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...is a collective term for characteristics that the two institutions share.
█ Deep Learning with TensorFlow
█
▄
█ by Jon Krohn
█ Publisher: Addison-Wesley Professional
▓ Release Date: August 2017
▓ ISBN: 013477082X
▒ Running time: 4:07:42
▒ Topic: Analytics
░ Scale: 66x100mb
░ Downloads: ( ) Included (■) Without
░
░ Lecture Date: 07/2017
░ Lecture Link: https://goo.gl/1y5D4F
░
:
| Rough Cuts / Sneak Peek:
|
| The Rough Cuts/Sneak Peek program provides early access to Pearson
| video products and is exclusively available to Safari subscribers.
| Content for titles in this program is made available throughout
| the development cycle, so products may not be complete, edited, or
| finalized, including video post-production editing.
|
| 6+ Hours of Video Instruction
|
| Overview
|
| Deep Learning with TensorFlow LiveLessons is an introduction to
| Deep Learning that bring the revolutionary machine-learning
| approach to life with interactive demos from the most popular Deep
| Learning library, TensorFlow, and its high-level API, Keras.
| Essential theory is whiteboarded to provide an intuitive
| understanding of Deep LearningÆs underlying foundations, i.e.,
| artificial neural networks. Paired with tips for overcoming common
| pitfalls and hands-on code run-throughs provided in Python-based
| Jupyter notebooks, this foundational knowledge empowers
| individuals with no previous understanding of neural networks to
| build powerful state-of-the-art Deep Learning models.
|
| About the Instructor
|
| Jon Krohn is the Chief Data Scientist at untapt, a machine
| learning startup in New York. He leads a Deep Learning Study Group
| and, having obtained his doctorate in neuroscience from Oxford
| University, continues to publish academic papers.
|
| Skill Level
|
| Intermediate
| Learn How 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
| Who Should Take This Course
|
| These LiveLessons are perfectly-suited to software engineers, data
| scientists, analysts, and statisticians with an interest in Deep
| Learning. Code examples are provided in Python, so familiarity
| with it or another object-oriented programming language would be
| helpful. Previous experience with statistics or machine learning
| is not necessary.
|
| Course Requirements
|
| Some experience with any of the following are an asset, but none
| are essential:
|
| object-oriented programming, specifically Python
| simple shell commands, e.g., in Bash
| machine learning or statistics
| first-year college calculus
| Table of Contents
|
| Introduction to Deep Learning with TensorFlow LiveLessons
|
| Lesson 1: Introduction to Deep Learning
| Learning Objectives
| 1.1 Neural Networks and Deep Learning
| 1.2 Running the Code in These LiveLessons
| 1.3 An Introductory Artificial Neural Network
|
| Lesson 2: How Deep Learning Works
| Learning Objectives
| 2.1 The Families of Deep Neural Nets and their Applications
| 2.2 Essential Theory Ié─εNeural Units, Cost Functions, Gradient
| Descent, and Backpropagation
| 2.3 TensorFlow Playgroundé─εVisualizing a Deep Net in Action
| 2.4 Data Sets for Deep Learning
| 2.5 Applying Deep Net Theory to Code I
|
| Lesson 3: Convolutional Networks
| Learning Objectives
| 3.1 Essential Theory IIé─εMini-Batches, Unstable Gradients, and
| Avoiding Overfitting
| 3.2 Applying Deep Net Theory to Code II
| 3.3 Introduction to Convolutional Neural Networks for Visual
| Recognition
| 3.4 Classic ConvNet Architecturesé─εLeNet-5
| 3.5 Classic ConvNet Architecturesé─εAlexNet and VGGNet
| 3.6 TensorBoard and the Interpretation of Model Outputs
|
| Lessons 4: Introduction to TensorFlow
| Learning Objectives
| 4.1 Comparison of the Leading Deep Learning Libraries
| 4.2 Introduction to TensorFlow
| 4.3 Fitting Models in TensorFlow
| 4.4 Dense Nets in TensorFlow
| 4.5 Deep Convolutional Nets in TensorFlow
|
| Lesson 5: Improving Deep Networks
| Learning Objectives
| 5.1 Improving Performance and Tuning Hyperparameters
| 5.2 How to Build Your Own Deep Learning Project
| 5.3 Resources for Self-Study
|
| Summary of Deep Learning with TensorFlow LiveLessons
,