Deep Learning and Generative Artificial Intelligence

Explore the course code repository on GitHub, open examples in Google Colab, switch runtime to GPU, and run code cells or all cells to align with the shared environment.

See how a convolutional neural network is structured and implemented. Gain insight into CNN concepts through a first general view and a typical code implementation.

Explore how neural networks process images by using ReLU in hidden layers and softmax in the output layer for multi-class classification, with 28x28 grayscale inputs and RGB color encoding.

Explore how a convolutional neural network processes input data, images as pixel grids, grayscale values 0-255, and color channels red, green, blue, for filtering and image recognition.

Slide a three by three filter over the input image, perform element-wise multiplication with each patch, and sum results to form feature map, with filter values learned during CNN training.

CNNs, with their architecture, apply to many tasks by combining convolution, ReLU, and pooling layers to learn complex features for classification, object detection, segmentation, and probabilistic control.

Engage with interactive playgrounds to test and visualize CNN predictions, deepening your understanding of CNN-based deep learning concepts through hands-on code demos and GitHub-backed experimentation.

Shows a convolutional neural network for mnist digit classification, highlighting training dynamics and hyperparameters: learning rate, batch size, momentum, nesterov momentum; architecture with conv, relu, pooling, softmax, and random crops.

Explore recurrent neural networks and how they recognize patterns in sequences, enabling time series forecasting through variable-length data and sharing parameters across the sequence.

Identify the vanishing gradients problem, where gradients shrink through repeated multiplication of weights less than one, causing updates to vanish and the training process to stall.

RNNs capture differences in sequence order to understand context in time series. They distinguish subtle meaning changes with the same words, a key strength for modeling sequential data.

Explore how LSTM networks overcome standard RNN limits with forget, store, update, and output gates that control the cell state and sustain gradient flow for long-term dependencies.

Utilize an LSTM-based time-series model to predict the S&P 500 index price using historical data since 1990, including volatility, rates, unemployment, sentiment, and new indicators beyond the original paper.

Explore how indicators behave over time in time series data by visualizing historical trends and their relation to closing prices, building an intuitive understanding before modeling.

Explore transformers as the leading model for language processing since 2017, powering ChatGPT, and extending to image and time sequence processing, while noting emerging successor architectures.

Understand how the transformer model uses an encoder-decoder architecture, with multi-head attention, feed-forward layers, and positional encodings, to compute output probabilities via softmax for efficient language processing.

Explore how word embeddings map semantic relationships in a vector space, showing how similar contexts link words like man and woman, guiding translation and text generation.

Explain how positional encoding in the transformer adds position information to input embeddings, enabling parallel sequence processing and unique sine and cosine encodings for each token position.

Explore the transformer architecture with a focus on multi-head attention, enabling the model to attend to different input parts simultaneously and boost translation and summarization performance.

See how a single attention head in a transformer links the word dog to other words, using query, keys, and values to weight dependencies and focus on sentence parts.

The transformer generates key vectors from word embeddings using a key weight matrix, enabling comparison with query vectors to compute attention scores.

Process input embeddings with positional encodings through self-attention and feed-forward networks in a transformer encoder to capture complex patterns and pass the final result for further processing.

Explore the transformer encoder architecture, including add and normalize layers, residual connections, and the combined power of self-attention and feedforward networks to capture language dependencies.

Explore transformer models in language processing through code demos and transformers beyond code, with playgrounds from various companies and platforms.