$2,700

Deep Learning for Computer Vision with TensorFlow - 3-Day Intensive, Seattl...

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Seattle, WA

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TensorFlow, the open-source framework for deep learning created by Google, has experienced explosive growth in popularity. Its powerful syntax allows for distributed computation, improved efficiency, and modularization, enabling developers to turn high-level designs into the low-level mathematical operations required for machine learning algorithms.

In this three-day intensive course, you will learn how to use TensorFlow for deep-learning-based computer vision applications through a combination of lectures and examples plus substantial hands-on cloud-based laboratory exercises. The course outline is:

INTRODUCTION

Introduction to different types of AI models, and the focus of the class.

LINEAR REGRESSION

Linear regression definition, how to create models, how to train linear regression models, how to generate data, how to test accuracy of regression models

PERCEPTRON

Classification definition, basic neuron definition and operation, neuron creation, neuron training, accuracy testing

MULTI-CLASS MODELS

Classification using multiple neurons, multiple neuron models, multiple neuron error calculation, multiple neuron optimization, multiple neuron training

DEEP NEURAL NETWORKS

Activation functions, multiple layer network creation, multiple layer operation, multiple layer optimization, multiple layer error propagation, loss differentiation

AI FRAMEWORKS

Introduction to AI frameworks, Introduction to Tensorflow

TENSORFLOW LINEAR REGRESSION

Placeholders, Variables, Sessions, Running sessions, optimization, calculating error

TENSORFLOW CLASSIFICATION

Softmax definition, Cross Entropy definition, error propagation, Tensorflow Optimizers, Learning rate, epochs

TENSORFLOW DEEP NETWORKS

Tensorflow activation functions, Tensorflow differentiation, Tensorflow error propagation, Tensorflow Deep Network optimization, Deep Network descriptions

VISUALIZING MODEL OPERATION

Tensorboard, adding summaries, adding histograms, adding graphs, interpreting results

CONVOLUTIONAL NEURAL NETWORKS

Convolutional filters, feature maps, convolutional layers, pooling layers, fully connected layers, stride, padding, constructing CNN networks, training CNN networks

TRANSFER LEARNING

What makes a good data set, balanced data sets, distinct data sets, non-conflicting data, ImageNet, Inception-V3, transfer learning description, transfer learning operation, transfer learning

EMBEDDED VISION

TensorFlow Lite, weight quantization, operation on mobile/embedded devices


Registration Terms and Conditions:

This class is presented by the Embedded Vision Alliance, an industry association of companies developing and using computer vision technology. By registering for this class you agree to the following terms and conditions:

  1. Your contact information may be used by the Alliance to inform and update you with information about activities of the Alliance and its Members and may be shared with Members of the Embedded Vision Alliance for their use in contacting you regarding their products and services.
  2. During the Class, you may be photographed and/or videorecorded; the Alliance may use, reproduce, distribute, and broadcast your image, including your name, voice, likeness, and affiliation captured in such recordings as well as your name for promoting, publicizing, or explaining the Alliance and/or the Class.
  3. Registration fees are non-refundable wihtin two weeks of the class date.

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Seattle, WA

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