rule to compute derivatives and gradients of the loss with respect to the network weights, addressing two questions.

Demonstrates computing backpropagation updates for w1, w2, and w3 using sigmoid activations, deriving gradients, and expressing them in vector form, while noting libraries handle these computations in practice.

Trace the forward pass through a three-layer network from input to output probabilities and outline the backpropagation gradient computations for each layer, preparing for pseudocode in the next lecture.

Define the backpropagation pseudocode for a deep learning model, outlining the forward pass, loss function, gradient calculations, and batch weight updates across layers.

Learn how stochastic gradient descent uses mini-batches to update weights and biases efficiently. Define epochs, batch sizes, and reshuffling to enable robust training on large datasets.

Explore how gradient descent can trap in local minima and improve finding the global minimum by exploring multiple starting points for W and B.

Learn how to perform backpropagation for batches, compute the average loss across a batch, and update weights and biases accordingly.

Explore key regularization techniques to combat overfitting in neural networks, including L1 and L2 regularization, dropout, and batch normalization, and understand how regularization affects bias and variance.

Apply dropout to neural networks per layer to combat overfitting by randomly masking connections during training with bernoulli-based masks and probabilistic alpha vectors.

Explore how dropout breaks symmetry by masking a random subset of weights in each mini-batch, reducing neuron dependencies during training. Implement with a 0.25 probability in one line.

Explore batch normalization as a regularisation technique that stabilizes training by decoupling layer updates in backpropagation and addressing nonlinear dependencies across weight matrices.

This lecture explains batch normalization by normalizing the output vectors across the batch using the batch mean and standard deviation, applying the normalization to each column of the data matrix.

Learn how videos are processed as sequences of frames by applying a CNN to extract per-frame features and producing a video feature vector via a sequential network for classification.

Discover how Keras provides a wrapper around TensorFlow to simplify deep learning, while TensorFlow underpins numerical computation with dataflow graphs, placeholders, and sessions.

Learn how Keras provides a high-level API on top of TensorFlow to build and deploy deep learning models with just a few lines of Python, using a six-step workflow.

Explore convolutional neural networks designed for visual data and their rise from early ImageNet progress to deep transfer-learning models. Learn to train CNNs with Python and Keras for image tasks.

Discover how CNNs enable object localization and semantic segmentation, perform optical character recognition, and support medical imaging, radiology, cardiology, and gastroenterology, plus insurance surveillance and disaster assessment.

Explore cnn architecture at a high level using regnet as a simple example, showing how an image passes through convolution and pooling to produce a classification.

Explore how convolutional neural networks extract features from images through convolution, relu, and max pooling, reducing spatial size and building feature maps for softmax classification.

Understand how images convert to numeric matrices for neural networks, including grayscale and color with red, green, blue channels, 0–255 pixel values, and 2D or 3D shapes.

Treat video as a sequence of frames, use CNNs to extract per-frame features, then a sequential network to produce a video feature vector for classification with a softmax function.

Apply the convolution operation to an image using a filter, performing element-wise multiplication and summing results to produce a convolved matrix; this edge-detecting process illustrates how CNNs generate feature maps.

Apply convolution with filters to detect edges and features in images, exploring vertical, horizontal, and diagonal edges with Sobel-like kernels and multiple filters for richer feature extraction.

Learn how stride controls how many steps the filter moves across an image during convolution, and how padding preserves edge information by using zero or last-value padding.

Padding in convolutional networks preserves the input size, preventing information loss, and explains how kernel size and padding determine output dimensions.

Derive a generic convolution output size formula using input size, padding, filter size, and stride, and apply it to 24x24 inputs to determine possible outputs.

Explore three-dimensional convolution in cnn by applying 3-d filters to multi-channel images, learning how weights and biases are trained, and understanding padding and dot-product calculations that set the output size.

Explore how multiple filters create feature maps through convolution and activation, using learned neuron weights to detect features across image regions.

Explore pooling, including max and average pooling, to aggregate regional features from convolution maps, making networks invariant to local shifts while reducing dimensions and parameters.

See how a deep cnn uses convolution with multiple filters to create feature maps, applies pooling and relu activation, flattens outputs, and stacks layers toward a fully connected network.

Begin hands-on training of a convolutional neural network on a 60k color image dataset with 10 classes (32x32), learning to compute layer parameters and output sizes while modifying code.

Explore the VGG16 architecture: 16 layers (13 convolutional and 3 fully connected) trained on ImageNet for 1000-class classification, using 3x3 filters with padding, max pooling, relu activations, and softmax output.

train cnn networks on the cifar-10 dataset, which contains 60,000 rgb images at 32 by 32 pixels across 10 classes such as airplane, automobile, cat, dog, frog, horse, ship, and truck. load data with keras, set the number of classes and epochs, and run experiments, while using Google Colab with a GPU by changing the runtime to GPU.

Learn hands-on deep learning in Google Colab with GPU, using dropout, batch normalization, and convolutional nets on a CIFAR dataset; tune filters, regularization, and evaluate with cross-entropy and accuracy.

Explore how dropout, batch normalization, and convolutional versus fully connected layers affect model performance; the best setup uses dropout with batch normalization after conv and dense layers, boosting generalization.

Examine dropout after each convolutional layer with L2 regularization and batch normalization, and test an extra convolutional layer to compare accuracy and training time.

Increase the convolutional feature maps from 32 to 64 and 128, compare with adding a convolutional layer, and apply l2 while observing 92% training vs 84% validation, hinting at overfitting.

Demonstrate an end-to-end cnn training pipeline through hands-on case studies, from flower classification to chest x-ray imaging, with preprocessing, augmentation, and model tuning.

Load a two-class flower dataset, use glob for file listing, preprocess with the escape image library, and plot random sample images to illustrate CNN data preparation.

Explore image preprocessing for natural and non-natural images, including resizing, RGB to grayscale conversion, binary masks, and morphological operations such as thresholding, erosion, dilation, opening, and closing for segmentation.

Explore morphology operations (erosion, dilation, opening, and closing) and learn how to remove dark spots and connect bright regions in images, including brain x-ray examples.

Explore normalization techniques for image data to smooth training, address outliers, and stabilize gradients by using min-max, percentile, or z-score normalization, with attention to activation effects like sigmoid.

Apply data augmentation to address insufficient data or outliers, boosting accuracy and reducing overfitting by using linear transformations such as flipping and cropping and flying transformations like translation.

Demonstrates applying translation and linear transformations to images, including flips, zoom, scaling, 45-degree rotation around the center, and shear; adds noise and explores normalization with 25th and 75th percentiles.

Apply and verify image transformations in a deep learning case study: left-right and up-down flips, normalize between 25 and 75 percent, and perform a 90-degree rotation with fourfold zoom.

Learn to build and train deep networks using residual and bottleneck blocks with skip connections, explore variants, and leverage open-source code for effective model development.

Explore building your own customized data generator for deep learning, enabling train–validation–test splits, batch processing (32 images), shuffling, and returning (x, y) batches while applying transformations only to training data.

Explore ablation experiments to validate deep learning training, starting from preprocessing with normalization and augmentation, through architecture choices and data generator setup, to one-epoch trials before addressing overfitting.

Investigate overfitting of training data by training across epochs, observe loss decline and accuracy growth, and adjust activation functions, learning rate, optimizer (Adam), Xavier initialization, and network architecture.

Learn how to tune hyperparameters and leverage Keras callbacks to monitor losses, save models, and adjust learning rate for efficient deep learning training.

Apply training-time augmentation via a custom data generator by rotating, shifting, and flipping images. Use a callback to track a robust metric per epoch rather than accuracy on imbalanced data.

Explains how recall and precision influence decisions in medical diagnosis, highlighting misdiagnosis risks in cancer detection, and demonstrates training, validation, and checkpoint workflows for X-ray image prediction.

Apply deep learning to detect anomalies in x-ray images, such as tuberculosis, within minutes using public datasets and basic augmentation to train reliable predictions.

Develop deep learning models to detect early lung abnormalities from x-ray images by fusing normal and abnormal images, using Keras, aiming to save lives.

Explore case study driven deep learning for X-ray bone images, focusing on mindful data augmentation, rotations and flips, and min-max normalization to prepare data and build a robust model.

Apply min-max scaling instead of dividing by 255 and resize images to 256 by 256 to train lung water detection, then discuss augmentation limits and upcoming model building.

Build a model with one image channel and 256 by 256 input, using the same setup and augmentation data generator for two classes: abnormal and normal.

This case study explores class imbalance causing accuracy issues and shows how weighted cross-entropy with a custom loss function penalizes misclassifications of rare classes to improve model performance.

Explore an end-to-end deep learning case study pipeline with data augmentation, cross-entropy loss, and model checkpointing guided by validation accuracy to predict medical images and detect early disease signs.

Explore cnn architectures and the rise of transfer learning with pretrained models, and learn how to apply them to new problems using large-scale data and distributed training.

Explore AlexNet's eight-layer CNN, with five convolutional and three fully connected layers using 11x11 and 5x5 kernels, and how small 3x3 filters enable deeper networks like Google Net.

Understand Google Net's inception module, which parallelizes 1x1, 3x3, and 5x5 convolutions to deepen networks while reducing parameters from 60 million to four million, with only the final softmax.

Trace the rise of deep networks from 2012 to 2014, examine residual networks and skip connections, and see how deeper models up to 150 layers improve training and accuracy.

Learn residual nets that use skip connections to bypass non-improving layers, enabling deep architectures with three-by-three convolutions, global average pooling, batch normalization, and a single final softmax layer.

Transfer learning reuses pretrained CNN features as a versatile extractor for new tasks like classification or object protection, explaining when fine-tuning helps and outlining practical considerations.

Apply transfer learning to reuse skills from one task to a related one, explaining why data abundance or limited compute motivates this approach, with driverless car and pre-trained model examples.

Apply transfer learning with pre-trained convolutional networks, reuse generic initial-layer features and tailor the final layers for new image classifications.

Utilize transfer learning by leveraging a pre-trained model's general features and replacing the last layer for task-specific learning with limited data, using video frames for classification.

Explore practical transfer learning strategies: freeze initial layers and train only newly added layers, or retrain the full network with a reduced learning rate to adapt pretrained models.

Explore when to apply transfer learning using pretrained networks or your own models, freeze initial layers and retrain the last layers, and decide based on task similarity.

Learn transfer learning in python using a google dataset with five classes and a pretrained resnet, exploring freezing strategies, data preprocessing, augmentation, and on-the-fly data generation.

Learn how data augmentation increases training data variance through rotations, flips, zooms, and color tweaks, using data generators that batch and preprocess augmented images, while validation remains unaugmented.

We demonstrate transfer learning experiments by freezing the initial layers and retraining the rest with added pooling and softmax, achieving about 98.6% accuracy in few epochs.

Analyze practical considerations for deep learning models beyond accuracy, comparing architectures on memory footprint, number of parameters, operation count, and power consumption for mobile, real-time phase detection.

identify the hyperbolic trade-off between accuracy and interface time, note power consumption is independent of body size and architecture, and recognize inet as architecture where network operations estimate interface time.