Deep Learning Training Courses

Deep Learning Training

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

Subcategories

Deep Learning Course Outlines

Code Name Duration Overview
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 Machine Learning and Recursive Neural Networks (RNN) basics NN and RNN Backprogation Long short-term memory (LSTM) TensorFlow Basics Creation, Initializing, Saving, and Restoring TensorFlow variables Feeding, Reading and Preloading TensorFlow Data How to use TensorFlow infrastructure to train models at scale Visualizing and Evaluating models with TensorBoard TensorFlow Mechanics 101 Prepare the Data Download Inputs and Placeholders Build the Graph Inference Loss Training Train the Model The Graph The Session Train Loop Evaluate the Model Build the Eval Graph Eval Output Advanced Usage Threading and Queues Distributed TensorFlow Writing Documentation and Sharing your Model Customizing Data Readers Using GPUs¹ Manipulating TensorFlow Model Files TensorFlow Serving Introduction Basic Serving Tutorial Advanced Serving Tutorial Serving Inception Model Tutorial ¹ The Advanced Usage topic, “Using GPUs”, is not available as a part of a remote course. This module can be delivered during classroom-based courses, but only by prior agreement, and only if both the trainer and all participants have laptops with supported NVIDIA GPUs, with 64-bit Linux installed (not provided by NobleProg). NobleProg cannot guarantee the availability of trainers with the required hardware.
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. Deep Learning vs Machine Learning vs Other Methods When Deep Learning is suitable Limits of Deep Learning Comparing accuracy and cost of different methods Methods Overview Nets and  Layers Forward / Backward: the essential computations of layered compositional models. Loss: the task to be learned is defined by the loss. Solver: the solver coordinates model optimization. Layer Catalogue: the layer is the fundamental unit of modeling and computation Convolution​ Methods and models Backprop, modular models Logsum module RBF Net MAP/MLE loss Parameter Space Transforms Convolutional Module Gradient-Based Learning  Energy for inference, Objective for learning PCA; NLL:  Latent Variable Models Probabilistic LVM Loss Function Detection with Fast R-CNN Sequences with LSTMs and Vision + Language with LRCN Pixelwise prediction with FCNs Framework design and future Tools Caffe Tensorflow R Matlab Others...
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 Installation Docker Ubuntu RHEL / CentOS / Fedora installation Windows Caffe Overview Nets, Layers, and Blobs: the anatomy of a Caffe model. Forward / Backward: the essential computations of layered compositional models. Loss: the task to be learned is defined by the loss. Solver: the solver coordinates model optimization. Layer Catalogue: the layer is the fundamental unit of modeling and computation – Caffe’s catalogue includes layers for state-of-the-art models. Interfaces: command line, Python, and MATLAB Caffe. Data: how to caffeinate data for model input. Caffeinated Convolution: how Caffe computes convolutions. New models and new code Detection with Fast R-CNN Sequences with LSTMs and Vision + Language with LRCN Pixelwise prediction with FCNs Framework design and future Examples: MNIST    
dladv Advanced Deep Learning 28 hours Machine Learning Limitations Machine Learning, Non-linear mappings Neural Networks Non-Linear Optimization, Stochastic/MiniBatch Gradient Decent Back Propagation Deep Sparse Coding Sparse Autoencoders (SAE) Convolutional Neural Networks (CNNs) Successes: Descriptor Matching Stereo-based Obstacle Avoidance for Robotics Pooling and invariance Visualization/Deconvolutional Networks Recurrent Neural Networks (RNNs) and their optimizaiton Applications to NLP RNNs continued, Hessian-Free Optimization Language analysis: word/sentence vectors, parsing, sentiment analysis, etc. Probabilistic Graphical Models Hopfield Nets, Boltzmann machines, Restricted Boltzmann Machines Hopfield Networks, (Restricted) Bolzmann Machines Deep Belief Nets, Stacked RBMs Applications to NLP , Pose and Activity Recognition in Videos Recent Advances Large-Scale Learning Neural Turing Machines  
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 Machine Learning and Recursive Neural Networks (RNN) basics NN and RNN Backprogation Long short-term memory (LSTM) TensorFlow Basics Creation, Initializing, Saving, and Restoring TensorFlow variables Feeding, Reading and Preloading TensorFlow Data How to use TensorFlow infrastructure to train models at scale Visualizing and Evaluating models with TensorBoard TensorFlow Mechanics 101 Tutorial Files Prepare the Data Download Inputs and Placeholders Build the Graph Inference Loss Training Train the Model The Graph The Session Train Loop Evaluate the Model Build the Eval Graph Eval Output Advanced Usage Threading and Queues Distributed TensorFlow Writing Documentation and Sharing your Model Customizing Data Readers Using GPUs¹ Manipulating TensorFlow Model Files TensorFlow Serving Introduction Basic Serving Tutorial Advanced Serving Tutorial Serving Inception Model Tutorial Convolutional Neural Networks Overview Goals Highlights of the Tutorial Model Architecture Code Organization CIFAR-10 Model Model Inputs Model Prediction Model Training Launching and Training the Model Evaluating a Model Training a Model Using Multiple GPU Cards¹ Placing Variables and Operations on Devices Launching and Training the Model on Multiple GPU cards Deep Learning for MNIST Setup Load MNIST Data Start TensorFlow InteractiveSession Build a Softmax Regression Model Placeholders Variables Predicted Class and Cost Function Train the Model Evaluate the Model Build a Multilayer Convolutional Network Weight Initialization Convolution and Pooling First Convolutional Layer Second Convolutional Layer Densely Connected Layer Readout Layer Train and Evaluate the Model Image Recognition Inception-v3 C++ Java ¹ Topics related to the use of GPUs are not available as a part of a remote course. They can be delivered during classroom-based courses, but only by prior agreement, and only if both the trainer and all participants have laptops with supported NVIDIA GPUs, with 64-bit Linux installed (not provided by NobleProg). NobleProg cannot guarantee the availability of trainers with the required hardware.
Neuralnettf Neural Networks Fundamentals using TensorFlow as Example 28 hours This course will give you knowledge in neural networks and generally in machine learning algorithm,  deep learning (algorithms and applications). This training is more focus on fundamentals, but will help you choosing the right technology : TensorFlow, Caffe, Teano, DeepDrive, Keras, etc. The examples are made in TensorFlow. TensorFlow Basics Creation, Initializing, Saving, and Restoring TensorFlow variables Feeding, Reading and Preloading TensorFlow Data How to use TensorFlow infrastructure to train models at scale Visualizing and Evaluating models with TensorBoard TensorFlow Mechanics Inputs and Placeholders Build the GraphS Inference Loss Training Train the Model The Graph The Session Train Loop Evaluate the Model Build the Eval Graph Eval Output The Perceptron Activation functions The perceptron learning algorithm Binary classification with the perceptron Document classification with the perceptron Limitations of the perceptron From the Perceptron to Support Vector Machines Kernels and the kernel trick Maximum margin classification and support vectors Artificial Neural Networks Nonlinear decision boundaries Feedforward and feedback artificial neural networks Multilayer perceptrons Minimizing the cost function Forward propagation Back propagation Improving the way neural networks learn Convolutional Neural Networks Goals Model Architecture Principles Code Organization Launching and Training the Model Evaluating a Model
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. Backprop, modular models Logsum module RBF Net MAP/MLE loss Parameter Space Transforms Convolutional Module Gradient-Based Learning  Energy for inference, Objective for learning PCA; NLL:  Latent Variable Models Probabilistic LVM Loss Function Handwriting recognition

Upcoming Courses

CourseCourse DateCourse Price [Remote / Classroom]
Introduction to Deep Learning - Rzeszów, Plac Wolności 13Wed, 2017-03-08 09:0018260PLN / 6583PLN
Introduction to Deep Learning - Tarnów ul. Kościuszki 10 Mon, 2017-03-13 09:0018260PLN / 6583PLN
Deep Learning for Vision - Kielce, ul. Warszawska 19Mon, 2017-03-13 09:0028150PLN / 9580PLN
Advanced Deep Learning - Gliwice ul. Karola Marksa 11 Mon, 2017-03-13 09:0040890PLN / 13791PLN
Deep Learning for Vision with Caffe - Zielona Góra, ul. Reja 6Mon, 2017-03-13 09:0024430PLN / 8153PLN

Other regions

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Course Discounts

Course Venue Course Date Course Price [Remote / Classroom]
Node.js Kraków, ul. Rzemieślnicza 1 Thu, 2017-02-23 09:00 3861PLN / 2731PLN
Cassandra for Developers Łódź, ul. Tatrzańska 11 Mon, 2017-02-27 09:00 17117PLN / 6087PLN
Microsoft Office Excel - poziom średniozaawansowany Kraków, ul. Rzemieślnicza 1 Mon, 2017-02-27 09:00 1485PLN / 1295PLN
Working with spreadsheet in Microsoft Excel Szczecin, ul. Małopolska 23 Tue, 2017-02-28 09:00 1485PLN / 995PLN
Oracle 11g - Programowanie w PL/SQL - wprowadzenie Zielona Góra, ul. Reja 6 Tue, 2017-02-28 09:00 15048PLN / 5310PLN
DTP (InDesign, Photoshop, Illustrator, Acrobat) Rzeszów, Plac Wolności 13 Mon, 2017-03-06 09:00 5940PLN / 3730PLN
UML in Enterprise Architect (workshops) Bydgoszcz, ul. Dworcowa 94 Mon, 2017-03-06 09:00 5940PLN / 3720PLN
Bezstresowe zarządzanie sobą w czasie - jak mieć zawsze określone cele i być efektywnym w ich realizacji. Olsztyn, ul. Kajki 3/1 Tue, 2017-03-07 09:00 2772PLN / 1340PLN
Understanding Your Brand and Your Business Katowice ul. Opolska 22 Wed, 2017-03-08 09:00 15008PLN / 5598PLN
Visual Basic for Applications (VBA) in Excel - Advanced Gdynia, ul. Ejsmonda 2 Wed, 2017-03-08 09:00 2772PLN / 1974PLN
Programowanie w C# 5.0 z Visual Studio 2012 Lublin, ul. Spadochroniarzy 9 Mon, 2017-03-13 09:00 5940PLN / 2963PLN
Automatyzacja testów za pomocą Selenium Warszawa, ul. Złota 3/11 Mon, 2017-03-20 09:00 7722PLN / 3174PLN
Microsoft Office Excel - poziom średniozaawansowany Poznan, Garbary 100/63 Tue, 2017-03-21 09:00 1485PLN / 895PLN
Object-Oriented Analysis and Design using UML Warszawa, ul. Złota 3/11 Wed, 2017-03-22 09:00 5940PLN / 2580PLN
Programowanie w ASP.NET MVC 5 Gdynia, ul. Ejsmonda 2 Wed, 2017-03-22 09:00 5841PLN / 2673PLN
Introduction to CSS3 Poznan, Garbary 100/63 Wed, 2017-03-22 09:00 1881PLN / 952PLN
Visual Basic for Applications (VBA) in Excel - Advanced Gdynia, ul. Ejsmonda 2 Wed, 2017-03-29 09:00 2772PLN / 1974PLN
Microsoft Office Excel - poziom średniozaawansowany Lublin, ul. Spadochroniarzy 9 Mon, 2017-04-10 09:00 1485PLN / 1195PLN
Six thinking hats by Edward de Bono Gdańsk, ul. Powstańców Warszawskich 45 Thu, 2017-04-13 09:00 5148PLN / 1926PLN
Microsoft Office Excel - poziom średniozaawansowany Olsztyn, ul. Kajki 3/1 Wed, 2017-04-26 09:00 1485PLN / 995PLN
BPMN 2.0 dla Analityków Biznesowych Wroclaw, ul.Ludwika Rydygiera 2a/22 Wed, 2017-05-17 09:00 6200PLN / 2667PLN
Adobe Photoshop Elements Gdańsk, ul. Powstańców Warszawskich 45 Wed, 2017-06-07 09:00 1881PLN / 1327PLN
Adobe Photoshop Elements Gdynia, ul. Ejsmonda 2 Wed, 2017-06-07 09:00 1881PLN / 1327PLN
Drools Rules Administration Wroclaw, ul.Ludwika Rydygiera 2a/22 Wed, 2017-06-14 09:00 21196PLN / 7023PLN

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