Deep Learning

Deep Learning

Deep Learning

Intermediate

Via analogy to biological neurons and human perception, this course is an introduction to artificial neural networks that brings high-level theory to life with interactive labs featuring TensorFlow, the most popular open-source Deep Learning library. Essential theory will be covered in a manner that provides students with an intuitive understanding of Deep Learning’s underlying foundations. Paired with hands-on code run-throughs in Jupyter notebooks as well as strategies for overcoming common pitfalls, this foundational knowledge will empower individuals with no previous understanding of neural networks to build production-ready Deep Learning applications across the major contemporary families: Convolutional Nets for machine vision; Long Short-Term Memory Recurrent Nets for natural language processing and time series analysis; Generative Adversarial Networks for producing realistic images; and Reinforcement Learning for playing video games.

Course Overview
Intermediate

Via analogy to biological neurons and human perception, this course is an introduction to artificial neural networks that brings high-level theory to life with interactive labs featuring TensorFlow, the most popular open-source Deep Learning library. Essential theory will be covered in a manner that provides students with an intuitive understanding of Deep Learning’s underlying foundations. Paired with hands-on code run-throughs in Jupyter notebooks as well as strategies for overcoming common pitfalls, this foundational knowledge will empower individuals with no previous understanding of neural networks to build production-ready Deep Learning applications across the major contemporary families: Convolutional Nets for machine vision; Long Short-Term Memory Recurrent Nets for natural language processing and time series analysis; Generative Adversarial Networks for producing realistic images; and Reinforcement Learning for playing video games.

Date and Time

October Session

Oct 14 - Dec 16, 2017, 10:00am-4:00pm
Day 1: October 14, 2017
Day 2: October 28, 2017
Day 3: November 18, 2017
Day 4: December 2, 2017
Day 5: December 16, 2017
$2990.00
Add to Cart

Instructors

Jon Krohn
Jon Krohn
Jon Krohn is the Chief Data Scientist of the machine learning startup untapt. He leads a flourishing Deep Learning Study Group and presents the acclaimed Deep Learning with TensorFlow LiveLessons. Jon holds a doctorate in neuroscience from Oxford University and has been publishing on machine learning in leading academic journals since 2010. His forthcoming book, Deep Learning Illustrated, is being published on Pearson's Addison-Wesley imprint and will be distributed in 2018.

Product Description


Overview

 

Facilitated by the intersection of inexpensive computing power, unprecedently large data sets, and clever computational statistics advances, Deep Learning algorithms are driving an Artificial Intelligence revolution. Deep Learning has emerged as uniquely influential across a broad range of the statistical domain, including classification (e.g., visual recognition, sentiment analysis), prediction (e.g., stock markets, health outcomes) and generation (e.g., creating images, composing music). In the past few years, Deep Neural Networks have made their way into countless everyday applications, including Tesla’s Autopilot, Siri’s voice recognition, Facebook’s face identification, and hundreds of products at Google such as Inbox with its suggested replies. Indeed, Deep Learning algorithms have exceeded human performance on previously intractable computational problems like language translation, object detection, and the game of Go.

Via analogy to biological neurons and human perception, this course is an introduction to artificial neural networks that brings high-level theory to life with interactive labs featuring TensorFlow, the most popular open-source Deep Learning library. Essential theory will be covered in a manner that provides students with an intuitive understanding of Deep Learning’s underlying foundations. Paired with hands-on code run-throughs in Jupyter notebooks as well as strategies for overcoming common pitfalls, this foundational knowledge will empower individuals with no previous understanding of neural networks to build production-ready Deep Learning applications across the major contemporary families: Convolutional Nets for machine vision; Long Short-Term Memory Recurrent Nets for natural language processing and time series analysis; Generative Adversarial Networks for producing realistic images; and Reinforcement Learning for playing video games.


Details


Instructor Interview


Jon Krohn, Instructor for Deep Learning course at NYC Data Science Academy on Vimeo.


Goals

 

This is a “short course” of five weeks, with six hours of class per week. Classes will be given in a lab setting, with hands-on exercises mixed with lectures. Students should bring a laptop to class. You will have the option of creating and completing your own major Deep Learning project over the duration of the program.

Who Is This Course For?

 

This course is perfect for software engineers, data scientists, analysts, and statisticians with an interest in Deep Learning. No previous knowledge of Deep Learning is assumed. Previous experience with statistics or machine learning is not necessary, but will be an asset if you have it. Code examples are provided in Python, so familiarity with it or another object-oriented programming language would be helpful.

Prerequisites

 

It will be challenging to follow along through the code demos and exercises without some experience in:

  • object-oriented programming, ideally Python (introductory course: https://nycdatascience.com/courses/introductory-python/)
  • simple shell commands, e.g., in Bash (tutorial of the fundamentals: https://learnpythonthehardway.org/book/appendixa.html)

Outcomes

 

By the end of the course, you will be able to:
  • build Deep Learning models in TensorFlow and Keras
  • interpret the results of Deep Learning models
  • troubleshoot and improve Deep Learning models
  • understand the language and fundamentals of artificial neural networks
  • build your own Deep Learning project

Syllabus

 
Unit 1: The Unreasonable Effectiveness of Deep Learning

  • An Introduction to Neural Networks and Deep Learning
  • Course Survey
  • Interactive Visualization of an Artificial Neural Network
  • Hardware Options for Deep Learning, including How to Build a Deep Learning Server
  • Running a TensorFlow Jupyter Notebook within a Docker Container
  • A Shallow Artificial Neural Network

Unit 2: How Deep Learning Works

  • Essential Theory I: Neural Units
  • Interactive Visualization of Neural Units
  • Essential Theory II: Cost Functions, Gradient Descent, and Backpropagation
  • Interactive Visualization of a Deep Neural Network
  • An Intermediate Neural Network
  • Data Sets for Deep Learning
  • Your Deep Learning Project: Ideating

Unit 3: Building and Training a Deep Learning Network

  • Review Content and Take-Home Exercises
  • Essential Theory III: Weight Initialization and Mini-Batches
  • Essential Theory IV: Unstable Gradients and Avoiding Overfitting
  • A Deep Neural Network
  • TensorBoard and the Interpretation of Model Outputs

Unit 4: Machine Vision

  • Introduction to Convolutional Neural Networks for Visual Recognition
  • Classic ConvNet Architecture I: LeNet-5
  • Classic ConvNet Architecture II: AlexNet
  • Transfer Learning
  • The Families of Deep Neural Nets and their Applications
  • Your Deep Learning Project: Formulating

Unit 5: TensorFlow

  • Reviewing Content and Take-Home Exercises
  • Comparison of the Leading Deep Learning Libraries
  • TensorFlow Graphs
  • Neurons in TensorFlow
  • Fitting Models in TensorFlow

Unit 6: Deep Learning with TensorFlow

  • Deep Dense Nets in TensorFlow
  • Deep Convolutional Nets in TensorFlow
  • TensorBoard Best-Practices
  • Improving Model Performance
  • Tuning Hyperparameters
  • Your Deep Learning Project: Assessing

Unit 7: Natural Language Processing

  • Reviewing Content and Take-Home Exercises
  • Word Vectors: word2vec and Vector-Space Embedding
  • Recurrent Neural Networks
  • Long Short-Term Memory Units
  • Sentiment Analysis

Unit 8: Time Series Analysis

  • Machine Translation: Sequence-to-Sequence Models and Attention
  • Neural Network Architectures for Question-Answering
  • Forecasting with Financial Time Series Data
  • Your Deep Learning Project: Improving

Unit 9: Generative Adversarial Networks

  • Reviewing Content and Take-Home Exercises
  • Applications of GANs
  • Essential Theory of GANs
  • Implementations of GANs

Unit 10: Reinforcement Learning

  • Applications of Reinforcement Learning
  • OpenAI Gym
  • Essential Theory of Reinforcement Learning
  • Implementations of Reinforcement Learning
  • Jeanne Calment and Your Role in the AI Revolution

Follow-Up Session Three Weeks Later

  • Your Deep Learning Project: Presentation

Reviews

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Be the first to review “Deep Learning”

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Instructors

Jon Krohn
Jon Krohn
Jon Krohn is the Chief Data Scientist of the machine learning startup untapt. He leads a flourishing Deep Learning Study Group and presents the acclaimed Deep Learning with TensorFlow LiveLessons. Jon holds a doctorate in neuroscience from Oxford University and has been publishing on machine learning in leading academic journals since 2010. His forthcoming book, Deep Learning Illustrated, is being published on Pearson's Addison-Wesley imprint and will be distributed in 2018.

Product Description


Overview

 

Facilitated by the intersection of inexpensive computing power, unprecedently large data sets, and clever computational statistics advances, Deep Learning algorithms are driving an Artificial Intelligence revolution. Deep Learning has emerged as uniquely influential across a broad range of the statistical domain, including classification (e.g., visual recognition, sentiment analysis), prediction (e.g., stock markets, health outcomes) and generation (e.g., creating images, composing music). In the past few years, Deep Neural Networks have made their way into countless everyday applications, including Tesla’s Autopilot, Siri’s voice recognition, Facebook’s face identification, and hundreds of products at Google such as Inbox with its suggested replies. Indeed, Deep Learning algorithms have exceeded human performance on previously intractable computational problems like language translation, object detection, and the game of Go.

Via analogy to biological neurons and human perception, this course is an introduction to artificial neural networks that brings high-level theory to life with interactive labs featuring TensorFlow, the most popular open-source Deep Learning library. Essential theory will be covered in a manner that provides students with an intuitive understanding of Deep Learning’s underlying foundations. Paired with hands-on code run-throughs in Jupyter notebooks as well as strategies for overcoming common pitfalls, this foundational knowledge will empower individuals with no previous understanding of neural networks to build production-ready Deep Learning applications across the major contemporary families: Convolutional Nets for machine vision; Long Short-Term Memory Recurrent Nets for natural language processing and time series analysis; Generative Adversarial Networks for producing realistic images; and Reinforcement Learning for playing video games.


Details


Instructor Interview


Jon Krohn, Instructor for Deep Learning course at NYC Data Science Academy on Vimeo.


Goals

 

This is a “short course” of five weeks, with six hours of class per week. Classes will be given in a lab setting, with hands-on exercises mixed with lectures. Students should bring a laptop to class. You will have the option of creating and completing your own major Deep Learning project over the duration of the program.

Who Is This Course For?

 

This course is perfect for software engineers, data scientists, analysts, and statisticians with an interest in Deep Learning. No previous knowledge of Deep Learning is assumed. Previous experience with statistics or machine learning is not necessary, but will be an asset if you have it. Code examples are provided in Python, so familiarity with it or another object-oriented programming language would be helpful.

Prerequisites

 

It will be challenging to follow along through the code demos and exercises without some experience in:

  • object-oriented programming, ideally Python (introductory course: https://nycdatascience.com/courses/introductory-python/)
  • simple shell commands, e.g., in Bash (tutorial of the fundamentals: https://learnpythonthehardway.org/book/appendixa.html)

Outcomes

 

By the end of the course, you will be able to:
  • build Deep Learning models in TensorFlow and Keras
  • interpret the results of Deep Learning models
  • troubleshoot and improve Deep Learning models
  • understand the language and fundamentals of artificial neural networks
  • build your own Deep Learning project

Syllabus

 
Unit 1: The Unreasonable Effectiveness of Deep Learning

  • An Introduction to Neural Networks and Deep Learning
  • Course Survey
  • Interactive Visualization of an Artificial Neural Network
  • Hardware Options for Deep Learning, including How to Build a Deep Learning Server
  • Running a TensorFlow Jupyter Notebook within a Docker Container
  • A Shallow Artificial Neural Network

Unit 2: How Deep Learning Works

  • Essential Theory I: Neural Units
  • Interactive Visualization of Neural Units
  • Essential Theory II: Cost Functions, Gradient Descent, and Backpropagation
  • Interactive Visualization of a Deep Neural Network
  • An Intermediate Neural Network
  • Data Sets for Deep Learning
  • Your Deep Learning Project: Ideating

Unit 3: Building and Training a Deep Learning Network

  • Review Content and Take-Home Exercises
  • Essential Theory III: Weight Initialization and Mini-Batches
  • Essential Theory IV: Unstable Gradients and Avoiding Overfitting
  • A Deep Neural Network
  • TensorBoard and the Interpretation of Model Outputs

Unit 4: Machine Vision

  • Introduction to Convolutional Neural Networks for Visual Recognition
  • Classic ConvNet Architecture I: LeNet-5
  • Classic ConvNet Architecture II: AlexNet
  • Transfer Learning
  • The Families of Deep Neural Nets and their Applications
  • Your Deep Learning Project: Formulating

Unit 5: TensorFlow

  • Reviewing Content and Take-Home Exercises
  • Comparison of the Leading Deep Learning Libraries
  • TensorFlow Graphs
  • Neurons in TensorFlow
  • Fitting Models in TensorFlow

Unit 6: Deep Learning with TensorFlow

  • Deep Dense Nets in TensorFlow
  • Deep Convolutional Nets in TensorFlow
  • TensorBoard Best-Practices
  • Improving Model Performance
  • Tuning Hyperparameters
  • Your Deep Learning Project: Assessing

Unit 7: Natural Language Processing

  • Reviewing Content and Take-Home Exercises
  • Word Vectors: word2vec and Vector-Space Embedding
  • Recurrent Neural Networks
  • Long Short-Term Memory Units
  • Sentiment Analysis

Unit 8: Time Series Analysis

  • Machine Translation: Sequence-to-Sequence Models and Attention
  • Neural Network Architectures for Question-Answering
  • Forecasting with Financial Time Series Data
  • Your Deep Learning Project: Improving

Unit 9: Generative Adversarial Networks

  • Reviewing Content and Take-Home Exercises
  • Applications of GANs
  • Essential Theory of GANs
  • Implementations of GANs

Unit 10: Reinforcement Learning

  • Applications of Reinforcement Learning
  • OpenAI Gym
  • Essential Theory of Reinforcement Learning
  • Implementations of Reinforcement Learning
  • Jeanne Calment and Your Role in the AI Revolution

Follow-Up Session Three Weeks Later

  • Your Deep Learning Project: Presentation

Reviews

There are no reviews yet.

Be the first to review “Deep Learning”

Your email address will not be published. Required fields are marked *

Date and Time

October Session

Oct 14 - Dec 16, 2017, 10:00am-4:00pm
Day 1: October 14, 2017
Day 2: October 28, 2017
Day 3: November 18, 2017
Day 4: December 2, 2017
Day 5: December 16, 2017
$2990.00
Add to Cart