Data-Driven Education Insights

  Kelleher, J. D. (2019). Deep Learning (1st ed.) . The MIT Press Buy the book here General Overview of the Book Introduces deep learning, its applications, and how it enables data-driven decision making Explains key machine learning concepts like datasets, algorithms, functions, overfitting vs underfitting Describes how neural networks work and how they implement functions  Traces history of neural networks through three key eras: threshold logic units, multilayer perceptrons/backpropagation, and deep learning Covers specialized neural network architectures like Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) Explains processes for learning functions from data using backpropagation and gradient descent algorithms Discusses future directions for deep learning like bigger datasets, new models, hardware improvements  Examines concept of representational learning in hidden neural network layers Considers challenges around interpretability and explainability of d

  Mitchell, M. (2019). Artificial Intelligence: A Guide for Thinking Humans (1st ed.). Farrar, Straus and Giroux. Buy the Book Here General Overview of the Book The book traces the history of AI through symbolic approaches, neural networks, machine learning, and deep learning. It discusses strengths and limitations of different techniques over time. A key thesis is that current AI systems, despite impressive capabilities on narrow tasks, lack true understanding and meaning that even young children display through intuitive physics, psychology etc. Understanding is linked to forming explanatory mental models, running simulations about likely outcomes, making predictions and generalizations - things lacking in today's AI. Abstraction, analogies, creativity, commonsense and metacognition remain extremely hard for AI systems and central to general intelligence displayed by humans. The book makes the case that today's AI may be more fragile, unreliable and opaque than commonly port