Deep Learning Training Courses

Deep Learning Training

Deep machine learning, deep structured learning, hierarchical learning, DL courses

Client Testimonials

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 - Travix International

Introduction to Deep Learning

The topic is very interesting

Wojciech Baranowski - Dolby Poland Sp. z o.o.

Neural Networks Fundamentals using TensorFlow as Example

Topic selection. Style of training. Practice orientation

Commerzbank AG

Neural Networks Fundamentals using TensorFlow as Example

Knowledgeable trainer

Sridhar Voorakkara - INTEL R&D IRELAND LIMITED

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.

Introduction to Deep Learning

Interesting subject

Wojciech Wilk - Dolby Poland Sp. z o.o.

Advanced Deep Learning

Doing exercises on real examples using Keras. Mihaly totally understood our expectations about this training.

Paul Kassis - OSONES

Neural Networks Fundamentals using TensorFlow as Example

Topic selection. Style of training. Practice orientation

Commerzbank AG

Introduction to Deep Learning

Trainers theoretical knowledge and willingness to solve the problems with the participants after the training

Grzegorz Mianowski - Dolby Poland Sp. z o.o.

Advanced Deep Learning

The exercises are sufficiently practical and do not need a high knowledge in Python to be done.

Alexandre GIRARD - OSONES

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 - INTEL R&D IRELAND LIMITED

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 - INTEL R&D IRELAND LIMITED

Introduction to Deep Learning

Topic. Very interesting!

Piotr - Dolby Poland Sp. z o.o.

Neural Networks Fundamentals using TensorFlow as Example

Very good all round overview.Good background into why Tensorflow operates as it does.

Kieran Conboy - INTEL R&D IRELAND LIMITED

Advanced Deep Learning

The global overview of deep learning

Bruno Charbonnier - OSONES

Machine Learning and Deep Learning

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 - Travix International

TensorFlow for Image Recognition

Very updated approach or api (tensorflow, kera, tflearn) to do machine learning

Paul Lee - Hong Kong Productivity Council

Neural Networks Fundamentals using TensorFlow as Example

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

Introduction to Deep Learning

The deep knowledge of the trainer about the topic.

Sebastian Görg - FANUC Europe Corporation

Machine Learning and Deep Learning

Coverage and depth of topics

Anirban Basu - Travix International

Subcategories

Deep Learning Course Outlines

Code Name Duration Overview
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  
mlbankingpython_ Machine Learning for Banking (with Python) 21 hours 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. 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 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
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.
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.
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
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.
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. 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
dladv Advanced Deep Learning 28 hours
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.
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. 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
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
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: 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
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.
dl4j Mastering Deeplearning4j 21 hours Deeplearning4j is the first commercial-grade, open-source, distributed deep-learning library written for Java and Scala. Integrated with Hadoop and Spark, DL4J is designed to be used in business environments on distributed GPUs and CPUs.   Audience This course is directed at engineers and developers seeking to utilize Deeplearning4j in their projects.   After this course delegates will be able to:
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
singa Mastering Apache SINGA 21 hours SINGA is a general distributed deep learning platform for training big deep learning models over large datasets. It is designed with an intuitive programming model based on the layer abstraction. A variety of popular deep learning models are supported, namely feed-forward models including convolutional neural networks (CNN), energy models like restricted Boltzmann machine (RBM), and recurrent neural networks (RNN). Many built-in layers are provided for users. SINGA architecture is sufficiently flexible to run synchronous, asynchronous and hybrid training frameworks. SINGA also supports different neural net partitioning schemes to parallelize the training of large models, namely partitioning on batch dimension, feature dimension or hybrid partitioning. Audience This course is directed at researchers, engineers and developers seeking to utilize Apache SINGA as a deep learning framework. After completing this course, delegates will: understand SINGA’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  
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
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
embeddingprojector Embedding Projector: Visualizing your Training Data 14 hours 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
dl4jir DeepLearning4J for Image Recognition 21 hours Deeplearning4j is an Open-Source Deep-Learning Software for Java and Scala on Hadoop and Spark. Audience This course is meant for engineers and developers seeking to utilize DeepLearning4J in their image recognition projects.
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
w2vdl4j NLP with Deeplearning4j 14 hours Deeplearning4j is an open-source, distributed deep-learning library written for Java and Scala. Integrated with Hadoop and Spark, DL4J is designed to be used in business environments on distributed GPUs and CPUs. Word2Vec is a method of computing vector representations of words introduced by a team of researchers at Google led by Tomas Mikolov. Audience This course is directed at researchers, engineers and developers seeking to utilize Deeplearning4J to construct Word2Vec models.
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
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: 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
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
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.
tensorflowserving TensorFlow Serving 7 hours 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
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.
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
mldt Machine Learning and Deep Learning 21 hours This course covers AI (emphasizing Machine Learning and Deep Learning)
mlbankingr Machine Learning for Banking (with R) 28 hours 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. 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 Developers Data scientists Banking professionals with a technical background Format of the course Part lecture, part discussion, exercises and heavy hands-on practice

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