Deep Learning Training Courses

Deep Learning Training

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

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Deep Learning Course Outlines

ID Name Duration Overview
289419 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  
287899 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
1267353 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 GPUs Manipulating TensorFlow Model Files TensorFlow Serving Introduction Basic Serving Tutorial Advanced Serving Tutorial Serving Inception Model Tutorial
1406005 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 GPUs 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 Cards 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  
2826385 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    
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