Build and train a data model to recognize objects in images!

Explore Python basics with single-value variables, numbers and strings, and core operations, then print results and manipulate strings using length, find, replace, and split.

Learn to use Python multi-value variables, including tuples, arrays, and dictionaries, covering creation, indexing, updates, and common operations on lists and multi-dimensional arrays.

Learn how TensorFlow provides a high-level API for building and training machine learning models, from tensors and computational graphs to training and testing data, with a simple linear regression example.

Explore variable nodes and their differences from constant nodes, including initialization, reassignment, and extra functionalities. Use a global initializer to set values and perform operations, preparing for linear regression model.

Learn to build a sequential image recognition model from 32x32 RGB images using convolutional layers, max pooling, dense layers, dropout, and softmax classification, with SGD optimization and cross-entropy loss.

Delve into activation functions for CNNs, including identity, binary step, sigmoid, tanh, leaky ReLU, and softmax, and learn how to avoid dead neurons in final class probabilities.

build and train an image classifier from scratch by constructing the model layer by layer, compiling and training code, and exploring gradient descent and optimizers.

Explore gradient descent, optimizers, and momentum, including stochastic and mini-batch methods, as backpropagation adjusts weights to minimize loss in image classification and save weights for reuse.