An Overview of a Three-Day Introductory TensorFlow Tutorial

TensorFlow, released as open‑source software in November 2015 by Google Brain, is a deep‑learning framework that runs on multiple CPUs, GPUs, and optional CUDA, supporting 64‑bit Linux, macOS, Android, and iOS platforms.

The name comes from operations on multi‑dimensional arrays called tensors; Google has also created dedicated TPUs and TensorFlow Lite for mobile use, aiming to embed AI technologies across its ecosystem.

To help engineers adopt TensorFlow, the Hong Kong University of Science and Technology published a concise three‑day tutorial, which Machine of Heart briefly introduces while outlining core concepts and implementations.

Day 1 covers deep‑learning fundamentals, TensorFlow installation, the “Hello TensorFlow” example, and basic models such as linear regression, logistic regression, softmax classification, and perceptron‑based XOR solving, with detailed derivations of forward and backward propagation implemented in TensorFlow.

Day 2 focuses on convolutional neural networks (CNNs), discussing weight initialization, activation functions, loss functions, regularization, and optimization methods, largely following Stanford’s CS231n material, and demonstrates TensorFlow code for 2‑D convolution layers and max‑pooling.

Day 3 delves into recurrent neural networks (RNNs), covering encoder‑decoder architectures, attention mechanisms, and gated recurrent units (GRUs), providing extensive TensorFlow code for building single RNN cells, batch inputs, loss functions, and training graphs.

TensorFlow uses data‑flow graphs composed of nodes (operations or data sources) and edges (data dependencies); after constructing a graph, a Session runs it, allocating computations to available CPU or GPU resources.