Curso de TensorFlow

Curso de TensorFlow

TensorFlow es una biblioteca de software de código abierto para el aprendizaje profundo. Los cursos de capacitación TensorFlow en vivo, locales y con instructor demuestran a través de debates interactivos y practican cómo usar el sistema TensorFlow para facilitar la investigación en aprendizaje automático y para hacer una transición rápida y fácil del prototipo de investigación al sistema de producción La capacitación de TensorFlow está disponible como "capacitación en vivo en el sitio" o "capacitación remota en vivo" El entrenamiento en vivo in situ se puede llevar a cabo localmente en las instalaciones del cliente en Venezuela o en los centros de capacitación corporativa de NobleProg en Venezuela La capacitación remota en vivo se lleva a cabo a través de un escritorio remoto interactivo NobleProg Su proveedor local de capacitación.

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Programa del curso TensorFlow

CódigoNombreDuraciónInformación General
tf101Deep Learning with TensorFlow21 horasTensorFlow is a 2nd Generation API of Google's open source software library for Deep Learning. The system is designed to facilitate research in machine learning, and to make it quick and easy to transition from research prototype to production system.

Audience

This course is intended for engineers seeking to use TensorFlow for their Deep Learning projects

After completing this course, delegates will:

- understand TensorFlow’s structure and deployment mechanisms
- be able to carry out installation / production environment / architecture tasks and configuration
- be able to assess code quality, perform debugging, monitoring
- be able to implement advanced production like training models, building graphs and logging
tfirTensorFlow for Image Recognition28 horasThis course explores, with specific examples, the application of Tensor Flow to the purposes of image recognition

Audience

This course is intended for engineers seeking to utilize TensorFlow for the purposes of Image Recognition

After completing this course, delegates will be able to:

- understand TensorFlow’s structure and deployment mechanisms
- carry out installation / production environment / architecture tasks and configuration
- assess code quality, perform debugging, monitoring
- implement advanced production like training models, building graphs and logging
tsflw2vNatural Language Processing with TensorFlow35 horasTensorFlow™ is an open source software library for numerical computation using data flow graphs.

SyntaxNet is a neural-network Natural Language Processing framework for TensorFlow.

Word2Vec is used for learning vector representations of words, called "word embeddings". Word2vec is a particularly computationally-efficient predictive model for learning word embeddings from raw text. It comes in two flavors, the Continuous Bag-of-Words model (CBOW) and the Skip-Gram model (Chapter 3.1 and 3.2 in Mikolov et al.).

Used in tandem, SyntaxNet and Word2Vec allows users to generate Learned Embedding models from Natural Language input.

Audience

This course is targeted at Developers and engineers who intend to work with SyntaxNet and Word2Vec models in their TensorFlow graphs.

After completing this course, delegates will:

- understand TensorFlow’s structure and deployment mechanisms
- be able to carry out installation / production environment / architecture tasks and configuration
- be able to assess code quality, perform debugging, monitoring
- be able to implement advanced production like training models, embedding terms, building graphs and logging
dlvDeep Learning for Vision21 horasAudience

This course is suitable for Deep Learning researchers and engineers interested in utilizing available tools (mostly open source ) for analyzing computer images

This course provide working examples.
NeuralnettfNeural Networks Fundamentals using TensorFlow as Example28 horasThis course will give you knowledge in neural networks and generally in machine learning algorithm, deep learning (algorithms and applications).

This training is more focus on fundamentals, but will help you choosing the right technology : TensorFlow, Caffe, Teano, DeepDrive, Keras, etc. The examples are made in TensorFlow.
tpuprogrammingTPU Programming: Building Neural Network Applications on Tensor Processing Units7 horasThe Tensor Processing Unit (TPU) is the architecture which Google has used internally for several years, and is just now becoming available for use by the general public. It includes several optimizations specifically for use in neural networks, including streamlined matrix multiplication, and 8-bit integers instead of 16-bit in order to return appropriate levels of precision.

In this instructor-led, live training, participants will learn how to take advantage of the innovations in TPU processors to maximize the performance of their own AI applications.

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

- Train various types of neural networks on large amounts of data
- Use TPUs to speed up the inference process by up to two orders of magnitude
- Utilize TPUs to process intensive applications such as image search, cloud vision and photos

Audience

- Developers
- Researchers
- Engineers
- Data scientists

Format of the course

- Part lecture, part discussion, exercises and heavy hands-on practice
embeddingprojectorEmbedding Projector: Visualizing Your Training Data14 horasEmbedding 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
tensorflowservingTensorFlow Serving7 horasTensorFlow Serving is a system for serving machine learning (ML) models to production.

In this instructor-led, live training, participants will learn how to configure and use TensorFlow Serving to deploy and manage ML models in a production environment.

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

- Train, export and serve various TensorFlow models
- Test and deploy algorithms using a single architecture and set of APIs
- Extend TensorFlow Serving to serve other types of models beyond TensorFlow models

Audience

- Developers
- Data scientists

Format of the course

- Part lecture, part discussion, exercises and heavy hands-on practice
undnnUnderstanding Deep Neural Networks35 horasThis course begins with giving you conceptual knowledge in neural networks and generally in machine learning algorithm, deep learning (algorithms and applications).

Part-1(40%) of this training is more focus on fundamentals, but will help you choosing the right technology : TensorFlow, Caffe, Theano, DeepDrive, Keras, etc.

Part-2(20%) of this training introduces Theano - a python library that makes writing deep learning models easy.

Part-3(40%) of the training would be extensively based on Tensorflow - 2nd Generation API of Google's open source software library for Deep Learning. The examples and handson would all be made in TensorFlow.

Audience

This course is intended for engineers seeking to use TensorFlow for their Deep Learning projects

After completing this course, delegates will:

-

have a good understanding on deep neural networks(DNN), CNN and RNN

-

understand TensorFlow’s structure and deployment mechanisms

-

be able to carry out installation / production environment / architecture tasks and configuration

-

be able to assess code quality, perform debugging, monitoring

-

be able to implement advanced production like training models, building graphs and logging

Not all the topics would be covered in a public classroom with 35 hours duration due to the vastness of the subject.

The Duration of the complete course will be around 70 hours and not 35 hours.
dlfornlpDeep Learning for NLP (Natural Language Processing)28 horasDeep Learning for NLP allows a machine to learn simple to complex language processing. Among the tasks currently possible are language translation and caption generation for photos. DL (Deep Learning) is a subset of ML (Machine Learning). Python is a popular programming language that contains libraries for Deep Learning for NLP.

In this instructor-led, live training, participants will learn to use Python libraries for NLP (Natural Language Processing) as they create an application that processes a set of pictures and generates captions.

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

- Design and code DL for NLP using Python libraries
- Create Python code that reads a substantially huge collection of pictures and generates keywords
- Create Python Code that generates captions from the detected keywords

Audience

- Programmers with interest in linguistics
- Programmers who seek an understanding of NLP (Natural Language Processing)

Format of the course

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

Próximos Cursos TensorFlow

CursoFechaPrecio del Curso [A distancia / Presencial]
Aprendizaje Profundo con TensorFlow - Caracas - Centro LidoMié, 2018-11-28 09:305,259USD / 6,352USD
Cursos de Fin de Semana de TensorFlow, Capacitación por la Tarde de TensorFlow, TensorFlow boot camp, Clases de TensorFlow, Capacitación de Fin de Semana de TensorFlow, Cursos por la Tarde de TensorFlow, TensorFlow coaching, Instructor de TensorFlow, Capacitador de TensorFlow, TensorFlow con instructor, Cursos de Formación de TensorFlow, TensorFlow en sitio, Cursos Privados de TensorFlow, Clases Particulares de TensorFlow, Capacitación empresarial de TensorFlow, Talleres para empresas de TensorFlow, Cursos en linea de TensorFlow, Programas de capacitación de TensorFlow, Clases de TensorFlow

Promociones

Curso Ubicación Fecha Precio del Curso [A distancia / Presencial]
Fundamentos de Haskell Caracas - Centro Lido Jue, 2018-09-27 09:30 2,743USD / 3,823USD
Docker for Developers and System Administrators Caracas - Centro Lido Jue, 2018-10-11 09:30 2,743USD / 3,823USD
Gestión de Reglas de Negocios (BRMS) con Drools Caracas - Centro Lido Mar, 2018-10-16 09:30 1,796USD / 2,862USD
Drupal y Solr Caracas - Centro Lido Mar, 2018-10-16 09:30 2,743USD / 3,823USD
Marco de Arquitectura Unificado de OMG (UAF) Caracas - Centro Lido Lun, 2018-11-05 09:30 6,629USD / 7,747USD

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