Packt | Learn Artificial Intelligence with TensorFlow [FCO]
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| 1.TensorFlow Setup and Fundamental Concepts | |||
| 01.The Course Overview.mp4 | 50.53 MB | ||
| 02.Machine Learning Basics.mp4 | 60.9 MB | ||
| 03.TensorFlow Basics Part 1 - Tensors and Variables.mp4 | 113.3 MB | ||
| 04.TensorFlow Basics Part 2 - Graphs and Sessions.mp4 | 100.71 MB | ||
| 05.TensorFlow Basics Part 3 - Training, Saving, and Restoring.mp4 | 99.12 MB | ||
| 2.Computer Vision | |||
| 06.Convolutional Neural Networks.mp4 | 101.29 MB | ||
| 07.Preprocessing, Pooling, and Batch Normalization.mp4 | 8.73 MB | ||
| 08.Training a CNN on CIFAR-10 – Part 1.mp4 | 24.13 MB | ||
| 09.Training a CNN on CIFAR-10 – Part 2.mp4 | 36.13 MB | ||
| 3.Natural Language Processing and Recurrent Neural Networks | |||
| 10.Embeddings.mp4 | 356.77 MB | ||
| 11.Recurrent Neural Networks.mp4 | 20.3 MB | ||
| 12.Bidirectionality and Stacking RNNs.mp4 | 7.94 MB | ||
| 13.Models for Text Classification – Part 1.mp4 | 23.24 MB | ||
| 14.Models for Text Classification – Part 2.mp4 | 18.9 MB | ||
| 4.Tips and Tricks | |||
| 15.TensorBoard.mp4 | 61.3 MB | ||
| 16.Working with Estimators.mp4 | 38.89 MB | ||
| 17.Training Tips.mp4 | 38.92 MB | ||
| 18.Debugging Strategies.mp4 | 31.45 MB | ||
| 5.TensorFlow in Production | |||
| 19.Requirements for ML at Scale.mp4 | 81.32 MB | ||
| 20.TensorFlow with C++.mp4 | 74.61 MB | ||
| 21.TensorFlow Serving.mp4 | 38.89 MB | ||
| 22.TensorFlow Lite.mp4 | 4.58 MB | ||
| 6.Miscellaneous Topics and Course Summary | |||
| 23.TPUs.mp4 | 104.85 MB | ||
| 24.AutoML.mp4 | 11.69 MB | ||
| 25.TensorFlow Eager.mp4 | 311.92 MB | ||
| 26.Course Summary and Next Steps.mp4 | 67.98 MB | ||
| Exercise Files | |||
| code_33512.zip | 75.48 KB |
Description
For More Udemy Free Courses >>> https://freetutorials.us/
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By : Brandon McKinzie
Released : May 27, 2018
Course Source : https://www.packtpub.com/big-data-and-business-intelligence/learn-artificial-intelligence-tensorflow-video
Get hands-on and create your own machine learning solutions
Video Details
ISBN 9781788472463
Course Length 4 hours 20 minutes
Table of Contents
• TensorFlow Setup and Fundamental Concepts
• Computer Vision
• Natural Language Processing and Recurrent Neural Networks
• Tips and Tricks
• TensorFlow in Production
• Miscellaneous Topics and Course Summary
Learn
• Basic fundamentals of TensorFlow
• Using Computer Vision for modeling of images
• Learn to implement models for Natural Language Processing
• Some exciting tips & tricks for designing, training and evaluating the models.
• TensorFlow in a production setting
• Use TPU’s and AutoML for developing smart applications
About
Google’s TensorFlow framework is the current leading software for implementing and experimenting with the algorithms that power AI and machine learning. Google deploys TensorFlow for many of its products, such as Translate and Maps.
We will embark on this journey by quickly wrapping up some important fundamental concepts, followed by a focus on TensorFlow to complete tasks in computer vision and natural language processing. You will be introduced to some important tips and tricks necessary for enhancing the efficiency of our models. We will highlight how TensorFlow is used in an advanced environment and brush through some of the unique concepts at the cutting edge of practical AI.
If you want to develop a solid foundation on using TensorFlow and continue your journey into advancing the state of the art in AI to create your own smart machine learning solutions, this course is for you.
All the code and supporting files for this course are available on Github at https://github.com/PacktPublishing/-Learn-Artificial-Intelligence-with-TensorFlow
Style and Approach
This is an introductory course with a right balance between theory and practical implementation.
Features:
• Introduction to building models for computer vision and natural language processing.
• Overview of the many utilities that TensorFlow has to offer, such as TensorBoard, Estimators, and more.
• Learn how models are made in production settings, and how to best structure your TensorFlow programs.
Author
Brandon McKinzie
Brandon McKinzie is an NLP engineer/researcher and lover of all things associated with machine learning, with a particular interest in deep learning for natural language processing. The author is extremely passionate about contributing to research and learning in general, and in his free time he’s either working through textbooks, personal projects, or browsing blogs related to ML/AI.
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