Deep Learning with TensorFlow, Keras, and PyTorch

Category: Tutorial


Posted on 2020-02-29, by perica123.

Description

V3ubn-BNW4-Hf-T3-Q7-Xoeh-Vd-Rx-Otrbwr-Gbn.jpg

Deep Learning with TensorFlow, Keras, and PyTorch
Video: .MP4, 1280x720 30 fps | Audio: AAC, 48kHz, 2ch | Duration: 7h 19m
Genre: eLearning | Language: English | Size: 13.1 GB

An intuitive, application-focused introduction to deep learning and TensorFlow, Keras, and PyTorch Deep Learning with TensorFlow, Keras, and PyTorch LiveLessons is an introduction to deep learning that brings the revolutionary machine-learning approach to life with interactive demos from the most popular deep learning library, TensorFlow, and its high-level API, Keras, as well as the hot new library PyTorch. Essential theory is whiteboarded to provide an intuitive understanding of deep learning's 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 the machine learning company untapt. He presents a popular series of tutorials published by Addison-Wesley and is the author of the acclaimed book Deep Learning Illustrated. Jon teaches his deep learning curriculum in-classroom at the New York City Data Science Academy. He holds a doctorate in neuroscience from Oxford University, lectures at Columbia University, and carries out machine vision research at Columbia's Irving Medical Center.

Skill Level
Intermediate
Learn How To
Build deep learning models in all the major libraries: TensorFlow, Keras, and PyTorch
Understand the language and theory of artificial neural networks
Excel across a broad range of computational problems including machine vision, natural language processing, and reinforcement learning
Create algorithms with state-of-the-art performance by fine-tuning model architectures
Self-direct and complete your own Deep Learning projects
Who Should Take This Course
Software engineers, data scientists, analysts, and statisticians with an interest in deep learning.
Code e x a mples 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
Lesson Descriptions

Lesson 1: Introduction to Deep Learning and Artificial Intelligence
The first lesson starts off by giving the viewer an overview of what neural networks are, how they're related to machine learning (ML) and artificial intelligence (AI), as well as the breadth of transformative applications deep learning has supplied. Subsequently, Jon leverages visual analogies to describe what deep learning is and why it's a uniquely powerful approach. You use an interactive tool to observe for yourself in real-time how a deep learning network learns, and Jon goes over how to run the code e x a mples he provides throughout these LiveLessons before building an introductory neural network with you.

Lesson 2: How Deep Learning Works
The lesson begins with a discussion of the main families of deep neural networks and their applications. The heart of the lesson is a high-level overview of the essential theory that underlies deep learning. To bring this theory to life, Jon shows you deep learning in action via a web application called the TensorFlow Playground. He introduces the archetypal deep learning data sets, and then you build a deep neural network together to tackle a classic machine vision problem.

Lesson 3: High-Performance Deep Learning Networks
The previous lesson covered the principal foundations of deep learning and enabled you to construct a deep network. This lesson builds upon those theoretical foundations by covering weight initialization, unstable gradients, batch normalization, how to avoid overfitting, and more sophisticated learning optimizers. This additional theory enables you to build a state-of-the-art deep learning model using TensorFlow's Keras API. In addition, you and Jon tackle a regression problem with deep learning for the first time, having focused on classification problems only up until this point. In order to make sense of the outputs from these sophisticated models, the TensorBoard result-visualization tool is added to your arsenal at the end of the lesson.

Lesson 4: Convolutional Neural Networks
Up to this point you have relied exclusively on dense nets to solve the machine learning problems. In this lesson, you dig into the theory of convolutional layers and then stack them together with Keras in TensorFlow in order to build your first convolutional neural network. Jon wraps up the lesson by discussing model architectures.

Lesson 5: Moving Forward with Your Own Deep Learning Projects
In Lesson 5, Jon compares and contrasts all the leading Deep Learning libraries and provides detailed hands-on e x a mples of how to use PyTorch – the hot new library on the block – to build deep learning models. He concludes these LiveLessons by providing a framework for optimally tuning any model's hyperparameters before leaving you with advice on how to build your own deep learning project, including datasets and resources for further self-study.

09-1-7-An-Introductory-Neural-Network-with-Tensor-Flow-and-Kera.jpg


https://nitroflare.com/view/D7045868C172F52/Deep_Learning_with_TensorFlow%2C_Keras%2C_and_PyTorch.part01.rar
https://nitroflare.com/view/FA25DC04CCCA6F5/Deep_Learning_with_TensorFlow%2C_Keras%2C_and_PyTorch.part02.rar
https://nitroflare.com/view/FFA1F398762E259/Deep_Learning_with_TensorFlow%2C_Keras%2C_and_PyTorch.part03.rar
https://nitroflare.com/view/FCC604235753D76/Deep_Learning_with_TensorFlow%2C_Keras%2C_and_PyTorch.part04.rar
https://nitroflare.com/view/6DA005DDA847ADE/Deep_Learning_with_TensorFlow%2C_Keras%2C_and_PyTorch.part05.rar
https://nitroflare.com/view/4C4CC4D3FC7046C/Deep_Learning_with_TensorFlow%2C_Keras%2C_and_PyTorch.part06.rar
https://nitroflare.com/view/9A90E8522A0AD49/Deep_Learning_with_TensorFlow%2C_Keras%2C_and_PyTorch.part07.rar
https://nitroflare.com/view/340F7BECE1D2DA5/Deep_Learning_with_TensorFlow%2C_Keras%2C_and_PyTorch.part08.rar
https://nitroflare.com/view/CE7A4ED9D5710AD/Deep_Learning_with_TensorFlow%2C_Keras%2C_and_PyTorch.part09.rar
https://nitroflare.com/view/81BD54C77C14783/Deep_Learning_with_TensorFlow%2C_Keras%2C_and_PyTorch.part10.rar
https://nitroflare.com/view/8BB996F3EC6AD12/Deep_Learning_with_TensorFlow%2C_Keras%2C_and_PyTorch.part11.rar
https://nitroflare.com/view/56B3E1D72099099/Deep_Learning_with_TensorFlow%2C_Keras%2C_and_PyTorch.part12.rar
https://nitroflare.com/view/9CACF08CD806EF9/Deep_Learning_with_TensorFlow%2C_Keras%2C_and_PyTorch.part13.rar
https://nitroflare.com/view/3EAF30357441CDA/Deep_Learning_with_TensorFlow%2C_Keras%2C_and_PyTorch.part14.rar



https://rapidgator.net/file/a58eec11bcf4e9327509880e3c2f4dc9/Deep_Learning_with_TensorFlow,_Keras,_and_PyTorch.part01.rar.html
https://rapidgator.net/file/752cd244af61b8c5526b86098512815f/Deep_Learning_with_TensorFlow,_Keras,_and_PyTorch.part02.rar.html
https://rapidgator.net/file/517e4ea2a6c96ab2b989ffa1848994d8/Deep_Learning_with_TensorFlow,_Keras,_and_PyTorch.part03.rar.html
https://rapidgator.net/file/92ef6cba2dc5c7cf2b505b76e5f35a19/Deep_Learning_with_TensorFlow,_Keras,_and_PyTorch.part04.rar.html
https://rapidgator.net/file/f83a6b42025d6053ce7042d25e99c36c/Deep_Learning_with_TensorFlow,_Keras,_and_PyTorch.part05.rar.html
https://rapidgator.net/file/5adc11685f4cbd9f47cb79defe922142/Deep_Learning_with_TensorFlow,_Keras,_and_PyTorch.part06.rar.html
https://rapidgator.net/file/9ef1585d745621bf64bcdc8f13667c88/Deep_Learning_with_TensorFlow,_Keras,_and_PyTorch.part07.rar.html
https://rapidgator.net/file/2b9606bd8340ff0c16ef99d50ac4102f/Deep_Learning_with_TensorFlow,_Keras,_and_PyTorch.part08.rar.html
https://rapidgator.net/file/f0cdfe7e1e9ddaf5d38f204355fb73b0/Deep_Learning_with_TensorFlow,_Keras,_and_PyTorch.part09.rar.html
https://rapidgator.net/file/72a4f7015b83891c303a861144eae6d0/Deep_Learning_with_TensorFlow,_Keras,_and_PyTorch.part10.rar.html
https://rapidgator.net/file/3c3e80a986959ff7a1ee4b9a1e0776fb/Deep_Learning_with_TensorFlow,_Keras,_and_PyTorch.part11.rar.html
https://rapidgator.net/file/e19d4605b4be9de41428d071f0c0fe60/Deep_Learning_with_TensorFlow,_Keras,_and_PyTorch.part12.rar.html
https://rapidgator.net/file/c2ecd92dfefaee88858e37c56ec235ac/Deep_Learning_with_TensorFlow,_Keras,_and_PyTorch.part13.rar.html
https://rapidgator.net/file/5a901e9c14756611c054dd588395e96a/Deep_Learning_with_TensorFlow,_Keras,_and_PyTorch.part14.rar.html


Sponsored High Speed Downloads
8784 dl's @ 3326 KB/s
Download Now [Full Version]
7690 dl's @ 3668 KB/s
Download Link 1 - Fast Download
6251 dl's @ 2644 KB/s
Download Mirror - Direct Download



Search More...
Deep Learning with TensorFlow, Keras, and PyTorch

Search free ebooks in ebookee.com!


Related Archive Books

Archive Books related to "Deep Learning with TensorFlow, Keras, and PyTorch":



Links
Download this book

No active download links here?
Please check the description for download links if any or do a search to find alternative books.


Related Books

  1. Ebooks list page : 42830
  2. 2020-04-20Addison Wesley Professional Deep Learning With Tensorflow Keras And Pytorch Sneak Peak
  3. 2020-04-15Deep Learning With Tensorflow, Keras, And Pytorch
  4. 2020-04-09Addison Wesley Professional Deep Learning with TensorFlow Keras and PyTorch Sneak Peak
  5. 2020-03-18Deep Learning with Tensorflow, Keras, and PyTorch, 2nd Edition Addison-Wesley Professional
  6. 2020-02-24Deep Learning with TensorFlow, Keras, and PyTorch
  7. 2020-01-05Deep Learning with TensorFlow 2 and Keras: Regression, ConvNets, GANs, RNNs, NLP, and more with TensorFlow 2 and the Keras API, 2nd Edition
  8. 2020-01-05A Practical Guide to Deep Learning with TensorFlow 2.0 and Keras - Removed
  9. 2019-12-20Advanced Deep Learning with Python: Design and implement advanced next-generation AI solutions using TensorFlow and PyTorch
  10. 2020-04-01Applied Deep Learning with TensorFlow and Google Cloud AI
  11. 2020-02-25Applied Deep Learning with TensorFlow and Google Cloud AI
  12. 2019-12-28Deep Learning with TensorFlow: Explore neural networks and build intelligent systems with Python, 2nd Edition Ed 2
  13. 2019-12-10Applied Deep Learning with TensorFlow and Google Cloud AI
  14. 2019-11-24Applied Deep Learning with TensorFlow and Google Cloud AI
  15. 2019-11-13Applied Deep Learning with TensorFlow and Google Cloud AI
  16. 2019-04-28PACKT APPLIED DEEP LEARNING WITH TENSORFLOW AND GOOGLE CLOUD AI-JGTiSO
  17. 2019-03-25Hands-On Serverless Deep Learning with TensorFlow and AWS Lambda
  18. 2019-03-21Hands-On Serverless Deep Learning with TensorFlow and AWS Lambda
  19. 2019-01-26PACKT- APPLIED DEEP LEARNING WITH TENSORFLOW AND GOOGLE CLOUD AI JGTiSO
  20. 2018-12-31Serverless Deep Learning with TensorFlow and AWS Lambda

Comments

No comments for "Deep Learning with TensorFlow, Keras, and PyTorch".


    Add Your Comments
    1. Download links and password may be in the description section, read description carefully!
    2. Do a search to find mirrors if no download links or dead links.
    Back to Top