TensorFlow Training Courses

TensorFlow Training Courses

Οι τοπικές σειρές μαθημάτων TensorFlow με καθοδήγηση από τους τοπικούς εκπαιδευτές, επιδεικνύουν μέσω διαδραστικής συζήτησης και πρακτικής άσκησης πώς να χρησιμοποιήσουν το σύστημα TensorFlow για να διευκολύνουν την έρευνα στη μηχανική μάθηση και να κάνουν γρήγορο και εύκολο τη μετάβαση από το πρωτότυπο της έρευνας στο σύστημα παραγωγής. Η εκπαίδευση TensorFlow είναι διαθέσιμη ως "onsite live training" ή "remote live training". Η επιτόπια κατάρτιση σε πραγματικό χρόνο μπορεί να πραγματοποιηθεί σε τοπικό επίπεδο στις εγκαταστάσεις του πελάτη Ελλάδα ή σε εταιρικά κέντρα κατάρτισης NobleProg στο Ελλάδα . Η απομακρυσμένη ζωντανή προπόνηση πραγματοποιείται μέσω μιας διαδραστικής, απομακρυσμένης επιφάνειας εργασίας. NobleProg - Ο τοπικός παροχέας εκπαίδευσης

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TensorFlow Course Outlines

Title
Duration
Overview
Title
Duration
Overview
28 hours
Overview
This is a 4 day course introducing AI and it's application. There is an option to have an additional day to undertake an AI project on completion of this course.
28 hours
Overview
DL (Deep Learning) is a subset of ML (Machine Learning).

Python is a popular programming language that contains libraries for Deep Learning for NLP.

Deep Learning for NLP (Natural Language Processing) allows a machine to learn simple to complex language processing. Among the tasks currently possible are language translation and caption generation for photos.

In this instructor-led, live training, participants will learn to use Python libraries for NLP 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.

Format of the course

- Part lecture, part discussion, exercises and heavy hands-on practice
21 hours
Overview
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.
14 hours
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
28 hours
Overview
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 to choose the right technology : TensorFlow, Caffe, Teano, DeepDrive, Keras, etc. The examples are made in TensorFlow.
21 hours
Overview
το

TensorFlow είναι μια δημοφιλής και μηχανική βιβλιοθήκη μάθησης που αναπτύχθηκε από την Google για βαθιά μάθηση, αριθμητική υπολογιστική και μεγάλης κλίμακας μηχανική μάθηση. TensorFlow 2,0, κυκλοφόρησε σε Ιανουαρίου 2019, είναι η νεότερη έκδοση του TensorFlow και περιλαμβάνει βελτιώσεις στην πρόθυμη εκτέλεση, συμβατότητα και τη συνέπεια API.

αυτή η εκπαιδεύτρια, ζωντανή εκπαίδευση (επιτόπου ή απομακρυσμένη) απευθύνεται σε προγραμματιστές και επιστήμονες δεδομένων που επιθυμούν να χρησιμοποιήσουν Tensorflow 2,0 για να χτίσουν προφορείς, τάξεις, γενετική μοντέλα, νευρικά δίκτυα και ούτω καθεξής.

μέχρι το τέλος αυτής της εκπαίδευσης, οι συμμετέχοντες θα είναι σε θέση να:

- Εγκαταστήστε και ρυθμίστε τις παραμέτρους του TensorFlow 2,0.
- κατανοήσουν τα οφέλη του TensorFlow 2,0 πάνω από προηγούμενες εκδόσεις.
- οικοδομήσουμε μοντέλα βαθιάς μάθησης.
- εφαρμογή μιας σύνθετης ταξινόμησης εικόνων.
- Αναπτύξτε ένα μοντέλο βαθιάς μάθησης για το σύννεφο, κινητά και τα συστήματα του Διαδικτύου.

μορφή του μαθήματος

- διαδραστική διάλεξη και συζήτηση.
- πολλές ασκήσεις και εξάσκηση.
- εφαρμογή σε περιβάλλον ζωντανού εργαστηρίου.

επιλογές προσαρμογής μαθήματος

- για να ζητήσετε μια προσαρμοσμένη εκπαίδευση για αυτό το μάθημα, παρακαλούμε επικοινωνήστε μαζί μας για να κανονίσετε.
- για να μάθετε περισσότερα σχετικά με TensorFlow, επισκεφθείτε: https://www.tensorflow.org/
7 hours
Overview
TensorFlow 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
21 hours
Overview
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
28 hours
Overview
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
7 hours
Overview
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:

- 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.

Format of the course

- Part lecture, part discussion, exercises and heavy hands-on practice
35 hours
Overview
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:

- 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
35 hours
Overview
This 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
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