Knowledgeable trainer
Sridhar Voorakkara - INTEL R&D IRELAND LIMITED
TensorFlow Schulungen
TensorFlow is an open source software library for deep learning.
Erfahrungsberichte
TensorFlow Schulungsübersicht
| Code | Name | Dauer | Übersicht |
|---|---|---|---|
| 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 |
| 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 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 |
| 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. |
| 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 To request a customized course outline for this training, please contact us. |
| 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. |
| 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 |
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