Advanced Deep Learning Training Course 2017-08-02T16:02:22+00:00

Deep Learning Training, Machine learning is one of the fastest-growing and most exciting fields out there, and deep learning represents its true bleeding edge

Deep Learning Training Course

Machine Learning, TensorFlow, Data Flow, Neural Networks

Advanced Deep Learning Training CourseMachine 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 TensorFlow 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.

TRAINING METHODOLOGY

In Class: $9,999
Locations: NEW YORK CITY, D.C, BAY AREA.
Next Session: 31st Jul 2017

Online: $4,999
Next Session: 31st Jul 2017

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Advanced Deep Learning Training Course

Instructor: John Doe, Lamar George

DESCRIPTION

advanced deep learning training Pre-Requisites:

A good grounding in basic machine learning. Programming skills in any language (ideally Python/R).
Topics:

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
Good understanding of Machine Learning.

At least theoretical knowledge of Deep Learning.

The average salary for deep learning jobs is $56,881

CURRICULUM

MACHINE LEARNING LIMITATIONS
Lecture1.1 Machine Learning, Nonlinear mappings 30m
Lecture1.2 Neural Networks
Lecture1.3 Non-Linear Optimization, Stochastic/MiniBatch Gradient Descent
Lecture1.4 Back Propagation
Lecture1.5 Deep Sparse Coding
Lecture1.6 Sparse Autoencoders (SAE)
CONVOLUTIONAL NEURAL NETWORKS
Lecture2.1 Successes: Descriptor Matching
Lecture2.2 Stereo-based Obstacle
Lecture2.3 Avoidance for Robotics
Lecture2.4 Pooling and invariance
Lecture2.5 Visualization/Deconvolutional Networks
Lecture2.6 Recurrent Neural Networks (RNNs) and their optimization
APPLICATIONS TO NLP
Lecture3.1 Probabilistic Graphical Models
Lecture3.2 Hopfield Nets, Boltzmann machines, Restricted Boltzmann Machines
HOPFIELD NETWORKS, (RESTRICTED) BOLZMANN MACHINES
Lecture4.1 Deep Belief Nets, Stacked RBMs
Lecture4.2 Applications to NLP, Pose and Activity Recognition in Videos
Lecture4.3 Recent Advances
Lecture4.4 Large-Scale Learning
Lecture4.5 Neural Turing Machines

Online: $2,499
Next Batch: starts from Monday

In Class: $9,999
Locations: New York City, D.C., Bay Area
Next Batch: starts from 31st July 2017

INSTRUCTORS

 [wp-review id=”20290″]

Lamar George
Learning Scientist & Master Trainer He has been a professional educator for the past 20 years. He’s taught, tutored, and coached over 1000 students, and he holds degrees in Physics and Literature from Northwestern University. He has spent the last 4 years studying how people learn to code and develop applications.

COURSE HIGHLIGHTS

Skill level: Intermediate
Language: English
Certificate: No
Assessments: Self
Prerequisites: Basic Python programming

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FAQ'S

What do I need to know before taking this Course?

A basic understanding of Python and modeling.
Familiarity with matrices and linear algebra.

Does Tensor Flow work with Python 3?

As of the 0.6.0 release timeframe (Early December 2015), it does support Python 3.3+.

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