Neural Networks Schulungen
Ein Neuronales Netzwerk (NN) oder Künstliches Neuronales Netzwerk (ANN) ist ein Computerdatenmodell, das bei der Entwicklung von Systemen der Künstlichen Intelligenz (KI) verwendet wird, die "intelligente" Aufgaben ausführen können. Neuronale Netze werden häufig in Machine Learning (ML) -Anwendungen verwendet, die selbst eine Implementierung von AI sind. NobleProg on site live Neural Network-Trainingskurse demonstrieren durch Handson-Praxis, wie man neuronale Netzwerke mit einer Anzahl von meist Open Source-Toolkits und -Bibliotheken baut und wie man die Leistungsfähigkeit moderner Hardware (GPUs) und Optimierungstechniken nutzt, die verteiltes Rechnen und Big Data beinhalten. Unsere Neural Network Kurse basieren auf populären Programmiersprachen wie Python, Java, R Sprache und leistungsstarken Bibliotheken, einschließlich TensorFlow, Torch, Caffe, Theano und mehr. Unsere Kurse im Neuronalen Netz decken sowohl die Theorie als auch die Implementierung ab und verwenden eine Reihe neuronaler Netzwerkimplementierungen wie Deep Neural Networks (DNN), Convolutional Neural Networks (CNN) und Recurrent Neural Networks (RNN). Neural Network Training ist in verschiedenen Formaten verfügbar, einschließlich Live-Training vor Ort und Live-Training mit einem interaktiven Remote-Desktop-Setup. Das Training des lokalen Neuronalen Netzes kann live in den Räumlichkeiten des Kunden oder in lokalen Schulungszentren von NobleProg durchgeführt werden. .
Erfahrungsberichte
Neural Networks Schulungsübersicht
| Code | Name | Dauer | Übersicht |
|---|---|---|---|
| mldt | Machine Learning and Deep Learning | 21 hours | This course covers AI (emphasizing Machine Learning and Deep Learning) |
| 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 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 |
| 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 Data analysts PhD students, researchers and practitioners |
| dlforbankingwithpython | Deep Learning for Banking (with Python) | 28 hours | Machine learning is a branch of Artificial Intelligence wherein computers have the ability to learn without being explicitly programmed. Deep learning is a subfield of machine learning which uses methods based on learning data representations and structures such as neural networks. Python is a high-level programming language famous for its clear syntax and code readability. In this instructor-led, live training, participants will learn how to implement deep learning models for banking using Python as they step through the creation of a deep learning credit risk model. By the end of this training, participants will be able to: Understand the fundamental concepts of deep learning Learn the applications and uses of deep learning in banking Use Python, Keras, and TensorFlow to create deep learning models for banking Build their own deep learning credit risk model using Python Audience Developers Data scientists Format of the course Part lecture, part discussion, exercises and heavy hands-on practice |
| Torch | Torch: Getting started with Machine and Deep Learning | 21 hours | Torch is an open source machine learning library and a scientific computing framework based on the Lua programming language. It provides a development environment for numerics, machine learning, and computer vision, with a particular emphasis on deep learning and convolutional nets. It is one of the fastest and most flexible frameworks for Machine and Deep Learning and is used by companies such as Facebook, Google, Twitter, NVIDIA, AMD, Intel, and many others. In this course we cover the principles of Torch, its unique features, and how it can be applied in real-world applications. We step through numerous hands-on exercises all throughout, demonstrating and practicing the concepts learned. By the end of the course, participants will have a thorough understanding of Torch's underlying features and capabilities as well as its role and contribution within the AI space compared to other frameworks and libraries. Participants will have also received the necessary practice to implement Torch in their own projects. Audience Software developers and programmers wishing to enable Machine and Deep Learning within their applications Format of the course Overview of Machine and Deep Learning In-class coding and integration exercises Test questions sprinkled along the way to check understanding |
| dlforfinancewithpython | Deep Learning for Finance (with Python) | 28 hours | Machine learning is a branch of Artificial Intelligence wherein computers have the ability to learn without being explicitly programmed. Deep learning is a subfield of machine learning which uses methods based on learning data representations and structures such as neural networks. Python is a high-level programming language famous for its clear syntax and code readability. In this instructor-led, live training, participants will learn how to implement deep learning models for finance using Python as they step through the creation of a deep learning stock price prediction model. By the end of this training, participants will be able to: Understand the fundamental concepts of deep learning Learn the applications and uses of deep learning in finance Use Python, Keras, and TensorFlow to create deep learning models for finance Build their own deep learning stock price prediction model using Python Audience Developers Data scientists Format of the course Part lecture, part discussion, exercises and heavy hands-on practice |
| aiint | Ein Überblick über künstliche Intelligenz | 7 hours | This course has been created for managers, solutions architects, innovation officers, CTOs, software architects and anyone who is interested in an overview of applied artificial intelligence and the nearest forecast for its development. |
| OpenNN | OpenNN: Implementing neural networks | 14 hours | OpenNN is an open-source class library written in C++ which implements neural networks, for use in machine learning. In this course we go over the principles of neural networks and use OpenNN to implement a sample application. Audience Software developers and programmers wishing to create Deep Learning applications. Format of the course Lecture and discussion coupled with hands-on exercises. |
| drlpython | Deep Reinforcement Learning with Python | 21 hours | Deep Reinforcement Learning refers to the ability of an "artificial agent" to learn by trial-and-error and rewards-and-punishments. An artificial agent aims to emulate a human's ability to obtain and construct knowledge on its own, directly from raw inputs such as vision. To realize reinforcement learning, deep learning and neural networks are used. Reinforcement learning is different from machine learning and does not rely on supervised and unsupervised learning approaches. In this instructor-led, live training, participants will learn the fundamentals of Deep Reinforcement Learning as they step through the creation of a Deep Learning Agent. By the end of this training, participants will be able to: Understand the key concepts behind Deep Reinforcement Learning and be able to distinguish it from Machine Learning Apply advanced Reinforcement Learning algorithms to solve real-world problems Build a Deep Learning Agent Audience Developers Data Scientists Format of the course Part lecture, part discussion, exercises and heavy hands-on practice |
| rneuralnet | Training Neural Network in R | 14 hours | This course is an introduction to applying neural networks in real world problems using R-project software. |
| opennmt | OpenNMT: Setting up a Neural Machine Translation System | 7 hours | OpenNMT is a full-featured, open-source (MIT) neural machine translation system that utilizes the Torch mathematical toolkit. In this training participants will learn how to set up and use OpenNMT to carry out translation of various sample data sets. The course starts with an overview of neural networks as they apply to machine translation. Participants will carry out live exercises throughout the course to demonstrate their understanding of the concepts learned and get feedback from the instructor. By the end of this training, participants will have the knowledge and practice needed to implement a live OpenNMT solution. Source and target language samples will be pre-arranged per the audience's requirements. Audience Localization specialists with a technical background Global content managers Localization engineers Software developers in charge of implementing global content solutions Format of the course Part lecture, part discussion, heavy hands-on practice |
| neuralnet | Introduction to the use of neural networks | 7 hours | The training is aimed at people who want to learn the basics of neural networks and their applications. |
| Fairsec | Fairsec: Setting up a CNN-based machine translation system | 7 hours | Fairseq is an open-source sequence-to-sequence learning toolkit created by Facebok for use in Neural Machine Translation (NMT). In this training participants will learn how to use Fairseq to carry out translation of sample content. By the end of this training, participants will have the knowledge and practice needed to implement a live Fairseq based machine translation solution. Source and target language content samples can be prepared according to audience's requirements. Audience Localization specialists with a technical background Global content managers Localization engineers Software developers in charge of implementing global content solutions Format of the course Part lecture, part discussion, heavy hands-on practice |
| appliedml | Angewandtes Maschinelles Lernen | 14 hours | Der Übungskurs ist für alle diejenigen gedacht, die "Machine Learning" in praktischen Applikationen anwenden möchten Teilnehmer Dieser Kurs ist für Data Scientists und Statistiker, die Grundkenntnisse in Statistik haben und wissen, wie man R programmiert. Der Schwerpunkt des Kurses liegt auf dem praktischen Aspekt von Daten/Modell-Vorbereitung, Execution, post hoc Analyse und Visualisierung. Das Ziel ist es, den Teilnehmern praktische Kenntnisse im Maschinellen Lernen zu vermitteln. Bereichsspezifische Beispiele erhöhen die Relevanz der Schulung für die Teilnehmer. |
| Fairseq | Fairseq: Setting up a CNN-based machine translation system | 7 hours | Fairseq is an open-source sequence-to-sequence learning toolkit created by Facebok for use in Neural Machine Translation (NMT). In this training participants will learn how to use Fairseq to carry out translation of sample content. By the end of this training, participants will have the knowledge and practice needed to implement a live Fairseq based machine translation solution. Audience Localization specialists with a technical background Global content managers Localization engineers Software developers in charge of implementing global content solutions Format of the course Part lecture, part discussion, heavy hands-on practice Note If you wish to use specific source and target language content, please contact us to arrange. |
| mlintro | Introduction to Machine Learning | 7 hours | This training course is for people that would like to apply basic Machine Learning techniques in practical applications. Audience Data scientists and statisticians that have some familiarity with machine learning and know how to program R. The emphasis of this course is on the practical aspects of data/model preparation, execution, post hoc analysis and visualization. The purpose is to give a practical introduction to machine learning to participants interested in applying the methods at work Sector specific examples are used to make the training relevant to the audience. |
| facebooknmt | Facebook NMT: Setting up a Neural Machine Translation System | 7 hours | Fairseq is an open-source sequence-to-sequence learning toolkit created by Facebok for use in Neural Machine Translation (NMT). In this training participants will learn how to use Fairseq to carry out translation of sample content. By the end of this training, participants will have the knowledge and practice needed to implement a live Fairseq based machine translation solution. Audience Localization specialists with a technical background Global content managers Localization engineers Software developers in charge of implementing global content solutions Format of the course Part lecture, part discussion, heavy hands-on practice Note If you wish to use specific source and target language content, please contact us to arrange. |
| d2dbdpa | From Data to Decision with Big Data and Predictive Analytics | 21 hours | Audience If you try to make sense out of the data you have access to or want to analyse unstructured data available on the net (like Twitter, Linked in, etc...) this course is for you. It is mostly aimed at decision makers and people who need to choose what data is worth collecting and what is worth analyzing. It is not aimed at people configuring the solution, those people will benefit from the big picture though. Delivery Mode During the course delegates will be presented with working examples of mostly open source technologies. Short lectures will be followed by presentation and simple exercises by the participants Content and Software used All software used is updated each time the course is run so we check the newest versions possible. It covers the process from obtaining, formatting, processing and analysing the data, to explain how to automate decision making process with machine learning. |
| 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: 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 |
| MLFWR1 | Machine Learning Fundamentals with R | 14 hours | The aim of this course is to provide a basic proficiency in applying Machine Learning methods in practice. Through the use of the R programming platform and its various libraries, and based on a multitude of practical examples this course teaches how to use the most important building blocks of Machine Learning, how to make data modeling decisions, interpret the outputs of the algorithms and validate the results. Our goal is to give you the skills to understand and use the most fundamental tools from the Machine Learning toolbox confidently and avoid the common pitfalls of Data Sciences applications. |
| MicrosoftCognitiveToolkit | Microsoft Cognitive Toolkit 2.x | 21 hours | Microsoft Cognitive Toolkit 2.x (previously CNTK) is an open-source, commercial-grade toolkit that trains deep learning algorithms to learn like the human brain. According to Microsoft, CNTK can be 5-10x faster than TensorFlow on recurrent networks, and 2 to 3 times faster than TensorFlow for image-related tasks. In this instructor-led, live training, participants will learn how to use Microsoft Cognitive Toolkit to create, train and evaluate deep learning algorithms for use in commercial-grade AI applications involving multiple types of data such data, speech, text, and images. By the end of this training, participants will be able to: Access CNTK as a library from within a Python, C#, or C++ program Use CNTK as a standalone machine learning tool through its own model description language (BrainScript) Use the CNTK model evaluation functionality from a Java program Combine feed-forward DNNs, convolutional nets (CNNs), and recurrent networks (RNNs/LSTMs) Scale computation capacity on CPUs, GPUs and multiple machines Access massive datasets using existing programming languages and algorithms Audience Developers Data scientists Format of the course Part lecture, part discussion, exercises and heavy hands-on practice Note If you wish to customize any part of this training, including the programming language of choice, please contact us to arrange. |
| annmldt | Artificial Neural Networks, Machine Learning, Deep Thinking | 21 hours | Artificial Neural Network is a computational data model used in the development of Artificial Intelligence (AI) systems capable of performing "intelligent" tasks. Neural Networks are commonly used in Machine Learning (ML) applications, which are themselves one implementation of AI. Deep Learning is a subset of ML. |
| snorkel | Snorkel: Rapidly process training data | 7 hours | Snorkel is a system for rapidly creating, modeling, and managing training data. It focuses on accelerating the development of structured or "dark" data extraction applications for domains in which large labeled training sets are not available or easy to obtain. In this instructor-led, live training, participants will learn techniques for extracting value from unstructured data such as text, tables, figures, and images through modeling of training data with Snorkel. By the end of this training, participants will be able to: Programmatically create training sets to enable the labeling of massive training sets Train high-quality end models by first modeling noisy training sets Use Snorkel to implement weak supervision techniques and apply data programming to weakly-supervised machine learning systems Audience Developers Data scientists Format of the course Part lecture, part discussion, exercises and heavy hands-on practice |
| deeplearning1 | Introduction to Deep Learning | 21 hours | This course is general overview for Deep Learning without going too deep into any specific methods. It is suitable for people who want to start using Deep learning to enhance their accuracy of prediction. |
| encogadv | Encog: Advanced Machine Learning | 14 hours | Encog is an open-source machine learning framework for Java and .Net. In this instructor-led, live training, participants will learn advanced machine learning techniques for building accurate neural network predictive models. By the end of this training, participants will be able to: Implement different neural networks optimization techniques to resolve underfitting and overfitting Understand and choose from a number of neural network architectures Implement supervised feed forward and feedback networks Audience Developers Analysts Data scientists Format of the course Part lecture, part discussion, exercises and heavy hands-on practice |
| cntk | Using Computer Network ToolKit (CNTK) | 28 hours | Computer Network ToolKit (CNTK) is Microsoft's Open Source, Multi-machine, Multi-GPU, Highly efficent RNN training machine learning framework for speech, text, and images. Audience This course is directed at engineers and architects aiming to utilize CNTK in their projects. |
| encogintro | Encog: Introduction to Machine Learning | 14 hours | Encog is an open-source machine learning framework for Java and .Net. In this instructor-led, live training, participants will learn how to create various neural network components using ENCOG. Real-world case studies will be discussed and machine language based solutions to these problems will be explored. By the end of this training, participants will be able to: Prepare data for neural networks using the normalization process Implement feed forward networks and propagation training methodologies Implement classification and regression tasks Model and train neural networks using Encog's GUI based workbench Integrate neural network support into real-world applications Audience Developers Analysts Data scientists Format of the course Part lecture, part discussion, exercises and heavy hands-on practice |
| aiintrozero | From Zero to AI | 35 hours | This course is created for people who have no previous experience in probability and statistics. |
| matlabdl | Matlab for Deep Learning | 14 hours | In this instructor-led, live training, participants will learn how to use Matlab to design, build, and visualize a convolutional neural network for image recognition. By the end of this training, participants will be able to: Build a deep learning model Automate data labeling Work with models from Caffe and TensorFlow-Keras Train data using multiple GPUs, the cloud, or clusters Audience Developers Engineers Domain experts Format of the course Part lecture, part discussion, exercises and heavy hands-on practice |
| aiauto | Artificial Intelligence in Automotive | 14 hours | This course covers AI (emphasizing Machine Learning and Deep Learning) in Automotive Industry. It helps to determine which technology can be (potentially) used in multiple situation in a car: from simple automation, image recognition to autonomous decision making. |
| mlbankingpython_ | Machine Learning for Banking (with Python) | 21 hours | Machine Learning is a branch of Artificial Intelligence wherein computers have the ability to learn without being explicitly programmed. Python is a programming language famous for its clear syntax and readability. It offers an excellent collection of well-tested libraries and techniques for developing machine learning applications. In this instructor-led, live training, participants will learn how to apply machine learning techniques and tools for solving real-world problems in the banking industry. 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 Developers Data scientists Format of the course Part lecture, part discussion, exercises and heavy hands-on practice |
| 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. |
| 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). 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. |
Zukünftige Kurse
| Course | Schulungsdatum | Kurspreis (Fernkurs / Schulungsraum) |
|---|---|---|
| Microsoft Cognitive Toolkit 2.x - Hamburg | Mo, 2018-05-07 09:30 | N/A / 5300EUR |
| Torch: Getting started with Machine and Deep Learning - Köln | Mo, 2018-05-07 09:30 | N/A / 6050EUR |
| Encog: Introduction to Machine Learning - Leipzig | Di, 2018-05-08 09:30 | 3500EUR / 4100EUR |
| Deep Learning for Banking (with Python) - Hannover | Mo, 2018-05-14 09:30 | 7000EUR / 8000EUR |
| Angewandtes Maschinelles Lernen - München | Di, 2018-05-15 09:30 | 3500EUR / 4100EUR |
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