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      Bay Area Deep Learning Training [April 7-29, 2018] | IT Training | Disruptive Technologies training | Artificial Intelligence Training | Machine learning training in Granite Bay


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      April 7, 2018

      Saturday  10:00 AM

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      Granite Bay, California

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      Bay Area Deep Learning Training [April 7-29, 2018] | IT Training | Disruptive Technologies training | Artificial Intelligence Training | Machine learning training

      WATCH THE OMNI212 VISION Features & Benefits 8 sessions, each session of 2 hours spread over 4 weeks 16 hours of LIVE Instruction spread over 4 weeks Training material with lab exercises provided Each session is recorded and recordings are provided to students over Microsoft Cloud Each student will have be provided with an AWS (Amazon Web Services) cloud instance to build real-life blockchain applications. Video Conference Details Will be sent after registration and payment Deep Learning Training Course Overview In this course you will learn an intuitive approach to building the complex models that help machines solve real-world problems with human-like intelligence. About this course Machine learning is one of the fastest-growing and most exciting fields out there, and deep learning represents its true bleeding edge. In this course, you’ll develop a clear understanding of the motivation for deep learning, and design intelligent systems that learn from complex and/or large-scale datasets. We’ll show you how to train and optimize basic neural networks, convolutional neural networks, and long short-term memory networks. Complete learning systems in Tensor Flow will be introduced via projects and assignments. You will learn to solve new classes of problems that were once thought prohibitively challenging, and come to better appreciate the complex nature of human intelligence as you solve these same problems effortlessly using deep learning methods. What you will learn in this course?  The components of a deep neural network and how they work together The basic types of deep neural networks (MLP, CNN, RNN, LSTM) and the type of data each is designed for A working knowledge of vocabulary, concepts, and algorithms used in deep learning How to build: An end-to-end model for recognizing hand-written digit images, using a multi-class Logistic Regression and MLP (Multi-Layered Perceptron) A CNN (Convolution Neural Network) model for improved digit recognition An RNN (Recurrent Neural Network) model to forecast time-series data An LSTM (Long Short Term Memory) model to process sequential text data What are the pre-requisites? Python programming knowledge Basic machine learning knowledge (especially supervised learning) Basic statistics knowledge (mean, variance, standard deviation, etc.) Linear algebra (vectors, matrices, etc.) Calculus (differentiation, integration, partial derivatives, etc.) Course Outline From Machine Learning to Deep Learning Understand the historical context and motivation for Deep Learning. Set up a basic supervised classification task and train a black box classifier on it. Train a logistic classifier “by hand”, and using gradient descent (and stochastic gradient descent). Deep Neural Networks Train a simple deep network: Relus, the chain rule, and backpropagation. Effectively regularize a simple deep network. L2 regularization, and dropout. Train a competitive deep network via model exploration and hyperparameter tuning. Convolutional Neural Networks Train a simple convolutional neural net. Explore the design space for convolutional nets. Deep Models for Text and Sequences Train a text embedding model using models like Word2Vec. Reduce the dimensionality of the space using tSNE. Train a LSTM model, and regularize it. Refund Policy 1. There are no refunds.2. If for any reason the course has not been taken, class is cancelled or rescheduled, the payment can be applied towards any future course by Omni212.

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