Course Code

embeddingprojector

Duration

14 hours (usually 2 days including breaks)

Requirements

Working experience with data visualization tools
Knowledge of machine learning is helpful, but not required
Knowledge of TensorFlow is helpful, but not required

Overview

Embedding Projector is an open-source web application for visualizing the data used to train machine learning systems. Created by Google, it is part of TensorFlow.

This instructor-led, live training introduces the concepts behind Embedding Projector and walks participants through the setup of a demo project.

By the end of this training, participants will be able to:

Explore how data is being interpreted by machine learning models
Navigate through 3D and 2D views of data to understand how a machine learning algorithm interprets it
Understand the concepts behind Embeddings and their role in representing mathematical vectors for images, words and numerals.
Explore the properties of a specific embedding to understand the behavior of a model
Apply Embedding Project to real-world use cases such building a song recommendation system for music lovers

Audience

Developers
Data scientists

Format of the course

Part lecture, part discussion, exercises and heavy hands-on practice

Course Outline

To request a customized course outline for this training, please contact us.

Testimonials

★★★★★

I really appreciated the crystal clear answers of Chris to our questions.

Léo Dubus

Course:Réseau de Neurones, les Fondamentaux en utilisant TensorFlow comme Exemple

I generally enjoyed the knowledgeable trainer.

Sridhar Voorakkara

Course:Neural Networks Fundamentals using TensorFlow as Example

I was amazed at the standard of this class - I would say that it was university standard.

David Relihan

Course:Neural Networks Fundamentals using TensorFlow as Example

Very good all round overview. Good background into why Tensorflow operates as it does.

Kieran Conboy

Course:Neural Networks Fundamentals using TensorFlow as Example

I liked the opportunities to ask questions and get more in depth explanations of the theory.

Sharon Ruane

Course:Neural Networks Fundamentals using TensorFlow as Example

Very updated approach or CPI (tensor flow, era, learn) to do machine learning.

Paul Lee

Course:TensorFlow for Image Recognition

Given outlook of the technology: what technology/process might become more important in the future; see, what the technology can be used for.

Commerzbank AG

Course:Neural Networks Fundamentals using TensorFlow as Example

I was benefit from topic selection. Style of training. Practice orientation.

Commerzbank AG

Course:Neural Networks Fundamentals using TensorFlow as Example

About face area.

中移物联网

Course:Deep Learning for NLP (Natural Language Processing)

I started with close to zero knowledge, and by the end I was able to build and train my own networks.

Huawei Technologies Duesseldorf GmbH

Course:TensorFlow for Image Recognition

Organization, adhering to the proposed agenda, the trainer's vast knowledge in this subject

Ali Kattan - TWPI

Course:Natural Language Processing with TensorFlow

the way he present everything with examples and training was so useful

Ibrahim Mohammedameen - TWPI

Course:Natural Language Processing with TensorFlow

Very knowledgeable

Usama Adam - TWPI

Course:Natural Language Processing with TensorFlow

The excersise where we should train a network to approximate a function

Nercia Utbildning AB

Course:Deep Learning with TensorFlow 2.0

Tomasz really know the information well and the course was well paced.

Raju Krishnamurthy - Google

Course:TensorFlow Extended (TFX)

A wide range of topics covered and substantial knowledge of the leaders.

ING Bank Śląski S.A.; Kamil Kurek Programowanie