Introduces deep learning, from logistic regression to neural networks, emphasizing the need for handling non-linearly separable data.
Introduces deep learning fundamentals, covering data representations, neural networks, and convolutional neural networks.
Covers Multilayer Perceptrons, artificial neurons, activation functions, matrix notation, flexibility, regularization, regression, and classification tasks.
Covers the fundamentals of deep learning, including data representations, bag of words, data pre-processing, artificial neural networks, and convolutional neural networks.
Explores neural networks optimization, including backpropagation, batch normalization, weight initialization, and hyperparameter search strategies.
Covers the back-propagation algorithm for deep neural networks and the importance of locality in CNN.
Covers variance reduction techniques in optimization, focusing on gradient descent and stochastic gradient descent methods.
Covers modern Neural Network approaches to NLP, focusing on word embeddings, Neural Networks for NLP tasks, and future Transfer Learning techniques.
Introduces the basics of data science, covering decision trees, machine learning advancements, and deep reinforcement learning.
