Getting started with TensorFlow
An introduction to TensorFlow and Machine Learning. The presentation originally used for the TIDx event 2018
@tidxconference
Following presentation gives the brief view about dynamic memory allocation used for allocating space at runtime.
Go through the slides hope it will be helpful to get the basic knowledge about the dynamic memory allocation.
Please comment and shares your views.
A complete guide for building machine learning and deep learning solutions using Tensorflow. This TensorFlow tutorial is designed for newbies and advanced users in which they will learn basics & difficult concepts of Tensorflow from scratch. Enroll now and let’s take a step into the future with TensorFlow!
Get the Course here : https://www.eduonix.com/tensorflow-for-beginners?coupon_code=discount15
https://www.eduonix.com/tensorflow-for-beginners?coupon_code=discount15
Machine learning the next revolution or just another hypeJorge Ferrer
These are the slides of my session and ModConf / Liferay DevCon 2016.
It attempts to make it easy for any developer to get started with Machine Learning. It presents three exercises which I'm giving as homework (yup, homework, you missed it, right? ;) to the audience.
The video for this session is now available at https://www.facebook.com/liferay/videos/vl.383534535315216/10154154247423108/?type=1 (starts at min 34)
Following presentation gives the brief view about dynamic memory allocation used for allocating space at runtime.
Go through the slides hope it will be helpful to get the basic knowledge about the dynamic memory allocation.
Please comment and shares your views.
A complete guide for building machine learning and deep learning solutions using Tensorflow. This TensorFlow tutorial is designed for newbies and advanced users in which they will learn basics & difficult concepts of Tensorflow from scratch. Enroll now and let’s take a step into the future with TensorFlow!
Get the Course here : https://www.eduonix.com/tensorflow-for-beginners?coupon_code=discount15
https://www.eduonix.com/tensorflow-for-beginners?coupon_code=discount15
Machine learning the next revolution or just another hypeJorge Ferrer
These are the slides of my session and ModConf / Liferay DevCon 2016.
It attempts to make it easy for any developer to get started with Machine Learning. It presents three exercises which I'm giving as homework (yup, homework, you missed it, right? ;) to the audience.
The video for this session is now available at https://www.facebook.com/liferay/videos/vl.383534535315216/10154154247423108/?type=1 (starts at min 34)
Natural language processing open seminar For Tensorflow usagehyunyoung Lee
This is presentation for Natural Language Processing open seminar in Kookmin University.
The open seminar reference : https://cafe.naver.com/nlpk
My presentation about how to use tensorflow for NLP open seminar for newbies for tensorflow.
Workshop about TensorFlow usage for AI Ukraine 2016. Brief tutorial with source code example. Described TensorFlow main ideas, terms, parameters. Example related with linear neuron model and learning using Adam optimization algorithm.
Language translation with Deep Learning (RNN) with TensorFlowS N
The author is going to take you into the realm of Recurrent Neural Network (RNN). He will be training a sequence to sequence model on a dataset of English and French sentences that can translate new (unseen) sentences from English to French.
This will be a walkthrough of an end to end technique to train a Deep RNN model. You will learn to build various components necessary to build a Sequence-to-Sequence model.
You will learn about the fundamentals of Deep Learning, mainly RNN, concepts that will be required in this solution. A familiarity of Deep Learning concepts would be handy, but most of the concepts used in this example will be covered during the demo.
Technologies to be used:
Python, Jupyter, TensorFlow, FloydHub
Source code: https://github.com/syednasar/deeplearning/blob/master/language-translation/dlnd_language_translation.ipynb
...
What is TensorFlow? | Introduction to TensorFlow | TensorFlow Tutorial For Be...Simplilearn
This presentation on TensorFlow will help you in understanding what exactly is TensorFlow and how it is used in Deep Learning. TensorFlow is a software library developed by Google for the purposes of conducting machine learning and deep neural network research. In this tutorial, you will learn the fundamentals of TensorFlow concepts, functions, and operations required to implement deep learning algorithms and leverage data like never before. This TensorFlow tutorial is ideal for beginners who want to pursue a career in Deep Learning. Now, let us deep dive into this TensorFlow tutorial and understand what TensorFlow actually is and how to use it.
Below topics are explained in this TensorFlow presentation:
1. What is Deep Learning?
2. Top Deep Learning Libraries
3. Why TensorFlow?
4. What is TensorFlow?
5. What are Tensors?
6. What is a Data Flow Graph?
7. Program Elements in TensorFlow
8. Use case implementation using TensorFlow
Simplilearn’s Deep Learning course will transform you into an expert in deep learning techniques using TensorFlow, the open-source software library designed to conduct machine learning & deep neural network research. With our deep learning course, you’ll master deep learning and TensorFlow concepts, learn to implement algorithms, build artificial neural networks and traverse layers of data abstraction to understand the power of data and prepare you for your new role as deep learning scientist.
Why Deep Learning?
It is one of the most popular software platforms used for deep learning and contains powerful tools to help you build and implement artificial neural networks.
You can gain in-depth knowledge of Deep Learning by taking our Deep Learning certification training course. With Simplilearn’s Deep Learning course, you will prepare for a career as a Deep Learning engineer as you master concepts and techniques including supervised and unsupervised learning, mathematical and heuristic aspects, and hands-on modeling to develop algorithms. Those who complete the course will be able to:
1. Understand the concepts of TensorFlow, its main functions, operations and the execution pipeline
2. Implement deep learning algorithms, understand neural networks and traverse the layers of data abstraction which will empower you to understand data like never before
3. Master and comprehend advanced topics such as convolutional neural networks, recurrent neural networks, training deep networks and high-level interfaces
4. Build deep learning models in TensorFlow and interpret the results
5. Understand the language and fundamental concepts of artificial neural networks
6. Troubleshoot and improve deep learning models
7. Build your own deep learning project
8. Differentiate between machine learning, deep learning and artificial intelligence
Learn more at: https://www.simplilearn.com
In this talk, I will share my journey of using machine learning in Java to build a visual recognition system that can identify Lego blocks. As a Java developer, I wanted to use Java for this project rather than Python, which is more commonly used for machine learning projects. I will explain the basics of machine learning and give an overview of the current Java libraries for machine learning and transferring pre-trained models. I will demonstrate how to train and modify existing models using transfer learning. The goal of the project is to create a Java solution that can identify the top 1000 most popular Lego bricks. I will explain all of this without using any complex mathematical formulas, making it accessible to those with no prior knowledge of machine learning.
For the full video of this presentation, please visit:
http://www.embedded-vision.com/platinum-members/embedded-vision-alliance/embedded-vision-training/videos/pages/may-2016-embedded-vision-summit-google-keynote
For more information about embedded vision, please visit:
http://www.embedded-vision.com
Jeff Dean, Senior Fellow at Google, presents the "Large-Scale Deep Learning for Building Intelligent Computer Systems" keynote at the May 2016 Embedded Vision Summit.
Over the past few years, Google has built two generations of large-scale computer systems for training neural networks, and then applied these systems to a wide variety of research problems that have traditionally been very difficult for computers. Google has released its second generation system, TensorFlow, as an open source project, and is now collaborating with a growing community on improving and extending its functionality. Using TensorFlow, Google's research group has made significant improvements in the state-of-the-art in many areas, and dozens of different groups at Google use it to train state-of-the-art models for speech recognition, image recognition, various visual detection tasks, language modeling, language translation, and many other tasks.
In this talk, Jeff highlights some of ways that Google trains large models quickly on large datasets, and discusses different approaches for deploying machine learning models in environments ranging from large datacenters to mobile devices. He will then discuss ways in which Google has applied this work to a variety of problems in Google's products, usually in close collaboration with other teams. This talk describes joint work with many people at Google.
Introduction To TensorFlow | Deep Learning with TensorFlow | TensorFlow For B...Edureka!
** AI & Deep Learning with Tensorflow Training: https://goo.gl/vDxgi5 **
This Edureka tutorial on "Introduction to TensorFlow" provides you an insight into one of the top Deep Learning frameworks that you should consider learning!
Check out our Deep Learning blog series: https://bit.ly/2xVIMe1
Check out our complete Youtube playlist here: https://bit.ly/2OhZEpz
Follow us to never miss an update in the future.
Instagram: https://www.instagram.com/edureka_learning/
Facebook: https://www.facebook.com/edurekaIN/
Twitter: https://twitter.com/edurekain
LinkedIn: https://www.linkedin.com/company/edureka
Live coding session on AI / ML using Google Tensorflow (Python) - Tanmoy Deb ...Tech Triveni
Build your own system, extend your existing systems through ML capabilities. Machine learning is about teaching computers how to learn from data to make decisions or predictions. For true machine learning, the computer must be able to learn to identify patterns without being explicitly programmed to. The topic will give briefly cover how anybody can learn and start their journey in AI, discussing the opportunities and challenges from point of view of a student, developer, and entrepreneur. The take away from the session will be to take that first step beyond the question- "where to start? There is too much out there!". This will be given with a demo that how most popular framework for Deep Learning works.
Introduction to the new Tensorflow 2.x and the Coral AI Edge TPU hardware. The presentation introduces Tensorflow main features such as Sequential and Functional APIs, mobile support with Tensorflow Lite, web support with TensorflowJS and Google Cloud support with TFX.
In addition, the presentation introduces the new edge TPU architecture coming from Coral AI, including its main hardware features and description of the compiling flow.
Natural language processing open seminar For Tensorflow usagehyunyoung Lee
This is presentation for Natural Language Processing open seminar in Kookmin University.
The open seminar reference : https://cafe.naver.com/nlpk
My presentation about how to use tensorflow for NLP open seminar for newbies for tensorflow.
Workshop about TensorFlow usage for AI Ukraine 2016. Brief tutorial with source code example. Described TensorFlow main ideas, terms, parameters. Example related with linear neuron model and learning using Adam optimization algorithm.
Language translation with Deep Learning (RNN) with TensorFlowS N
The author is going to take you into the realm of Recurrent Neural Network (RNN). He will be training a sequence to sequence model on a dataset of English and French sentences that can translate new (unseen) sentences from English to French.
This will be a walkthrough of an end to end technique to train a Deep RNN model. You will learn to build various components necessary to build a Sequence-to-Sequence model.
You will learn about the fundamentals of Deep Learning, mainly RNN, concepts that will be required in this solution. A familiarity of Deep Learning concepts would be handy, but most of the concepts used in this example will be covered during the demo.
Technologies to be used:
Python, Jupyter, TensorFlow, FloydHub
Source code: https://github.com/syednasar/deeplearning/blob/master/language-translation/dlnd_language_translation.ipynb
...
What is TensorFlow? | Introduction to TensorFlow | TensorFlow Tutorial For Be...Simplilearn
This presentation on TensorFlow will help you in understanding what exactly is TensorFlow and how it is used in Deep Learning. TensorFlow is a software library developed by Google for the purposes of conducting machine learning and deep neural network research. In this tutorial, you will learn the fundamentals of TensorFlow concepts, functions, and operations required to implement deep learning algorithms and leverage data like never before. This TensorFlow tutorial is ideal for beginners who want to pursue a career in Deep Learning. Now, let us deep dive into this TensorFlow tutorial and understand what TensorFlow actually is and how to use it.
Below topics are explained in this TensorFlow presentation:
1. What is Deep Learning?
2. Top Deep Learning Libraries
3. Why TensorFlow?
4. What is TensorFlow?
5. What are Tensors?
6. What is a Data Flow Graph?
7. Program Elements in TensorFlow
8. Use case implementation using TensorFlow
Simplilearn’s Deep Learning course will transform you into an expert in deep learning techniques using TensorFlow, the open-source software library designed to conduct machine learning & deep neural network research. With our deep learning course, you’ll master deep learning and TensorFlow concepts, learn to implement algorithms, build artificial neural networks and traverse layers of data abstraction to understand the power of data and prepare you for your new role as deep learning scientist.
Why Deep Learning?
It is one of the most popular software platforms used for deep learning and contains powerful tools to help you build and implement artificial neural networks.
You can gain in-depth knowledge of Deep Learning by taking our Deep Learning certification training course. With Simplilearn’s Deep Learning course, you will prepare for a career as a Deep Learning engineer as you master concepts and techniques including supervised and unsupervised learning, mathematical and heuristic aspects, and hands-on modeling to develop algorithms. Those who complete the course will be able to:
1. Understand the concepts of TensorFlow, its main functions, operations and the execution pipeline
2. Implement deep learning algorithms, understand neural networks and traverse the layers of data abstraction which will empower you to understand data like never before
3. Master and comprehend advanced topics such as convolutional neural networks, recurrent neural networks, training deep networks and high-level interfaces
4. Build deep learning models in TensorFlow and interpret the results
5. Understand the language and fundamental concepts of artificial neural networks
6. Troubleshoot and improve deep learning models
7. Build your own deep learning project
8. Differentiate between machine learning, deep learning and artificial intelligence
Learn more at: https://www.simplilearn.com
In this talk, I will share my journey of using machine learning in Java to build a visual recognition system that can identify Lego blocks. As a Java developer, I wanted to use Java for this project rather than Python, which is more commonly used for machine learning projects. I will explain the basics of machine learning and give an overview of the current Java libraries for machine learning and transferring pre-trained models. I will demonstrate how to train and modify existing models using transfer learning. The goal of the project is to create a Java solution that can identify the top 1000 most popular Lego bricks. I will explain all of this without using any complex mathematical formulas, making it accessible to those with no prior knowledge of machine learning.
For the full video of this presentation, please visit:
http://www.embedded-vision.com/platinum-members/embedded-vision-alliance/embedded-vision-training/videos/pages/may-2016-embedded-vision-summit-google-keynote
For more information about embedded vision, please visit:
http://www.embedded-vision.com
Jeff Dean, Senior Fellow at Google, presents the "Large-Scale Deep Learning for Building Intelligent Computer Systems" keynote at the May 2016 Embedded Vision Summit.
Over the past few years, Google has built two generations of large-scale computer systems for training neural networks, and then applied these systems to a wide variety of research problems that have traditionally been very difficult for computers. Google has released its second generation system, TensorFlow, as an open source project, and is now collaborating with a growing community on improving and extending its functionality. Using TensorFlow, Google's research group has made significant improvements in the state-of-the-art in many areas, and dozens of different groups at Google use it to train state-of-the-art models for speech recognition, image recognition, various visual detection tasks, language modeling, language translation, and many other tasks.
In this talk, Jeff highlights some of ways that Google trains large models quickly on large datasets, and discusses different approaches for deploying machine learning models in environments ranging from large datacenters to mobile devices. He will then discuss ways in which Google has applied this work to a variety of problems in Google's products, usually in close collaboration with other teams. This talk describes joint work with many people at Google.
Introduction To TensorFlow | Deep Learning with TensorFlow | TensorFlow For B...Edureka!
** AI & Deep Learning with Tensorflow Training: https://goo.gl/vDxgi5 **
This Edureka tutorial on "Introduction to TensorFlow" provides you an insight into one of the top Deep Learning frameworks that you should consider learning!
Check out our Deep Learning blog series: https://bit.ly/2xVIMe1
Check out our complete Youtube playlist here: https://bit.ly/2OhZEpz
Follow us to never miss an update in the future.
Instagram: https://www.instagram.com/edureka_learning/
Facebook: https://www.facebook.com/edurekaIN/
Twitter: https://twitter.com/edurekain
LinkedIn: https://www.linkedin.com/company/edureka
Live coding session on AI / ML using Google Tensorflow (Python) - Tanmoy Deb ...Tech Triveni
Build your own system, extend your existing systems through ML capabilities. Machine learning is about teaching computers how to learn from data to make decisions or predictions. For true machine learning, the computer must be able to learn to identify patterns without being explicitly programmed to. The topic will give briefly cover how anybody can learn and start their journey in AI, discussing the opportunities and challenges from point of view of a student, developer, and entrepreneur. The take away from the session will be to take that first step beyond the question- "where to start? There is too much out there!". This will be given with a demo that how most popular framework for Deep Learning works.
Introduction to the new Tensorflow 2.x and the Coral AI Edge TPU hardware. The presentation introduces Tensorflow main features such as Sequential and Functional APIs, mobile support with Tensorflow Lite, web support with TensorflowJS and Google Cloud support with TFX.
In addition, the presentation introduces the new edge TPU architecture coming from Coral AI, including its main hardware features and description of the compiling flow.
Understanding Inductive Bias in Machine LearningSUTEJAS
This presentation explores the concept of inductive bias in machine learning. It explains how algorithms come with built-in assumptions and preferences that guide the learning process. You'll learn about the different types of inductive bias and how they can impact the performance and generalizability of machine learning models.
The presentation also covers the positive and negative aspects of inductive bias, along with strategies for mitigating potential drawbacks. We'll explore examples of how bias manifests in algorithms like neural networks and decision trees.
By understanding inductive bias, you can gain valuable insights into how machine learning models work and make informed decisions when building and deploying them.
KuberTENes Birthday Bash Guadalajara - K8sGPT first impressionsVictor Morales
K8sGPT is a tool that analyzes and diagnoses Kubernetes clusters. This presentation was used to share the requirements and dependencies to deploy K8sGPT in a local environment.
Using recycled concrete aggregates (RCA) for pavements is crucial to achieving sustainability. Implementing RCA for new pavement can minimize carbon footprint, conserve natural resources, reduce harmful emissions, and lower life cycle costs. Compared to natural aggregate (NA), RCA pavement has fewer comprehensive studies and sustainability assessments.
NUMERICAL SIMULATIONS OF HEAT AND MASS TRANSFER IN CONDENSING HEAT EXCHANGERS...ssuser7dcef0
Power plants release a large amount of water vapor into the
atmosphere through the stack. The flue gas can be a potential
source for obtaining much needed cooling water for a power
plant. If a power plant could recover and reuse a portion of this
moisture, it could reduce its total cooling water intake
requirement. One of the most practical way to recover water
from flue gas is to use a condensing heat exchanger. The power
plant could also recover latent heat due to condensation as well
as sensible heat due to lowering the flue gas exit temperature.
Additionally, harmful acids released from the stack can be
reduced in a condensing heat exchanger by acid condensation. reduced in a condensing heat exchanger by acid condensation.
Condensation of vapors in flue gas is a complicated
phenomenon since heat and mass transfer of water vapor and
various acids simultaneously occur in the presence of noncondensable
gases such as nitrogen and oxygen. Design of a
condenser depends on the knowledge and understanding of the
heat and mass transfer processes. A computer program for
numerical simulations of water (H2O) and sulfuric acid (H2SO4)
condensation in a flue gas condensing heat exchanger was
developed using MATLAB. Governing equations based on
mass and energy balances for the system were derived to
predict variables such as flue gas exit temperature, cooling
water outlet temperature, mole fraction and condensation rates
of water and sulfuric acid vapors. The equations were solved
using an iterative solution technique with calculations of heat
and mass transfer coefficients and physical properties.
6. Machine Learning Pseudocode
labeled_data = ... // f.e. X: square_meters -> y: house_prices
model = ... // f.e. y = B * x + A
// Training
do N times: // epochs
for d in labeled_data: // usually in batches
y_predicted = model.predict(d)
model.adjust(y_predicted - y, learning_rate)
// Inference
model.predict(unseen_data)
19. Real world TensorFlow
1. Find a model to the type of problem you want to solve
2. Obtain as much good data as possible
3. Train the model (or retrain)
4. Save the model and deploy it