Mastering Deep Learning for Generative AI

Discover how recurrent neural networks preserve information across a sentence using word vectors and hidden states. Explore 1-1, 1-to-many outputs, time steps, and cells like LSTM and GRU.

Build rnn architectures with TensorFlow, exploring LSTM and GRU alongside MLP, Keras integrations, and practical iris and NLP examples to understand many-to-one and one-to-many relations.

Train recurrent neural networks in TensorFlow using Keras, from data preparation with sine, square, and triangle datasets to building, compiling, and executing multiclass rnn models.

Learn how to optimize training of neural networks, including epoch choices, batch size, loss and accuracy monitoring, early stopping, and TensorBoard visualization, with notes on RNNs, softmax, and production latency.

Explore sequence-to-sequence models with encoder and decoder, explain cell states and context passing, illustrate translating English to Hindi, and discuss transfer learning and different weight initializations in backpropagation.

Explore how random and He initialization compare to zero and Xavier in neural networks, and see how LSTM gates—input, forget, and output—preserve long sequences in NLP tasks.

Explore how LSTM cells use input gates, tanh candidates, and forget mechanisms to preserve essential information across long sequences, contrasting with RNNs and enabling a custom TensorFlow implementation.

Explore custom, dependency-free model implementations for production systems, analyzing RNN and GRU performance with LSTM cells, activation functions, gates, and automated workflows using numpy and data frames.

Explore how convolutional neural networks process images by converting them to pixel arrays, highlighting edges and patterns, and applying padding and kernel concepts for classification.

Learn how CNNs convert rgb images to grayscale, represent pixels as matrices, and apply various kernels, including the Sobel kernel, through convolutional layers with padding and stride, aided by pooling.

Explore how padding and strides shape convolutional neural networks, with domain-based kernels, activation functions, and optimization strategies, to improve edge handling and output dimensions.

Master data augmentation and optimization for CNNs with TensorFlow, covering padding, stride, max pooling, and flatten layers to boost performance, expand datasets via data loader techniques, and mitigate overfitting.

Explore image operations like rotation, blurring, shifting, and cropping using OpenCV, then train CNN models with data augmentation, transfer learning, and hyperparameter tuning to improve accuracy and productionize models.

Learn to implement CNN models with TensorFlow for grayscale 224x224 images, preprocessing, configuring hidden layers and padding, and training with epochs for binary, multiclass, and regression tasks.

Explore cnn-based image classification with TensorFlow: data loading, preprocessing, rescaling, padding and striding, training with batch sizes, and using clustering to generate labels for pizza vs stick.

Explore convolutional neural networks using TensorFlow to preprocess data, load images, and visualize results, and tune hidden layers with padding and striding to transform inputs into outputs.

Train deep learning models on image data using a 1500-image pizza versus stick dataset, applying pre-processing and rescaling, and evaluating models on training and test data.

Rescale image data to 0–255, prepare train and test sets, resize to 24x24, and train a binary pizza-versus-steak classifier using mini-batches to prevent hangs.

Master the cleaning and preprocessing of image data, employ mini-batch sizing to stabilize training on about 15,000 training and 500 test images, and use clustering to label unlabeled data.

Practice multiclass image classification using a CNN in TensorFlow and Keras, with ten food classes, preprocessing, augmentation, and a VGG16 pretrained model; build, train, and evaluate accuracy and log loss.

Evaluate multi-class CNN models using softmax and categorical cross entropy, with Adam optimizers and accuracy metrics, then monitor training and validation loss, applying data augmentation to address underfitting.

Explore the VGG16 model, a 16-layer convolutional network from the Visual Geometry Group, and how transfer learning with pre-trained weights enables last-layer adaptation for custom datasets in Keras.

Explore how VGG-16 processes 224x224 RGB images through conv layers, pooling, and fully connected layers to recognize images, and how gradient descent optimizes loss toward convergence via backpropagation.

Understand how gradient descent updates weights via dL/dw across time steps to minimize loss toward global minima, and how stochastic and mini-batch variants improve efficiency on large datasets.

Explore mini-batch gradient descent and stochastic gradient descent, noting denoising effects compared to sequential updates. Examine momentum and adaptively learned rates via Adadelta for sparse weights.

Explore gradient-based optimization in deep learning, from Adadelta and Adagrad to Adam, using momentum and first/second moment estimates to achieve faster, more stable convergence.

Explore building a web-based Flask app that serves Keras models via get and post requests, uploads images, and performs transfer learning with pre-trained ImageNet models to return predictions.