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TensorFlow in 3 sentences

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Barbara Fusinska
Barbara Fusinska

TensorFlow in 3 sentences Barbara Fusinska provides a high-level overview of TensorFlow in 3 sentences or less. She demonstrates how to build a computational graph for classification tasks using APIs like tf.nn and tf.layers. Barbara encourages attendees to get involved with open source TensorFlow communities on GitHub and through tools like Docker containers.

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TensorFlow in 3 sentences Barbara Fusinska @BasiaFusinska
About me Data Science Freelancer Machine Learning Programmer @BasiaFusinska BarbaraFusinska.com Barbara@Fusinska.com
Agenda • What is Deep Learning? • MNIST dataset Classification • 3 sentences & ~6 demos https://katacoda.com/basiafusinska/courses/tensorflow-in-3-sentences
Deep Learning?
Neural Networks • Interconnected units (neurons) • Activation signal(s) • Information processing • Learning involves adjustments to the synaptic connections
Artificial Neural Networks building blocks
Brief History of Neural Network
What has changed? • Big Data • The Cloud • GPU • Training methods • Tools • Computer Vision, NLP
Build the Computational Graph Sentence #1
What is Computational Graph?
Computational Graph: Constants, Placeholders & Variables Demo
Classification task Network training Data & Labels 0 1 2 3 4 5 6 7 8 9http://yann.lecun.com/exdb/mnist/
Data preparation 28 x 28 784
Training process Batches Dataset Parameters adjusting Loss function minimisation
Using trained model 784 Input data ... Input layer 2 9 0 1 ... Output layer 𝑜 = 𝜑(𝑾 ∙ 𝑥 + 𝒃) arg max 9 Label
Computational Graph: Training process Demo
Use APIs Sentence #2
Machine Learning workflow Data preparation & clean Data split Fit Machine Learning Coefficients Trained model Evaluate Training data Test data
Hidden layer ... Hidden layer 2 9 0 1 ... Output layer ... Input layer ℎ = 𝑅𝑒𝐿𝑈(𝑾 ∙ 𝑥 + 𝒃)
APIs: Estimators Demo
Convolutional Neural Network • Inputs have higher dimensions • Reduce the number of parameters (W, b) • Neurons arranged in 3D • Connected to the region of the previous layer
Pooling • Reduce spatial size • Control overfitting • Applied to every depth slice • Window size, stride • Max, Average
Convolutional layers ... Dense layer 2 9 0 1 ... Output layer ... Flattened convolutionInput layer 1st Convolutional 2nd Convolutional
APIs: tf.nn Demo
APIs: tf.layers Demo
Work with the communities Sentence #3
OpenSource
https://www.tensorflow.org/tutorials/seq2seq
Communities: Inception & Docker Demo
Keep in touch BarbaraFusinska.com Barbara@Fusinska.com @BasiaFusinska https://katacoda.com/basiafusinska/courses/tensorflow-in-3-sentences

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