| Kod | Nazwa | Miejscowość | Czas trwania | Data Kursu | PHP | Cena szkolenia [Zdalne / Stacjonarne] |
|---|---|---|---|---|---|---|
| tfir | TensorFlow for Image Recognition | Wrocław, ul.Ludwika Rydygiera 2a/22 | 28 hours | wt., 2017-10-24 09:00 | 25020PLN / 8382PLN | |
| dlv | Deep Learning for Vision | Wrocław, ul.Ludwika Rydygiera 2a/22 | 21 hours | pon., 2017-11-06 09:00 | 28150PLN / 9130PLN | |
| Neuralnettf | Neural Networks Fundamentals using TensorFlow as Example | Wrocław, ul.Ludwika Rydygiera 2a/22 | 28 hours | wt., 2017-11-07 09:00 | 30980PLN / 10188PLN |
| 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 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. |
| 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 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 |
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 hands-on 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 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:
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 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:
Audience
Format of the course
To request a customized course outline for this training, please contact us. |
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