Deep Learning with TensorFlow. 4 – Day (4 Saturdays) workshop. Artificial Intelligence and Deep Learning are the current buzzwords amongst academics, ...
A branch of Machine Learning. • Multiple levels of representation and abstraction. • One step closer to true “Artificial Intelligence”. • Typically refers to Artificial Neural Networks. • Externally can be thought of as a black box. • Maps inputs to
Deep-Learning-TensorFlow Documentation, Release stable This repository is a collection of various Deep Learning algorithms implemented using the TensorFlow library
Large-Scale Deep Learning With TensorFlow ... Mix of computer systems and machine learning ... By releasing TensorFlow, our core machine learning
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Supplemental material (code examples, exercises, etc.) is available for download at https://github.com/oreillymedia/title_title. This book is here to help you get your job done. In general, if example code is offered with this book, you may use it in
Deep Learning with Keras. Implement neural networks with Keras on Theano and TensorFlow .... has also authored TensorFlow Machine Learning Cookbook by Packt Publishing. He has a passion for ... Did you know that Packt offers eBook versions of every b
Large-Scale Deep Learning with TensorFlow for. Building Intelligent Systems. Jeff Dean. Google Brain Team g.co/brain. In collaboration with many other people at Google
Deep-Learning-TensorFlow Documentation, Release latest Thisprojectis a collection of various Deep Learning algorithms implemented using the TensorFlow library
Nov 10, 2016 ... Abstract– Resource management problems in systems and networking often manifest as difficult online decision mak- ing tasks where appropriate solutions depend on understand- ing the workload and environment. Inspired by recent ad- va
Deep learning is a type of machine learning in which a model learns to perform classification tasks directly from images, text, or sound. Deep learning is usually implemented using a neural network architecture. The term “deep” refers to the number o
Get some small index cards and create a set of five or six of them summarising the study under separate headings. ○. Read the chapter in Lauren Slater's ' Opening Skinner's Box'. ○. Role play the study; make a cardboard box your electric shock machin
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(Deep Belief Network) algorithm is used to design the deep learning controller. The simulation is performed using. Matlab/Simulink and the detailed results of a comparison study between the ... The machine learning algorithms can lead to significant
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TensorFlow Machine Learning Cookbook Explore machine learning concepts using the latest numerical computing library — TensorFlow — with the
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Managing Relationships with Independent Contractors ... relationship as that of an independent contractor and client as opposed to employer and employee
TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems (Preliminary White Paper, November 9, 2015) Mart´ın Abadi, Ashish Agarwal, Paul Barham
4. DATA SCIENCE LANDSCAPE. Data Analytics. Machine. Learning. Graph Analytics. SQL Query. Traditional. Methods. Deep Neural. Networks. • Regression. • SVM ... companies, as well as the foremost research institutions, are using GPUs for machine learni
1 Neurostream: Scalable and Energy Efficient Deep Learning with Smart Memory Cubes Erfan Azarkhish, Davide Rossi, Igor Loi, and Luca Benini, Fellow, IEEE
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Lecture 8: Deep Learning Software. Fei-Fei Li & Justin Johnson & Serena Yeung Lecture 8 - 2 2 April 27, 2017 ... TensorFlow (Google) Caffe2 (Facebook) PyTorch (Facebook)
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Deep Learning with TensorFlow 4 – Day (4 Saturdays) workshop
Artificial Intelligence and Deep Learning are the current buzzwords amongst academics, businesses and other industries. Deep Learning has the ability to transform many parts of modern life as recent innovations show. TensorFlow is a 2ndGeneration API of Google's open source software library for Deep Learning. The course is designed as a 4 – day (4 Saturdays) workshop and providescomprehensive knowledge in Deep Learning and hands-on experience in TensorFlow. The workshop is targeted for developers, hackers, academic researchers, graduate & postgraduate students, data scientists or data analysts who already know about machine learning and have experience in Programming. Prerequisites: • Programming experience (preferably in Python and/or sound knowledge in C++/C#/Java) • Basic machine learning knowledge • Basic Statistics, Linear Algebra and Calculus
Day 1 09:30 - 10:00
Registration and Networking Breakfast
10:00 - 11:30
Introduction to Neural Networks
This session provides learners with the fundamental background of Neural Networks including origin, theoretical framework, its various paradigms and applications. 11:30 - 12:00
Lab Session 1: Single Layered Perceptron using Python
The lab session guides the learners to develop a simple neuron (Perceptron) to solve linear classification problems with core Python. The aim of the session is to give learners a comparative understanding of development between core Python and Python with TensorFlow. 12:00 - 12:15
Coffee Break
12:15 - 01:00
Introduction to Backpropagation Learning
This session provides learners with theoretical background of backpropagation learning algorithm and its implementation over multilayer Neural Networks. 01:00 02:00
Lunch
02:00 - 03:00 learn
Lab Session 2: Multilayered Perceptron (MLP) using Scikit-
This session guides the learners to develop a simple MLP using Scikit-learn. The aim of the session is to give learners a comparative understanding of development between Scikit-learn and TensorFlow.
03:00 - 04:00
Lab Session 3: Introduction to TensorFlow
The lab session provides learners with the theoretical background of TensorFlow and let's get learner up and running with TensorFlow. The end of the session the learner will learn the basic features and components of TensorFlow and TensorFlow Data Flow Graph, and how to develop a simple application with TensorFlow.
04:00 - 04:15
Coffee Break
04:15 - 05:00
Lab Session 4: MLP using TensorFlow
The lab session is extension of lab session 2 and 3, to Introduce how to implement a simple MLP with TensorFlow. The end of the session the learner will learn how to develop computational graph of MLP and implementation of learning algorithm with TensorFlow, of course the training and validating the MLP.
Day 2 09:30 - 10:00
Registration and Networking Breakfast
10:00 - 11:00
Introduction to Deep Learning
The session will cover the fundamental theory behind the Deep Learning techniques with topics ranging from sparse coding/filtering, autoencoders, convolutional Neural Networks and deep belief nets. 11:00 - 11:45
Architecture of Deep Feedforward Neural Networks (DFNN)
The session will provide detail explanation of the architecture of DFNN and demonstrate visual and mathematical representation of DFNN. 11:45 - 12:00
Coffee Break
12:00 - 01:00
Lab Session 5: Developing Computational Graph of DFNN using TensorFlow
This lab session is the extension of lab session 4 which builds on the simple MLP computational graph into very large or deep feed-forward network computational graph.
01:00 02:00
Lunch
02:00 - 03:15
Lab Session 6: Implementing DFNN using TensorFlow
The lab session extends the lab session 5 and implements learning algorithm with TensorFlow. It further demonstrates how to train the model and validate to check the accuracy of the model.
03:15 - 03:30
Coffee Break
03:30 - 05:00
Mini-Project 1: Developing Classification Model with DFNN and TensorFlow
A case study with a dataset will be provided to the learners and they are expected to use the skill they have learnt from all the lecturers and the lab sessions. The learners should work independently, however support and guidance will be provided to them to successfully complete the mini-project.
Day 3 09:30 - 10:00
Registration and Networking Breakfast
10:00 - 11:00
Introduction to Convolutional Neural Networks (CNN)
The session provides learners with clear understanding of CNN and how the CNN differs from other Neural Network paradigms. It also explains how CNN based applications work. 11:00 -11:45
Architecture of CNN
The session will provide detail explanation of the architecture of CNN and demonstrate visual and mathematical representation of CNN.
11:45 - 12:00
Coffee Break
12:00 - 01:00
Lab Session 7: Developing Computational Graph of CNN using TensorFlow
The lab session guides the learners to develop a CNN using TensorFlow. The aim of the session is to give learners hands on experience of developing CNN computational graph using TensorFlow. 01:00 02:00
Lunch
02:00 - 03:15
Lab Session 8: Implementing CNN using TensorFlow
The lab session extends the lab session 7 and implements learning algorithm with TensorFlow for CNN computational graph. It alsodemonstrates how to train the model and validate to check the accuracy of the model.
03:15 - 03:30
Coffee Break
03:30 - 05:00
Mini-Project 2: Developing Image Classification Model with CNN and TensorFlow
A case study with a dataset will be provided to the learners and they are expected to use the skill they have learnt from all the previous lecturers and the lab sessions. The learners should work independently, however support and guidance will be provided to themfor the successful completion of the mini-project.
Day 4 09:30 - 10:00
Registration and Networking Breakfast
10:00 - 11:00
Introduction to Recurrent Neural Networks (RNN)
The session provides learners with clear understanding of RNN and how the RNN differ from other neural network paradigms. It will also explain how RNN based applications work. 11:00 - 11:45
Architectures of RNN
The session will provide detail explanation of the architecture of RNN and demonstrate visual and mathematical representations of RNN.
11:45 - 12:00
Coffee Break
12:00 - 01:00
Lab Session 9: Developing Computational Graph of RNN using TensorFlow
The lab session guides the learners to develop a RNN using TensorFlow. The aim of the lab session is to give the learners hands on experience of developing RNN computational graph using TensorFlow.
01:00 02:00
Lunch
02:00 - 04:00
Lab Session 10: Implementing RNN using TensorFlow
The lab session extends the lab session 9 and implements learning algorithm with TensorFlow for RNN computational graph. Furthermore, it will show how to train the model and validate to check the accuracy of the model.
04:00 - 04:15
Coffee Break
04:15 - 05:00 TensorFlow
Mini-Project 3: Developing NLP Model with RNN and
A case study with a dataset will be provided to the learners and they are expected to use the skill they have learnt from all the previous lecturers and the lab sessions. The learners should work independently, however support and guidance will be provided to them for the successful completion of the mini-project.
