$350 – $500

Bellevue Deep Learning Training | IT Training | Disruptive Technologies | A...

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Instructor Led Online | Video Conference

Bellevue, WA

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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?

1. The components of a deep neural network and how they work together

2. The basic types of deep neural networks (MLP, CNN, RNN, LSTM) and the type of data each is

designed for

3. A working knowledge of vocabulary, concepts, and algorithms used in deep learning

4. How to build:

a. An end-to-end model for recognizing hand-written digit images, using a multi-class Logistic Regression and MLP (Multi-Layered Perceptron)

b. A CNN (Convolution Neural Network) model for improved digit recognition

c. An RNN (Recurrent Neural Network) model to forecast time-series data

d. An LSTM (Long Short Term Memory) model to process sequential text data

What are the pre-requisites?

1. Python programming knowledge

2. Basic machine learning knowledge (especially supervised learning)

3. Basic statistics knowledge (mean, variance, standard deviation, etc.)

4. Linear algebra (vectors, matrices, etc.)

5. 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.

Training Dates

December 18, 2017 - January 18, 2017

Times: Every week, Mon & Thu 7:00pm - 9:00pm (Pacific Standard Time)

Each session will be recorded and the recordings will be shared after each session with students.

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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Instructor Led Online | Video Conference

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Refund Policy

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