The topic is very interesting

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

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

Code | Name | Duration | Overview |
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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 |

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. Getting Started Quickstart: Running Examples and DL4J in Your Projects Comprehensive Setup Guide Convolutional Neural Networks Convolutional Net Introduction Images Are 4-D Tensors? ConvNet Definition How Convolutional Nets Work Maxpooling/Downsampling DL4J Code Sample Other Resources Datasets Datasets and Machine Learning Custom Datasets CSV Data Uploads Scaleout Iterative Reduce Defined Multiprocessor / Clustering Running Worker Nodes Advanced DL2J Build Locally From Master Use the Maven Build Tool Vectorize Data With Canova Build a Data Pipeline Run Benchmarks Configure DL4J in Ivy, Gradle, SBT etc Find a DL4J Class or Method Save and Load Models Interpret Neural Net Output Visualize Data with t-SNE Swap CPUs for GPUs Customize an Image Pipeline Perform Regression With Neural Nets Troubleshoot Training & Select Network Hyperparameters Visualize, Monitor and Debug Network Learning Speed Up Spark With Native Binaries Build a Recommendation Engine With DL4J Use Recurrent Networks in DL4J Build Complex Network Architectures with Computation Graph Train Networks using Early Stopping Download Snapshots With Maven Customize a Loss Function |

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. Getting Started DL4J Examples in a Few Easy Steps Using DL4J In Your Own Projects: Configuring the POM.xml File Word2Vec Introduction Neural Word Embeddings Amusing Word2vec Results the Code Anatomy of Word2Vec Setup, Load and Train A Code Example Troubleshooting & Tuning Word2Vec Word2vec Use Cases Foreign Languages GloVe (Global Vectors) & Doc2Vec |

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 Getting Started Setup and Installation 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 GPUs Manipulating TensorFlow Model Files TensorFlow Serving Introduction Basic Serving Tutorial Advanced Serving Tutorial Serving Inception Model Tutorial Getting Started with SyntaxNet Parsing from Standard Input Annotating a Corpus Configuring the Python Scripts Building an NLP Pipeline with SyntaxNet Obtaining Data Part-of-Speech Tagging Training the SyntaxNet POS Tagger Preprocessing with the Tagger Dependency Parsing: Transition-Based Parsing Training a Parser Step 1: Local Pretraining Training a Parser Step 2: Global Training Vector Representations of Words Motivation: Why Learn word embeddings? Scaling up with Noise-Contrastive Training The Skip-gram Model Building the Graph Training the Model Visualizing the Learned Embeddings Evaluating Embeddings: Analogical Reasoning Optimizing the Implementation |

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... |

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 |

mldt | Machine Learning and Deep Learning | 21 hours | This course covers AI (emphasizing Machine Learning and Deep Learning)Machine learning Introduction to Machine Learning Applications of machine learning Supervised Versus Unsupervised Learning Machine Learning Algorithms Regression Classification Clustering Recommender System Anomaly Detection Reinforcement Learning Regression Simple & Multiple Regression Least Square Method Estimating the Coefficients Assessing the Accuracy of the Coefficient Estimates Assessing the Accuracy of the Model Post Estimation Analysis Other Considerations in the Regression Models Qualitative Predictors Extensions of the Linear Models Potential Problems Bias-variance trade off [under-fitting/over-fitting] for regression models Resampling Methods Cross-Validation The Validation Set Approach Leave-One-Out Cross-Validation k-Fold Cross-Validation Bias-Variance Trade-Off for k-Fold The Bootstrap Model Selection and Regularization Subset Selection [Best Subset Selection, Stepwise Selection, Choosing the Optimal Model] Shrinkage Methods/ Regularization [Ridge Regression, Lasso & Elastic Net] Selecting the Tuning Parameter Dimension Reduction Methods Principal Components Regression Partial Least Squares Classification Logistic Regression The Logistic Model cost function Estimating the Coefficients Making Predictions Odds Ratio Performance Evaluation Matrices [Sensitivity/Specificity/PPV/NPV, Precision, ROC curve etc.] Multiple Logistic Regression Logistic Regression for >2 Response Classes Regularized Logistic Regression Linear Discriminant Analysis Using Bayes’ Theorem for Classification Linear Discriminant Analysis for p=1 Linear Discriminant Analysis for p >1 Quadratic Discriminant Analysis K-Nearest Neighbors Classification with Non-linear Decision Boundaries Support Vector Machines Optimization Objective The Maximal Margin Classifier Kernels One-Versus-One Classification One-Versus-All Classification Comparison of Classification Methods Introduction to Deep Learning ANN Structure Biological neurons and artificial neurons Non-linear Hypothesis Model Representation Examples & Intuitions Transfer Function/ Activation Functions Typical classes of network architectures Feed forward ANN. Structures of Multi-layer feed forward networks Back propagation algorithm Back propagation - training and convergence Functional approximation with back propagation Practical and design issues of back propagation learning Deep Learning Artificial Intelligence & Deep Learning Softmax Regression Self-Taught Learning Deep Networks Demos and Applications Lab: Getting Started with R Introduction to R Basic Commands & Libraries Data Manipulation Importing & Exporting data Graphical and Numerical Summaries Writing functions Regression Simple & Multiple Linear Regression Interaction Terms Non-linear Transformations Dummy variable regression Cross-Validation and the Bootstrap Subset selection methods Penalization [Ridge, Lasso, Elastic Net] Classification Logistic Regression, LDA, QDA, and KNN, Resampling & Regularization Support Vector Machine Resampling & Regularization Artificial Neural Network Deep Learning Note: For ML algorithms, case studies will be used to discuss their application, advantages & potential issues. Analysis of different data sets will be performed using R |

deepmclrg | Machine Learning & Deep Learning with Python and R | 14 hours | MACHINE LEARNING 1: Introducing Machine Learning The origins of machine learning Uses and abuses of machine learning Ethical considerations How do machines learn? Abstraction and knowledge representation Generalization Assessing the success of learning Steps to apply machine learning to your data Choosing a machine learning algorithm Thinking about the input data Thinking about types of machine learning algorithms Matching your data to an appropriate algorithm Using R for machine learning Installing and loading R packages Installing an R package Installing a package using the point-and-click interface Loading an R package Summary 2: Managing and Understanding Data R data structures Vectors Factors Lists Data frames Matrixes and arrays Managing data with R Saving and loading R data structures Importing and saving data from CSV files Importing data from SQL databases Exploring and understanding data Exploring the structure of data Exploring numeric variables Measuring the central tendency – mean and median Measuring spread – quartiles and the five-number summary Visualizing numeric variables – boxplots Visualizing numeric variables – histograms Understanding numeric data – uniform and normal distributions Measuring spread – variance and standard deviation Exploring categorical variables Measuring the central tendency – the mode Exploring relationships between variables Visualizing relationships – scatterplots Examining relationships – two-way cross-tabulations Summary 3: Lazy Learning – Classification Using Nearest Neighbors Understanding classification using nearest neighbors The kNN algorithm Calculating distance Choosing an appropriate k Preparing data for use with kNN Why is the kNN algorithm lazy? Diagnosing breast cancer with the kNN algorithm Step 1 – collecting data Step 2 – exploring and preparing the data Transformation – normalizing numeric data Data preparation – creating training and test datasets Step 3 – training a model on the data Step 4 – evaluating model performance Step 5 – improving model performance Transformation – z-score standardization Testing alternative values of k Summary 4: Probabilistic Learning – Classification Using Naive Bayes Understanding naive Bayes Basic concepts of Bayesian methods Probability Joint probability Conditional probability with Bayes' theorem The naive Bayes algorithm The naive Bayes classification The Laplace estimator Using numeric features with naive Bayes Example – filtering mobile phone spam with the naive Bayes algorithm Step 1 – collecting data Step 2 – exploring and preparing the data Data preparation – processing text data for analysis Data preparation – creating training and test datasets Visualizing text data – word clouds Data preparation – creating indicator features for frequent words Step 3 – training a model on the data Step 4 – evaluating model performance Step 5 – improving model performance Summary 5: Divide and Conquer – Classification Using Decision Trees and Rules Understanding decision trees Divide and conquer The C5.0 decision tree algorithm Choosing the best split Pruning the decision tree Example – identifying risky bank loans using C5.0 decision trees Step 1 – collecting data Step 2 – exploring and preparing the data Data preparation – creating random training and test datasets Step 3 – training a model on the data Step 4 – evaluating model performance Step 5 – improving model performance Boosting the accuracy of decision trees Making some mistakes more costly than others Understanding classification rules Separate and conquer The One Rule algorithm The RIPPER algorithm Rules from decision trees Example – identifying poisonous mushrooms with rule learners Step 1 – collecting data Step 2 – exploring and preparing the data Step 3 – training a model on the data Step 4 – evaluating model performance Step 5 – improving model performance Summary 6: Forecasting Numeric Data – Regression Methods Understanding regression Simple linear regression Ordinary least squares estimation Correlations Multiple linear regression Example – predicting medical expenses using linear regression Step 1 – collecting data Step 2 – exploring and preparing the data Exploring relationships among features – the correlation matrix Visualizing relationships among features – the scatterplot matrix Step 3 – training a model on the data Step 4 – evaluating model performance Step 5 – improving model performance Model specification – adding non-linear relationships Transformation – converting a numeric variable to a binary indicator Model specification – adding interaction effects Putting it all together – an improved regression model Understanding regression trees and model trees Adding regression to trees Example – estimating the quality of wines with regression trees and model trees Step 1 – collecting data Step 2 – exploring and preparing the data Step 3 – training a model on the data Visualizing decision trees Step 4 – evaluating model performance Measuring performance with mean absolute error Step 5 – improving model performance Summary 7: Black Box Methods – Neural Networks and Support Vector Machines Understanding neural networks From biological to artificial neurons Activation functions Network topology The number of layers The direction of information travel The number of nodes in each layer Training neural networks with backpropagation Modeling the strength of concrete with ANNs Step 1 – collecting data Step 2 – exploring and preparing the data Step 3 – training a model on the data Step 4 – evaluating model performance Step 5 – improving model performance Understanding Support Vector Machines Classification with hyperplanes Finding the maximum margin The case of linearly separable data The case of non-linearly separable data Using kernels for non-linear spaces Performing OCR with SVMs Step 1 – collecting data Step 2 – exploring and preparing the data Step 3 – training a model on the data Step 4 – evaluating model performance Step 5 – improving model performance Summary 8: Finding Patterns – Market Basket Analysis Using Association Rules Understanding association rules The Apriori algorithm for association rule learning Measuring rule interest – support and confidence Building a set of rules with the Apriori principle Example – identifying frequently purchased groceries with association rules Step 1 – collecting data Step 2 – exploring and preparing the data Data preparation – creating a sparse matrix for transaction data Visualizing item support – item frequency plots Visualizing transaction data – plotting the sparse matrix Step 3 – training a model on the data Step 4 – evaluating model performance Step 5 – improving model performance Sorting the set of association rules Taking subsets of association rules Saving association rules to a file or data frame Summary 9: Finding Groups of Data – Clustering with k-means Understanding clustering Clustering as a machine learning task The k-means algorithm for clustering Using distance to assign and update clusters Choosing the appropriate number of clusters Finding teen market segments using k-means clustering Step 1 – collecting data Step 2 – exploring and preparing the data Data preparation – dummy coding missing values Data preparation – imputing missing values Step 3 – training a model on the data Step 4 – evaluating model performance Step 5 – improving model performance Summary 10: Evaluating Model Performance Measuring performance for classification Working with classification prediction data in R A closer look at confusion matrices Using confusion matrices to measure performance Beyond accuracy – other measures of performance The kappa statistic Sensitivity and specificity Precision and recall The F-measure Visualizing performance tradeoffs ROC curves Estimating future performance The holdout method Cross-validation Bootstrap sampling Summary 11: Improving Model Performance Tuning stock models for better performance Using caret for automated parameter tuning Creating a simple tuned model Customizing the tuning process Improving model performance with meta-learning Understanding ensembles Bagging Boosting Random forests Training random forests Evaluating random forest performance Summary DEEP LEARNING with R 1: Getting Started with Deep Learning What is deep learning? Conceptual overview of neural networks Deep neural networks R packages for deep learning Setting up reproducible results Neural networks The deepnet package The darch package The H2O package Connecting R and H2O Initializing H2O Linking datasets to an H2O cluster Summary 2: Training a Prediction Model Neural networks in R Building a neural network Generating predictions from a neural network The problem of overfitting data – the consequences explained Use case – build and apply a neural network Summary 3: Preventing Overfitting L1 penalty L1 penalty in action L2 penalty L2 penalty in action Weight decay (L2 penalty in neural networks) Ensembles and model averaging Use case – improving out-of-sample model performance using dropout Summary 4: Identifying Anomalous Data Getting started with unsupervised learning How do auto-encoders work? Regularized auto-encoders Penalized auto-encoders Denoising auto-encoders Training an auto-encoder in R Use case – building and applying an auto-encoder model Fine-tuning auto-encoder models Summary 5: Training Deep Prediction Models Getting started with deep feedforward neural networks Common activation functions – rectifiers, hyperbolic tangent, and maxout Picking hyperparameters Training and predicting new data from a deep neural network Use case – training a deep neural network for automatic classification Working with model results Summary 6: Tuning and Optimizing Models Dealing with missing data Solutions for models with low accuracy Grid search Random search Summary DEEP LEARNING WITH PYTHON I Introduction 1 Welcome Deep Learning The Wrong Way Deep Learning With Python Summary II Background 2 Introduction to Theano What is Theano? How to Install Theano Simple Theano Example Extensions and Wrappers for Theano More Theano Resources Summary 3 Introduction to TensorFlow What is TensorFlow? How to Install TensorFlow Your First Examples in TensorFlow Simple TensorFlow Example More Deep Learning Models Summary 4 Introduction to Keras What is Keras? How to Install Keras Theano and TensorFlow Backends for Keras Build Deep Learning Models with Keras Summary 5 Project: Develop Large Models on GPUs Cheaply In the Cloud Project Overview Setup Your AWS Account Launch Your Server Instance Login, Configure and Run Build and Run Models on AWS Close Your EC2 Instance Tips and Tricks for Using Keras on AWS More Resources For Deep Learning on AWS Summary III Multilayer Perceptrons 6 Crash Course In Multilayer Perceptrons Crash Course Overview Multilayer Perceptrons Neurons Networks of Neurons Training Networks Summary 7 Develop Your First Neural Network With Keras Tutorial Overview Pima Indians Onset of Diabetes Dataset Load Data Define Model Compile Model Fit Model Evaluate Model Tie It All Together Summary 8 Evaluate The Performance of Deep Learning Models Empirically Evaluate Network Configurations Data Splitting Manual k-Fold Cross Validation Summary 9 Use Keras Models With Scikit-Learn For General Machine Learning Overview Evaluate Models with Cross Validation Grid Search Deep Learning Model Parameters Summary 10 Project: Multiclass Classification Of Flower Species Iris Flowers Classification Dataset Import Classes and Functions Initialize Random Number Generator Load The Dataset Encode The Output Variable Define The Neural Network Model Evaluate The Model with k-Fold Cross Validation Summary 11 Project: Binary Classification Of Sonar Returns Sonar Object Classification Dataset Baseline Neural Network Model Performance Improve Performance With Data Preparation Tuning Layers and Neurons in The Model Summary 12 Project: Regression Of Boston House Prices Boston House Price Dataset Develop a Baseline Neural Network Model Lift Performance By Standardizing The Dataset Tune The Neural Network Topology Summary IV Advanced Multilayer Perceptrons and Keras 13 Save Your Models For Later With Serialization Tutorial Overview . Save Your Neural Network Model to JSON Save Your Neural Network Model to YAML Summary 14 Keep The Best Models During Training With Checkpointing Checkpointing Neural Network Models Checkpoint Neural Network Model Improvements Checkpoint Best Neural Network Model Only Loading a Saved Neural Network Model Summary 15 Understand Model Behavior During Training By Plotting History Access Model Training History in Keras Visualize Model Training History in Keras Summary 16 Reduce Overfitting With Dropout Regularization Dropout Regularization For Neural Networks Dropout Regularization in Keras Using Dropout on the Visible Layer Using Dropout on Hidden Layers Tips For Using Dropout Summary 17 Lift Performance With Learning Rate Schedules Learning Rate Schedule For Training Models Ionosphere Classification Dataset Time-Based Learning Rate Schedule Drop-Based Learning Rate Schedule Tips for Using Learning Rate Schedules Summary V Convolutional Neural Networks 18 Crash Course In Convolutional Neural Networks The Case for Convolutional Neural Networks Building Blocks of Convolutional Neural Networks Convolutional Layers Pooling Layers Fully Connected Layers Worked Example Convolutional Neural Networks Best Practices Summary 19 Project: Handwritten Digit Recognition Handwritten Digit Recognition Dataset Loading the MNIST dataset in Keras Baseline Model with Multilayer Perceptrons Simple Convolutional Neural Network for MNIST Larger Convolutional Neural Network for MNIST Summary 20 Improve Model Performance With Image Augmentation Keras Image Augmentation API Point of Comparison for Image Augmentation Feature Standardization ZCA Whitening Random Rotations Random Shifts Random Flips Saving Augmented Images to File Tips For Augmenting Image Data with Keras Summary 21 Project Object Recognition in Photographs Photograph Object Recognition Dataset Loading The CIFAR-10 Dataset in Keras Simple CNN for CIFAR-10 Larger CNN for CIFAR-10 Extensions To Improve Model Performance Summary 22 Project: Predict Sentiment From Movie Reviews Movie Review Sentiment Classification Dataset Load the IMDB Dataset With Keras Word Embeddings Simple Multilayer Perceptron Model One-Dimensional Convolutional Neural Network Summary VI Recurrent Neural Networks 23 Crash Course In Recurrent Neural Networks Support For Sequences in Neural Networks Recurrent Neural Networks Long Short-Term Memory Networks Summary 24 Time Series Prediction with Multilayer Perceptrons Problem Description: Time Series Prediction Multilayer Perceptron Regression Multilayer Perceptron Using the Window Method Summary 25 Time Series Prediction with LSTM Recurrent Neural Networks LSTM Network For Regression LSTM For Regression Using the Window Method LSTM For Regression with Time Steps LSTM With Memory Between Batches Stacked LSTMs With Memory Between Batches Summary 26 Project: Sequence Classification of Movie Reviews Simple LSTM for Sequence Classification LSTM For Sequence Classification With Dropout LSTM and CNN For Sequence Classification Summary 27 Understanding Stateful LSTM Recurrent Neural Networks Problem Description: Learn the Alphabet LSTM for Learning One-Char to One-Char Mapping LSTM for a Feature Window to One-Char Mapping LSTM for a Time Step Window to One-Char Mapping LSTM State Maintained Between Samples Within A Batch Stateful LSTM for a One-Char to One-Char Mapping LSTM with Variable Length Input to One-Char Output Summary 28 Project: Text Generation With Alice in Wonderland Problem Description: Text Generation Develop a Small LSTM Recurrent Neural Network Generating Text with an LSTM Network Larger LSTM Recurrent Neural Network Extension Ideas to Improve the Model Summary |

datamodeling | Pattern Recognition | 35 hours | This course provides an introduction into the field of pattern recognition and machine learning. It also 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, continuous feedback, and testing of knowledge and skills acquired. Audience Data analysts PhD students, researchers and practitioners Introduction Probability theory, model selection, decision and information theory Probability distributions Linear models for regression and classification Neural networks Kernel methods Sparse kernel machines Graphical models Mixture models and EM Approximate inference Sampling methods Continuous latent variables Sequential data Combining models |

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 Introduction to Torch Like NumPy but with CPU and GPU implementation Torch's usage in machine learning, computer vision, signal processing, parallel processing, image, video, audio and networking Installing Torch Linux, Windows, Mac Bitmapi and Docker Installing Torch packages Using the LuaRocks package manager Choosing an IDE for Torch ZeroBrane Studio Eclipse plugin for Lua Working with the Lua scripting language and LuaJIT Lua's integration with C/C++ Lua syntax: datatypes, loops and conditionals, functions, functions, tables, and file i/o. Object orientation and serialization in Torch Coding exercise Loading a dataset in Torch MNIST CIFAR-10, CIFAR-100 Imagenet Machine Learning in Torch Deep Learning Manual feature extraction vs convolutional networks Supervised and Unsupervised Learning Building a neural network with Torch N-dimensional arrays Image analysis with Torch Image package The Tensor library Working with the REPL interpreter Working with databases Networking and Torch GPU support in Torch Integrating Torch C, Python, and others Embedding Torch iOS and Android Other frameworks and libraries Facebook's optimized deep-learning modules and containers Creating your own package Testing and debugging Releasing your application The future of AI and Torch |

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. Introduction to OpenNN, Machine Learning and Deep Learning Downloading OpenNN Working with Neural Designer Using Neural Designer for descriptive, diagnostic, predictive and prescriptive analytics OpenNN architecture CPU parallelization OpenNN classes Data set, neural network, loss index, training strategy, model selection, testing analysis Vector and matrix templates Building a neural network application Choosing a suitable neural network Formulating the variational problem (loss index) Solving the reduced function optimization problem (training strategy) Working with datasets The data matrix (columns as variables and rows as instances) Learning tasks Function regression Pattern recognition Compiling with QT Creator Integrating, testing and debugging your application The future of neural networks and OpenNN |

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 |

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 |

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. |

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. |

Course | Course Date | Course Price [Remote / Classroom] |
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Introduction to Deep Learning - Manchester, King Street | Mon, 2017-06-12 09:30 | £3900 / £4975 |

Deep Learning for Vision - Portsmouth | Mon, 2017-06-12 09:30 | £3900 / £4350 |

Machine Learning and Deep Learning - Leeds | Tue, 2017-06-13 09:30 | £3900 / £4500 |

Deep Learning for Vision with Caffe - Cardiff | Mon, 2017-06-19 09:30 | £3300 / £4200 |