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Deep Learning

Fundamentals

Details

PREREQUISITES

Attendees should have basic Python and Machine Learning skills, as well as familiarity with Pandas and Numpy slicing

LENGTH

5 Days

LOCATION

On-site or Live Online

STUDENT PROFILE

Individuals with some programming and machine learning experience

Course Description

As companies continue to embrace digital tools many now find themselves with access to an unprecedented amount of data. While business leaders are aware of the importance of data as a resource, many still struggle with how to capitalize on its value.  Deep learning techniques can help your data and analytics team find insights in a data lake that would take an individual decades to process.  In Deep Learning Foundations by Metis, course attendees will receive a solid background in the topic as well as instruction on how to get started with the most common Python libraries for doing Deep Learning. This course is geared towards individuals who have some programming and machine learning experience.

Course Outcomes

Upon completion of the course, attendees should be able to:

Define deep learning and common terminology

Articulate the strengths and weaknesses of deep learning and identify the types of problems deep learning can solve

Explain how deep learning models are trained and practical techniques for training a deep learning model

Use a variety of preprocessing and initialization techniques

List, differentiate, and use different activation functions for deep learning models and the different optimizers to train them

And much more...

Training Content

Day 1

  • Machine Learning Review
  • Neural Networks Overview & Workflow
  • Fully Connected Networks
  • Activation Functions
  • Loss Functions
  • Training Neural Networks
  • Practical Issues
  • GPUs

Day 2

  • Review Day 1
  • More Activation Functions
  • Optimizers
  • Regularization

Day 3

  • Review Day 2
  • Introduction to CNNs
  • Hyperparameters
  • Data Augmentation
  • Pre-trained Models
  • Transfer Learning
  • Network Architectures

Day 4

  • Review Day 3
  • RNNs
  • RNN Extensions
  • Applications
  • Seq2Seq
  • Application of Seq2Seq and Attention to Language Translation

Day 5

  • Review of Day 4
  • Word Vectors
  • Unsupervised Representations of Images
  • Application of Unsupervised Learning to Image Similarity
  • GANs

Related Blog Posts

Details

PREREQUISITES

Attendees should have basic Python and Machine Learning skills, as well as familiarity with Pandas and Numpy slicing

LENGTH

5 Days

LOCATION

On-site or Live Online

STUDENT PROFILE

Individuals with some programming and machine learning experience