Local, instructor-led live Deep Learning (DL) training courses demonstrate through hands-on practice the fundamentals and applications of Deep Learning and cover subjects such as deep machine learning, deep structured learning, and hierarchical learning. Deep Learning training is available as "onsite live training" or "remote live training". Onsite live training can be carried out locally on customer premises in India or in NobleProg corporate training centers in India. Remote live training is carried out by way of an interactive, remote desktop. NobleProg -- Your Local Training Provider
Testimonials
It was very interactive and more relaxed and informal than expected. We covered lots of topics in the time and the trainer was always receptive to talking more in detail or more generally about the topics and how they were related. I feel the training has given me the tools to continue learning as opposed to it being a one off session where learning stops once you've finished which is very important given the scale and complexity of the topic.
Jonathan Blease
Course:Artificial Neural Networks, Machine Learning, Deep Thinking
The topic is very interesting.
Wojciech Baranowski
Course:Introduction to Deep Learning
Trainers theoretical knowledge and willingness to solve the problems with the participants after the training.
Grzegorz Mianowski
Course:Introduction to Deep Learning
Topic. Very interesting!.
Piotr
Course:Introduction to Deep Learning
Exercises after each topic were really helpful, despite there were too complicated at the end. In general, the presented material was very interesting and involving! Exercises with image recognition were great.
Dolby Poland Sp. z o.o.
Course:Introduction to Deep Learning
I think that if training would be done in polish it would allow the trainer to share his knowledge more efficient.
Radek
Course:Introduction to Deep Learning
The global overview of deep learning.
Bruno Charbonnier
Course:Advanced Deep Learning
The exercises are sufficiently practical and do not need high knowledge in Python to be done.
Alexandre GIRARD
Course:Advanced Deep Learning
Doing exercises on real examples using Eras. Italy totally understood our expectations about this training.
Paul Kassis
Course:Advanced Deep Learning
The subject. It seemed interesting, but I left knowing not much more than before.
Radoslaw Labedzki
Course:Introduction to Deep Learning
I liked that this course had very interesting subject.
Wojciech Wilk
Course:Introduction to Deep Learning
I really appreciated the crystal clear answers of Chris to our questions.
Léo Dubus
Course:Réseau de Neurones, les Fondamentaux en utilisant TensorFlow comme Exemple
I generally enjoyed the knowledgeable trainer.
Sridhar Voorakkara
Course:Neural Networks Fundamentals using TensorFlow as Example
I was amazed at the standard of this class - I would say that it was university standard.
David Relihan
Course:Neural Networks Fundamentals using TensorFlow as Example
Very good all round overview. Good background into why Tensorflow operates as it does.
Kieran Conboy
Course:Neural Networks Fundamentals using TensorFlow as Example
I liked the opportunities to ask questions and get more in depth explanations of the theory.
Sharon Ruane
Course:Neural Networks Fundamentals using TensorFlow as Example
We have gotten a lot more insight in to the subject matter. Some nice discussion were made with some real subjects within our company.
Sebastiaan Holman
Course:Machine Learning and Deep Learning
The training provided the right foundation that allows us to further to expand on, by showing how theory and practice go hand in hand. It actually got me more interested in the subject than I was before.
Jean-Paul van Tillo
Course:Machine Learning and Deep Learning
I really enjoyed the coverage and depth of topics.
Anirban Basu
Course:Machine Learning and Deep Learning
The deep knowledge of the trainer about the topic.
Sebastian Görg
Course:Introduction to Deep Learning
Very updated approach or CPI (tensor flow, era, learn) to do machine learning.
Paul Lee
Course:TensorFlow for Image Recognition
Very flexible.
Frank Ueltzhöffer
Course:Artificial Neural Networks, Machine Learning and Deep Thinking
I generally enjoyed the flexibility.
Werner Philipp
Course:Artificial Neural Networks, Machine Learning and Deep Thinking
Henry's presentation and discussion sessions were very dense with valuable information and insights. He adapted quickly to suit the setting and requests. Good knowledge of the area, and very personable.
Desmond Chung
Course:Deep Learning for Vision - customised
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
Course:Neural Networks Fundamentals using TensorFlow as Example
I was benefit from topic selection. Style of training. Practice orientation.
Commerzbank AG
Course:Neural Networks Fundamentals using TensorFlow as Example
In-depth coverage of machine learning topics, particularly neural networks. Demystified a lot of the topic.
Sacha Nandlall
Course:Python for Advanced Machine Learning
Paper explanation
Course:Deep Learning with TensorFlow - bespoke
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way of conducting and example given by the trainer
ORANGE POLSKA S.A.
Course:Machine Learning and Deep Learning
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Possibility to discuss the proposed issues yourself
ORANGE POLSKA S.A.
Course:Machine Learning and Deep Learning
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Communication with lecturers
文欣 张
Course:Artificial Neural Networks, Machine Learning, Deep Thinking
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like it all
lisa xie
Course:Artificial Neural Networks, Machine Learning, Deep Thinking
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Big and up-to-date knowledge of leading and practical application examples.
ING Bank Śląski S.A.
Course:Introduction to Deep Learning
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A lot of exercises, very good cooperation with the group.
Janusz Chrobot - ING Bank Śląski S.A.
Course:Introduction to Deep Learning
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work on colaborators,
ING Bank Śląski S.A.
Course:Introduction to Deep Learning
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It was obvious that the enthusiasts of the presented topics were leading. Used interesting examples during exercise.
ING Bank Śląski S.A.
Course:Introduction to Deep Learning
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A wide range of topics covered and substantial knowledge of the leaders.
ING Bank Śląski S.A.; Kamil Kurek Programowanie
Course:Understanding Deep Neural Networks
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Lack
ING Bank Śląski S.A.; Kamil Kurek Programowanie
Course:Understanding Deep Neural Networks
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Big theoretical and practical knowledge of the lecturers. Communicativeness of trainers. During the course, you could ask questions and get satisfactory answers.
Kamil Kurek - ING Bank Śląski S.A.; Kamil Kurek Programowanie
Course:Understanding Deep Neural Networks
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Practical part, where we implemented algorithms. This allowed for a better understanding of the topic.
ING Bank Śląski S.A.; Kamil Kurek Programowanie
Course:Understanding Deep Neural Networks
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exercises and examples implemented on them
Paweł Orzechowski - ING Bank Śląski S.A.; Kamil Kurek Programowanie
Course:Understanding Deep Neural Networks
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Examples and issues discussed.
ING Bank Śląski S.A.; Kamil Kurek Programowanie
Course:Understanding Deep Neural Networks
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Substantive knowledge, commitment, a passionate way of transferring knowledge. Practical examples after a theoretical lecture.
Janusz Chrobot - ING Bank Śląski S.A.; Kamil Kurek Programowanie
Course:Understanding Deep Neural Networks
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Practical exercises prepared by Mr. Maciej
ING Bank Śląski S.A.; Kamil Kurek Programowanie
Course:Understanding Deep Neural Networks
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DL (Deep Learning) Subcategories
DL (Deep Learning) Course Outlines
| Code | Name | Duration | Overview |
|---|---|---|---|
| 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. |
| dsstne | Amazon DSSTNE: Build a Recommendation System | 7 hours | Amazon DSSTNE is an open-source library for training and deploying recommendation models. It allows models with weight matrices that are too large for a single GPU to be trained on a single host. In this instructor-led, live training, participants will learn how to use DSSTNE to build a recommendation application. By the end of this training, participants will be able to: - Train a recommendation model with sparse datasets as input - Scale training and prediction models over multiple GPUs - Spread out computation and storage in a model-parallel fashion - Generate Amazon-like personalized product recommendations - Deploy a production-ready application that can scale at heavy workloads Audience - Developers - Data scientists Format of the course - Part lecture, part discussion, exercises and heavy hands-on practice |
| 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 |
| dlforbankingwithr | Deep Learning for Banking (with R) | 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. R is a popular programming language in the financial industry. It is used in financial applications ranging from core trading programs to risk management systems. In this instructor-led, live training, participants will learn how to implement deep learning models for banking using R 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 R to create deep learning models for banking - Build their own deep learning credit risk model using R Audience - Developers - Data scientists Format of the course - Part lecture, part discussion, exercises and heavy hands-on practice |
| 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 |
| dlfinancewithr | Deep Learning for Finance (with R) | 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. R is a popular programming language in the financial industry. It is used in financial applications ranging from core trading programs to risk management systems. In this instructor-led, live training, participants will learn how to implement deep learning models for finance using R 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 R to create deep learning models for finance - Build their own deep learning stock price prediction model using R Audience - Developers - Data scientists Format of the course - Part lecture, part discussion, exercises and heavy hands-on practice |
| 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 |
| 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. |
| 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 |
| 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 |
| radvml | Advanced Machine Learning with R | 21 hours | In this instructor-led, live training, participants will learn advanced techniques for Machine Learning with R as they step through the creation of a real-world application. By the end of this training, participants will be able to: - Use techniques as hyper-parameter tuning and deep learning - Understand and implement unsupervised learning techniques - Put a model into production for use in a larger application Audience - Developers - Analysts - Data scientists Format of the course - Part lecture, part discussion, exercises and heavy hands-on practice |
| pythonadvml | Python for Advanced Machine Learning | 21 hours | In this instructor-led, live training, participants will learn the most relevant and cutting-edge machine learning techniques in Python as they build a series of demo applications involving image, music, text, and financial data. By the end of this training, participants will be able to: - Implement machine learning algorithms and techniques for solving complex problems - Apply deep learning and semi-supervised learning to applications involving image, music, text, and financial data - Push Python algorithms to their maximum potential - Use libraries and packages such as NumPy and Theano Audience - Developers - Analysts - Data scientists Format of the course - Part lecture, part discussion, exercises and heavy hands-on practice |
| openface | OpenFace: Creating Facial Recognition Systems | 14 hours | OpenFace is Python and Torch based open-source, real-time facial recognition software based on Google's FaceNet research. In this instructor-led, live training, participants will learn how to use OpenFace's components to create and deploy a sample facial recognition application. By the end of this training, participants will be able to: - Work with OpenFace's components, including dlib, OpenVC, Torch, and nn4 to implement face detection, alignment, and transformation - Apply OpenFace to real-world applications such as surveillance, identity verification, virtual reality, gaming, and identifying repeat customers, etc. Audience - Developers - Data scientists Format of the course - Part lecture, part discussion, exercises and heavy hands-on practice |
| t2t | T2T: Creating Sequence to Sequence Models for Generalized Learning | 7 hours | Tensor2Tensor (T2T) is a modular, extensible library for training AI models in different tasks, using different types of training data, for example: image recognition, translation, parsing, image captioning, and speech recognition. It is maintained by the Google Brain team. In this instructor-led, live training, participants will learn how to prepare a deep-learning model to resolve multiple tasks. By the end of this training, participants will be able to: - Install tensor2tensor, select a data set, and train and evaluate an AI model - Customize a development environment using the tools and components included in Tensor2Tensor - Create and use a single model to concurrently learn a number of tasks from multiple domains - Use the model to learn from tasks with a large amount of training data and apply that knowledge to tasks where data is limited - Obtain satisfactory processing results using a single GPU Audience - Developers - Data scientists Format of the course - Part lecture, part discussion, exercises and heavy hands-on practice |
| PaddlePaddle | PaddlePaddle | 21 hours | PaddlePaddle (PArallel Distributed Deep LEarning) is a scalable deep learning platform developed by Baidu. In this instructor-led, live training, participants will learn how to use PaddlePaddle to enable deep learning in their product and service applications. By the end of this training, participants will be able to: - Set up and configure PaddlePaddle - Set up a Convolutional Neural Network (CNN) for image recognition and object detection - Set up a Recurrent Neural Network (RNN) for sentiment analysis - Set up deep learning on recommendation systems to help users find answers - Predict click-through rates (CTR), classify large-scale image sets, perform optical character recognition(OCR), rank searches, detect computer viruses, and implement a recommendation system. 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. |
| 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 as 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. |
| facebooknmt | Facebook NMT: Setting up a Neural Machine Translation System | 7 hours | Facebook NMT (Fairseq) is an open-source sequence-to-sequence learning toolkit created by Facebook 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. |
| intrdplrngrsneuing | Introduction Deep Learning & Neural Networks for Engineers | 21 hours | Artificial intelligence has revolutionized a large number of economic sectors (industry, medicine, communication, etc.) after having upset many scientific fields. Nevertheless, his presentation in the major media is often a fantasy, far removed from what really are the fields of Machine Learning or Deep Learning. The aim of this course is to provide engineers who already have a master's degree in computer tools (including a software programming base) an introduction to Deep Learning as well as to its various fields of specialization and therefore to the main existing network architectures today. If the mathematical bases are recalled during the course, a level of mathematics of type BAC + 2 is recommended for more comfort. It is absolutely possible to ignore the mathematical axis in order to maintain only a "system" vision, but this approach will greatly limit your understanding of the subject. |
| 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 |
| mlentre | Machine Learning Concepts for Entrepreneurs and Managers | 21 hours | This training course is for people that would like to apply Machine Learning in practical applications for their team. The training will not dive into technicalities and revolve around basic concepts and business/operational applications of the same. Target Audience - Investors and AI entrepreneurs - Managers and Engineers whose company is venturing into AI space - Business Analysts & Investors |
| 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. |
| 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 |
| mldt | Machine Learning and Deep Learning | 21 hours | This course covers AI (emphasizing Machine Learning and Deep Learning) |
| 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 to choose the right technology : TensorFlow, Caffe, Teano, DeepDrive, Keras, etc. The examples are made in TensorFlow. |
| 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. |
| caffe | Deep Learning for Vision with Caffe | 21 hours | Caffe is a deep learning framework made with expression, speed, and modularity in mind. This course explores the application of Caffe as a Deep learning framework for image recognition using MNIST as an example Audience This course is suitable for Deep Learning researchers and engineers interested in utilizing Caffe as a framework. After completing this course, delegates will be able to: - understand Caffe’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, implementing layers and logging |
| dladv | Advanced Deep Learning | 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. |
| bspkannmldt | Artificial Neural Networks, Machine Learning and 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. |
| 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 |
Upcoming Deep Learning (DL) Courses
| Course | Course Date | Course Price [Remote / Classroom] |
|---|---|---|
| Amazon DSSTNE: Build a Recommendation System - Gurgaon, Sohna Road-2 - Classroom | Thu, 2018-11-08 09:30 | 50,730INR / 63,830INR |
| Neural computing – Data science - Ghaziabad, Sahibabad | Thu, 2018-11-08 09:30 | 101,461INR / 122,461INR |
| OpenNN: Implementing Neural Networks - Ghaziabad, Indirapuram | Thu, 2018-11-08 09:30 | 101,461INR / 122,461INR |
| Deep Learning for Finance (with R) - Delhi, Karol Bagh - Classroom | Mon, 2018-11-12 09:30 | 202,922INR / 239,922INR |
| Microsoft Cognitive Toolkit 2.x - Agra, Cantt Agra | Mon, 2018-11-12 09:30 | N/A / 178,191INR |
Other regions
Other countries
Consulting
Course Discounts
| Course | Venue | Course Date | Course Price [Remote / Classroom] |
|---|---|---|---|
| Hadoop for Data Analysts | Ghaziabad, Muradnagar - Classroom | Thu, 2018-11-15 09:30 | 91,315INR / 112,315INR |
| Lean Management: Lean Management in Manufacturing | Surat, Dumas Rd | Tue, 2018-11-20 09:30 | 100,446INR / 121,446INR |
| Statistical Quality Analysis | Ghaziabad, Sahibabad | Mon, 2019-01-21 09:30 | 45,657INR / 61,657INR |
| Deep Reinforcement Learning with Python | Meerut, Pallav Puram - Classroom | Mon, 2019-02-11 09:30 | 136,972INR / 162,972INR |
| MATLAB Programming | Mumbai, Bandra Kurla Complex - Classroom | Wed, 2019-03-13 09:30 | 91,315INR / 109,715INR |
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