Generative AI & Deep Learning : All Models With Projects

Introduce deep learning and survey key models for beginners, including cnn, lstm, gan, gpt transformers, and object detection models like r-cnn and yolo, plus face recognition.

Define deep learning as a type of machine learning that trains on data with artificial neural networks inspired by the brain, applying to language, speech, face, and object recognition.

Explore essential deep learning terms, from neurons and layers to activation functions, feedforward and backpropagation, loss and optimizers like Adam, plus epoch, batch size, and exploding or vanishing gradient.

Explore the artificial neural network (ann) model with three layers: input, hidden, and output, and how it processes a cat image to output the cat class.

Explore deep neural networks with one input layer, many hidden layers, and one output. Harness hidden layers to extract features for image classification, object detection, NLP, sentiment analysis, and summarization.

Discover how convolutional neural networks extract features with convolutional and max pooling layers, then classify with dense layers, enabling image recognition and other computer vision tasks.

Explore recurrent neural networks (RNNs) for speech and text tasks, where each layer's output feeds the next to retain information, and understand vanishing gradients and the rise of LSTM.

Explore how long short-term memory (LSTM) uses a memory cell and three gates—input, forget, and output—to overcome vanishing gradients and enable applications like machine translation, speech recognition, and text summarization.

Explore how the transformer converts input sequences to output sequences using encoder attention decoder architectures. Learn how the transformer captures word relationships and context through self-attention and feed-forward layers.

Explore generative pre-trained transformers (GPT) and how they generate human-like text using transformer architecture, with self-attention, layer normalization, residual connections, and how they address vanishing gradients.

Explore generative adversarial networks (gan) and how a generator creates duplicate images from the original image using random noise, while a discriminator checks real versus fake.

Explore region-based deep learning for object detection with R-CNN, detailing the two-stage approach, from image regions and CNN feature extraction to SVM classification and bounding box localization.

Explore one-stage object detection with the YOLO model, a real-time CNN that uses a grid to predict bounding boxes and classify objects like dog, cat, or person.

Explore the residual network ResNet for object detection, a convolutional neural network that mitigates vanishing gradients with shortcut connections and skip layers.

Identify faces in images, extract facial features, and classify identities using hog with SVM, Naive Bayes, or decision trees.