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
TensorFlow is an open source software library for deep learning.
Kod  Nazwa  Miejscowość  Czas trwania  Data Kursu  PHP  Cena szkolenia [Zdalne / Stacjonarne] 

undnn  Understanding Deep Neural Networks  Białystok, ul. Malmeda 1  35 hours  pon., 20180205 09:00  70000PLN / 22962PLN  
mlbankingpython_  Machine Learning for Banking (with Python)  Białystok, ul. Malmeda 1  21 hours  wt., 20180206 09:00  15820PLN / 5844PLN  
tsflw2v  Natural Language Processing with TensorFlow  Białystok, ul. Malmeda 1  35 hours  pon., 20180219 09:00  33600PLN / 11932PLN  
mlbankingr  Machine Learning for Banking (with R)  Białystok, ul. Malmeda 1  28 hours  wt., 20180220 09:00  15820PLN / 6194PLN  
dlv  Deep Learning for Vision  Białystok, ul. Malmeda 1  21 hours  wt., 20180320 09:00  28150PLN / 9580PLN  
tfir  TensorFlow for Image Recognition  Białystok, ul. Malmeda 1  28 hours  wt., 20180327 09:00  25020PLN / 8982PLN  
undnn  Understanding Deep Neural Networks  Białystok, ul. Malmeda 1  35 hours  pon., 20180402 09:00  70000PLN / 22962PLN  
mlbankingpython_  Machine Learning for Banking (with Python)  Białystok, ul. Malmeda 1  21 hours  pon., 20180409 09:00  15820PLN / 5844PLN  
tsflw2v  Natural Language Processing with TensorFlow  Białystok, ul. Malmeda 1  35 hours  pon., 20180416 09:00  33600PLN / 11932PLN  
Neuralnettf  Neural Networks Fundamentals using TensorFlow as Example  Białystok, ul. Malmeda 1  28 hours  wt., 20180417 09:00  30980PLN / 10788PLN  
mlbankingr  Machine Learning for Banking (with R)  Białystok, ul. Malmeda 1  28 hours  pon., 20180423 09:00  15820PLN / 6194PLN  
tf101  Deep Learning with TensorFlow  Białystok, ul. Malmeda 1  21 hours  wt., 20180424 09:00  13900PLN / 5262PLN  
dlv  Deep Learning for Vision  Białystok, ul. Malmeda 1  21 hours  pon., 20180514 09:00  28150PLN / 9580PLN  
tfir  TensorFlow for Image Recognition  Białystok, ul. Malmeda 1  28 hours  pon., 20180521 09:00  25020PLN / 8982PLN  
mlbankingpython_  Machine Learning for Banking (with Python)  Białystok, ul. Malmeda 1  21 hours  pon., 20180604 09:00  15820PLN / 5844PLN  
undnn  Understanding Deep Neural Networks  Białystok, ul. Malmeda 1  35 hours  pon., 20180611 09:00  70000PLN / 22962PLN  
Neuralnettf  Neural Networks Fundamentals using TensorFlow as Example  Białystok, ul. Malmeda 1  28 hours  pon., 20180611 09:00  30980PLN / 10788PLN  
tsflw2v  Natural Language Processing with TensorFlow  Białystok, ul. Malmeda 1  35 hours  pon., 20180611 09:00  33600PLN / 11932PLN  
tf101  Deep Learning with TensorFlow  Białystok, ul. Malmeda 1  21 hours  pon., 20180618 09:00  13900PLN / 5262PLN  
mlbankingr  Machine Learning for Banking (with R)  Białystok, ul. Malmeda 1  28 hours  wt., 20180619 09:00  15820PLN / 6194PLN  
dlv  Deep Learning for Vision  Białystok, ul. Malmeda 1  21 hours  wt., 20180710 09:00  28150PLN / 9580PLN  
tfir  TensorFlow for Image Recognition  Białystok, ul. Malmeda 1  28 hours  pon., 20180716 09:00  25020PLN / 8982PLN 
Kod  Nazwa  Czas trwania  Spis treści 

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. AudienceThis course is intended for engineers seeking to use TensorFlow for their Deep Learning projects After completing this course, delegates will:
Machine Learning and Recursive Neural Networks (RNN) basics
TensorFlow Basics
TensorFlow Mechanics 101
Advanced Usage
TensorFlow Serving
¹ The Advanced Usage topic, “Using GPUs”, is not available as a part of a remote course. This module can be delivered during classroombased courses, but only by prior agreement, and only if both the trainer and all participants have laptops with supported NVIDIA GPUs, with 64bit Linux installed (not provided by NobleProg). NobleProg cannot guarantee the availability of trainers with the required hardware. 
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:
Machine Learning and Recursive Neural Networks (RNN) basics
TensorFlow Basics
TensorFlow Mechanics 101
Advanced Usage
TensorFlow Serving
Convolutional Neural Networks
Deep Learning for MNIST
Image Recognition
¹ Topics related to the use of GPUs are not available as a part of a remote course. They can be delivered during classroombased courses, but only by prior agreement, and only if both the trainer and all participants have laptops with supported NVIDIA GPUs, with 64bit Linux installed (not provided by NobleProg). NobleProg cannot guarantee the availability of trainers with the required hardware. 
dlv  Deep Learning for Vision  21 hours 
AudienceThis 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
Methods Overview
Methods and models
Tools

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
TensorFlow Mechanics
The Perceptron
From the Perceptron to Support Vector Machines
Artificial Neural Networks
Convolutional Neural Networks

datamodeling  Pattern Recognition  35 hours 
This course provides an introduction into the field of pattern recognition and machine learning. It touches on practical applications in statistics, computer science, signal processing, computer vision, data mining, and bioinformatics. The course is interactive and includes plenty of handson exercises, instructor feedback, and testing of knowledge and skills acquired. Audience
Introduction Probability theory, model selection, decision and information theory Probability distributions Linear models for regression and classification Neural networks Kernel methods Sparse kernel machines Graphical models Mixture models and EM Approximate inference Sampling methods Continuous latent variables Sequential data Combining models

tsflw2v  Natural Language Processing with TensorFlow  35 hours 
TensorFlow™ is an open source software library for numerical computation using data flow graphs. SyntaxNet is a neuralnetwork Natural Language Processing framework for TensorFlow. Word2Vec is used for learning vector representations of words, called "word embeddings". Word2vec is a particularly computationallyefficient predictive model for learning word embeddings from raw text. It comes in two flavors, the Continuous BagofWords model (CBOW) and the SkipGram 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:
Getting Started
TensorFlow Basics
TensorFlow Mechanics 101
Advanced Usage
TensorFlow Serving
Getting Started with SyntaxNet
Building an NLP Pipeline with SyntaxNet
Vector Representations of Words

tpuprogramming  TPU Programming: Building Neural Network Applications on Tensor Processing Units  7 hours 
The 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 8bit integers instead of 16bit in order to return appropriate levels of precision. In this instructorled, 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:
Audience
Format of the course
To request a customized course outline for this training, please contact us. 
embeddingprojector  Embedding Projector: Visualizing your Training Data  14 hours 
Embedding Projector is an opensource web application for visualizing the data used to train machine learning systems. Created by Google, it is part of TensorFlow. This instructorled, 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:
Audience
Format of the course
To request a customized course outline for this training, please contact us. 
tensorflowserving  TensorFlow Serving  7 hours 
TensorFlow Serving is a system for serving machine learning (ML) models to production. In this instructorled, 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:
Audience
Format of the course
To request a customized course outline for this training, please contact us. 
mlbankingr  Machine Learning for Banking (with R)  28 hours 
In this instructorled, live training, participants will learn how to apply machine learning techniques and tools for solving realworld problems in the banking industry. R will be used as the programming language. Participants first learn the key principles, then put their knowledge into practice by building their own machine learning models and using them to complete a number of live projects. Audience
Format of the course
Introduction
Different Types of Machine Learning
Machine Learning Languages and Toolsets
Machine Learning Case Studies
Introduction to R
How to Load Machine Learning Data
Modeling Business Decisions with Supervised Learning
Regression Analysis
Classification
Handson: Building an Estimation Model
Evaluating the performance of Machine Learning Algorithms
Modeling Business Decisions with Unsupervised Learning
Handson: Building a Recommendation System
Extending your company's capabilities
Closing Remarks 
mlbankingpython_  Machine Learning for Banking (with Python)  21 hours 
In this instructorled, live training, participants will learn how to apply machine learning techniques and tools for solving realworld problems in the banking industry. Python will be used as the programming language. Participants first learn the key principles, then put their knowledge into practice by building their own machine learning models and using them to complete a number of team projects. Audience
Format of the course
Introduction
Different Types of Machine Learning
Machine Learning Languages and Toolsets
Machine Learning Case Studies
Handson: Python for Machine Learning
How to Load Machine Learning Data
Modeling Business Decisions with Supervised Learning
Regression Analysis
Classification
Handson: Building an Estimation Model
Evaluating the performance of Machine Learning Algorithms
Modeling Business Decisions with Unsupervised Learning
Handson: Building a Recommendation System
Extending your company's capabilities
Closing Remarks 
undnn  Understanding Deep Neural Networks  35 hours 
This course begins with giving you conceptual knowledge in neural networks and generally in machine learning algorithm, deep learning (algorithms and applications). Part1(40%) of this training is more focus on fundamentals, but will help you choosing the right technology : TensorFlow, Caffe, Theano, DeepDrive, Keras, etc. Part2(20%) of this training introduces Theano  a python library that makes writing deep learning models easy. Part3(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:
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. Part 1 – Deep Learning and DNN Concepts
Basic Concepts of a Neural Network (Application: multilayer perceptron)
Standard ML / DL Tools A simple presentation with advantages, disadvantages, position in the ecosystem and use is planned.
Convolutional Neural Networks (CNN).
Recurrent Neural Networks (RNN).
Deep Reinforcement Learning.
Part 2 – Theano for Deep Learning Theano Basics
Theano Functions
Training and Optimization of a neural network using Theano
Testing the model
TensorFlow Basics
TensorFlow Mechanics
The Perceptron
From the Perceptron to Support Vector Machines
Artificial Neural Networks
Convolutional Neural Networks
Basic Introductions to be given to the below modules(Brief Introduction to be provided based on time availability): Tensorflow  Advanced Usage

dlfornlp  Deep Learning for NLP (Natural Language Processing)  28 hours 
Deep 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 instructorled, 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:
Audience
Format of the course
Introduction to Deep Learning for NLP Differentiating between the various types of DL models Using pretrained vs trained models Using word embeddings and sentiment analysis to extract meaning from text How Unsupervised Deep Learning works Installing and Setting Up Python Deep Learning libraries Using the Keras DL library on top of TensorFlow to allow Python to create captions Working with Theano (numerical computation library) and TensorFlow (general and linguistics library) to use as extended DL libraries for the purpose of creating captions. Using Keras on top of TensorFlow or Theano to quickly experiment on Deep Learning Creating a simple Deep Learning application in TensorFlow to add captions to a collection of pictures Troubleshooting A word on other (specialized) DL frameworks Deploying your DL application Using GPUs to accelerate DL Closing remarks 