Szkolenia TensorFlow w pomorskie

TensorFlow Training in pomorskie
TensorFlow is an open source software library for deep learning.

Gdańsk, ul. Powstańców Warszawskich 45

Hotel Amber
ul. Powstańców Warszawskich 45
80-165 Gdańsk
Poland
PL
Gdańsk, ul. Powstańców Warszawskich 45

TensorFlow Course Events - pomorskie

Kod Nazwa Miejscowość Czas trwania Data Kursu PHP Cena szkolenia [Zdalne / Stacjonarne]
dlv Deep Learning for Vision Gdańsk, ul. Powstańców Warszawskich 45 21 hours pon., 2017-02-06 09:00 28150PLN / 9580PLN
dlv Deep Learning for Vision Gdynia, ul. Ejsmonda 2 21 hours wt., 2017-02-07 09:00 28150PLN / 9580PLN
tfir TensorFlow for Image Recognition Gdańsk, ul. Powstańców Warszawskich 45 28 hours wt., 2017-02-07 09:00 25020PLN / 8982PLN
tf101 Deep Learning with TensorFlow Gdańsk, ul. Powstańców Warszawskich 45 21 hours śr., 2017-02-08 09:00 13900PLN / 5262PLN
Neuralnettf Neural Networks Fundamentals using TensorFlow as Example Gdynia, ul. Ejsmonda 2 28 hours wt., 2017-02-14 09:00 30980PLN / 10788PLN
tf101 Deep Learning with TensorFlow Gdynia, ul. Ejsmonda 2 21 hours pon., 2017-02-20 09:00 13900PLN / 5262PLN
tfir TensorFlow for Image Recognition Gdynia, ul. Ejsmonda 2 28 hours wt., 2017-03-14 09:00 25020PLN / 8982PLN
Neuralnettf Neural Networks Fundamentals using TensorFlow as Example Gdańsk, ul. Powstańców Warszawskich 45 28 hours pon., 2017-03-27 09:00 30980PLN / 10788PLN
dlv Deep Learning for Vision Gdańsk, ul. Powstańców Warszawskich 45 21 hours śr., 2017-03-29 09:00 28150PLN / 9580PLN
dlv Deep Learning for Vision Gdynia, ul. Ejsmonda 2 21 hours pon., 2017-04-03 09:00 28150PLN / 9580PLN
tfir TensorFlow for Image Recognition Gdańsk, ul. Powstańców Warszawskich 45 28 hours pon., 2017-04-03 09:00 25020PLN / 8982PLN
Neuralnettf Neural Networks Fundamentals using TensorFlow as Example Gdynia, ul. Ejsmonda 2 28 hours wt., 2017-04-11 09:00 30980PLN / 10788PLN
tf101 Deep Learning with TensorFlow Gdynia, ul. Ejsmonda 2 21 hours wt., 2017-04-25 09:00 13900PLN / 5262PLN
tf101 Deep Learning with TensorFlow Gdańsk, ul. Powstańców Warszawskich 45 21 hours wt., 2017-05-16 09:00 13900PLN / 5262PLN
dlv Deep Learning for Vision Gdańsk, ul. Powstańców Warszawskich 45 21 hours pon., 2017-05-22 09:00 28150PLN / 9580PLN
tfir TensorFlow for Image Recognition Gdańsk, ul. Powstańców Warszawskich 45 28 hours pon., 2017-05-29 09:00 25020PLN / 8982PLN
Neuralnettf Neural Networks Fundamentals using TensorFlow as Example Gdańsk, ul. Powstańców Warszawskich 45 28 hours wt., 2017-05-30 09:00 30980PLN / 10788PLN
tfir TensorFlow for Image Recognition Gdynia, ul. Ejsmonda 2 28 hours wt., 2017-05-30 09:00 25020PLN / 8982PLN
dlv Deep Learning for Vision Gdynia, ul. Ejsmonda 2 21 hours wt., 2017-06-06 09:00 28150PLN / 9580PLN
tf101 Deep Learning with TensorFlow Gdynia, ul. Ejsmonda 2 21 hours pon., 2017-06-26 09:00 13900PLN / 5262PLN
tf101 Deep Learning with TensorFlow Gdańsk, ul. Powstańców Warszawskich 45 21 hours wt., 2017-07-11 09:00 13900PLN / 5262PLN
dlv Deep Learning for Vision Gdańsk, ul. Powstańców Warszawskich 45 21 hours śr., 2017-07-12 09:00 28150PLN / 9580PLN
Neuralnettf Neural Networks Fundamentals using TensorFlow as Example Gdynia, ul. Ejsmonda 2 28 hours wt., 2017-07-18 09:00 30980PLN / 10788PLN
Neuralnettf Neural Networks Fundamentals using TensorFlow as Example Gdańsk, ul. Powstańców Warszawskich 45 28 hours wt., 2017-07-25 09:00 30980PLN / 10788PLN
dlv Deep Learning for Vision Gdynia, ul. Ejsmonda 2 21 hours pon., 2017-07-31 09:00 28150PLN / 9580PLN
tfir TensorFlow for Image Recognition Gdynia, ul. Ejsmonda 2 28 hours wt., 2017-08-01 09:00 25020PLN / 8982PLN
tfir TensorFlow for Image Recognition Gdańsk, ul. Powstańców Warszawskich 45 28 hours wt., 2017-08-01 09:00 25020PLN / 8982PLN
tf101 Deep Learning with TensorFlow Gdynia, ul. Ejsmonda 2 21 hours śr., 2017-08-16 09:00 13900PLN / 5262PLN

Plany Kursów

Kod Nazwa Czas trwania Spis treści
Neuralnettf Neural Networks Fundamentals using TensorFlow as Example 28 hours

This 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.

TensorFlow Basics

 Creation, Initializing, Saving, and Restoring TensorFlow variables

 Feeding, Reading and Preloading TensorFlow Data

 How to use TensorFlow infrastructure to train models at scale

 Visualizing and Evaluating models with TensorBoard

 

TensorFlow Mechanics

 Inputs and Placeholders

 Build the GraphS

o Inference

o Loss

o Training

 Train the Model

o The Graph

o The Session

o Train Loop

 Evaluate the Model

o Build the Eval Graph

o Eval Output

 

The Perceptron

 Activation functions

 The perceptron learning algorithm

 Binary classification with the perceptron

 Document classification with the perceptron

 Limitations of the perceptron

 

From the Perceptron to Support Vector Machines

 Kernels and the kernel trick

 Maximum margin classification and support vectors

 

Artificial Neural Networks

 Nonlinear decision boundaries

 Feedforward and feedback artificial neural networks

 Multilayer perceptrons

 Minimizing the cost function

 Forward propagation

 Back propagation

 Improving the way neural networks learn

 

Convolutional Neural Networks

 Goals

 Model Architecture

 Principles

 Code Organization

 Launching and Training the Model

 Evaluating a Model

tf101 Deep Learning with TensorFlow 21 hours

TensorFlow 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

Machine Learning and Recursive Neural Networks (RNN) basics

  • NN and RNN
  • Backprogation
  • Long short-term memory (LSTM)

TensorFlow Basics

  • Creation, Initializing, Saving, and Restoring TensorFlow variables
  • Feeding, Reading and Preloading TensorFlow Data
  • How to use TensorFlow infrastructure to train models at scale
  • Visualizing and Evaluating models with TensorBoard

TensorFlow Mechanics 101

  • Prepare the Data
    • Download
    • Inputs and Placeholders
  • Build the Graph
    • Inference
    • Loss
    • Training
  • Train the Model
    • The Graph
    • The Session
    • Train Loop
  • Evaluate the Model
    • Build the Eval Graph
    • Eval Output

Advanced Usage

  • Threading and Queues
  • Distributed TensorFlow
  • Writing Documentation and Sharing your Model
  • Customizing Data Readers
  • Using GPUs¹
  • Manipulating TensorFlow Model Files

TensorFlow Serving

  • Introduction
  • Basic Serving Tutorial
  • Advanced Serving Tutorial
  • Serving Inception Model Tutorial

¹ The Advanced Usage topic, “Using GPUs”, is not available as a part of a remote course. This module can be delivered during classroom-based courses, but only by prior agreement, and only if both the trainer and all participants have laptops with supported NVIDIA GPUs, with 64-bit Linux installed (not provided by NobleProg). NobleProg cannot guarantee the availability of trainers with the required hardware.

dlv Deep Learning for Vision 21 hours

Audience

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.

Deep Learning vs Machine Learning vs Other Methods

  • When Deep Learning is suitable
  • Limits of Deep Learning
  • Comparing accuracy and cost of different methods

Methods Overview

  • Nets and  Layers
  • Forward / Backward: the essential computations of layered compositional models.
  • Loss: the task to be learned is defined by the loss.
  • Solver: the solver coordinates model optimization.
  • Layer Catalogue: the layer is the fundamental unit of modeling and computation
  • Convolution​

Methods and models

  • Backprop, modular models
  • Logsum module
  • RBF Net
  • MAP/MLE loss
  • Parameter Space Transforms
  • Convolutional Module
  • Gradient-Based Learning 
  • Energy for inference,
  • Objective for learning
  • PCA; NLL: 
  • Latent Variable Models
  • Probabilistic LVM
  • Loss Function
  • Detection with Fast R-CNN
  • Sequences with LSTMs and Vision + Language with LRCN
  • Pixelwise prediction with FCNs
  • Framework design and future

Tools

  • Caffe
  • Tensorflow
  • R
  • Matlab
  • Others...
tfir TensorFlow for Image Recognition 28 hours

This 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

Machine Learning and Recursive Neural Networks (RNN) basics

  • NN and RNN
  • Backprogation
  • Long short-term memory (LSTM)

TensorFlow Basics

  • Creation, Initializing, Saving, and Restoring TensorFlow variables
  • Feeding, Reading and Preloading TensorFlow Data
  • How to use TensorFlow infrastructure to train models at scale
  • Visualizing and Evaluating models with TensorBoard

TensorFlow Mechanics 101

  • Tutorial Files
  • Prepare the Data
    • Download
    • Inputs and Placeholders
  • Build the Graph
    • Inference
    • Loss
    • Training
  • Train the Model
    • The Graph
    • The Session
    • Train Loop
  • Evaluate the Model
    • Build the Eval Graph
    • Eval Output

Advanced Usage

  • Threading and Queues
  • Distributed TensorFlow
  • Writing Documentation and Sharing your Model
  • Customizing Data Readers
  • Using GPUs¹
  • Manipulating TensorFlow Model Files

TensorFlow Serving

  • Introduction
  • Basic Serving Tutorial
  • Advanced Serving Tutorial
  • Serving Inception Model Tutorial

Convolutional Neural Networks

  • Overview
    • Goals
    • Highlights of the Tutorial
    • Model Architecture
  • Code Organization
  • CIFAR-10 Model
    • Model Inputs
    • Model Prediction
    • Model Training
  • Launching and Training the Model
  • Evaluating a Model
  • Training a Model Using Multiple GPU Cards¹
    • Placing Variables and Operations on Devices
    • Launching and Training the Model on Multiple GPU cards

Deep Learning for MNIST

  • Setup
  • Load MNIST Data
  • Start TensorFlow InteractiveSession
  • Build a Softmax Regression Model
  • Placeholders
  • Variables
  • Predicted Class and Cost Function
  • Train the Model
  • Evaluate the Model
  • Build a Multilayer Convolutional Network
  • Weight Initialization
  • Convolution and Pooling
  • First Convolutional Layer
  • Second Convolutional Layer
  • Densely Connected Layer
  • Readout Layer
  • Train and Evaluate the Model

Image Recognition

  • Inception-v3
    • C++
    • Java

¹ Topics related to the use of GPUs are not available as a part of a remote course. They can be delivered during classroom-based courses, but only by prior agreement, and only if both the trainer and all participants have laptops with supported NVIDIA GPUs, with 64-bit Linux installed (not provided by NobleProg). NobleProg cannot guarantee the availability of trainers with the required hardware.

Other regions

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