2. Machine Learning
Machine Learning is the science of getting computers to
learn and act like humans do, and improve their learning
over time in autonomous fashion, by feeding them data
and information in the form of observations and real-
world interactions [1]
7. Key Concepts
•TensorFlow bases its data management in tensors
•Tensors are manipulated using operations
•Each operations are executed in computational graph
•Computational graph are executed with CPU, GPU or TPU
10. What Happened?
•Computational graph is not immediately evaluated when
they are declared
•The graph is executed with tf.Session's run function
11. Linear Regression
•Predict continuous values from previously acquired data
•We try to find linear equation that minimizes the
distance between data points and modeled line
14. Artificial Neural Network
•ANN consists of artificial neuron, a mathematical
function modeled after a real biological neuron
•It receives one or more inputs, and sums them to
produce an output
•Additionaly, the sums are normally weighted and the
sum is passed to a nonlinear function (activation
function or transfer function)
17. Convolution Neural Network
•Part of many of most advanched models currently being
employed.
•Used in numerous fields, but mainly used in image
classifications
18. Convolution
•Introducing kernel, a m x n-dimensional matrix and is
usualy a square matrix (m = n)
•Convolution process consist of multiplying the
corresponding pixels with the kernel and summing the
values for assigning to the central pixel
21. Subsampling - pooling
•Reduce the quantity and complexity of information while
retaining the most important information elements
•Well known pooling operations are max pool and average
pool