An introduction to Google's AI Engine, look deeper into Artificial Networks and Machine Learning. Appreciate how our simplest neural network be codified and be used to data analytics.
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.
Rajat Monga at AI Frontiers: Deep Learning with TensorFlowAI Frontiers
In this talk at AI Frontiers Conference, Rajat Monga shares about TensorFlow that has enabled cutting-edge machine learning research at the top AI labs in the world. At the same time it has made the technology accessible to a large audience leading to some amazing uses. TensorFlow is used for classification, recommendation, text parsing, sentiment analysis and more. This talk goes over the design that makes it fast, flexible, and easy to use, and describe how we continue to make it better.
Abstract: This PDSG workshop introduces basic concepts on TensorFlow. The course covers fundamentals. Concepts covered are Vectors/Matrices/Vectors, Design&Run, Constants, Operations, Placeholders, Bindings, Operators, Loss Function and Training.
Level: Fundamental
Requirements: Some basic programming knowledge is preferred. No prior statistics background is required.
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.
Rajat Monga at AI Frontiers: Deep Learning with TensorFlowAI Frontiers
In this talk at AI Frontiers Conference, Rajat Monga shares about TensorFlow that has enabled cutting-edge machine learning research at the top AI labs in the world. At the same time it has made the technology accessible to a large audience leading to some amazing uses. TensorFlow is used for classification, recommendation, text parsing, sentiment analysis and more. This talk goes over the design that makes it fast, flexible, and easy to use, and describe how we continue to make it better.
Abstract: This PDSG workshop introduces basic concepts on TensorFlow. The course covers fundamentals. Concepts covered are Vectors/Matrices/Vectors, Design&Run, Constants, Operations, Placeholders, Bindings, Operators, Loss Function and Training.
Level: Fundamental
Requirements: Some basic programming knowledge is preferred. No prior statistics background is required.
Introduction to TensorFlow, by Machine Learning at BerkeleyTed Xiao
A workshop introducing the TensorFlow Machine Learning framework. Presented by Brenton Chu, Vice President of Machine Learning at Berkeley.
This presentation cover show to construct, train, evaluate, and visualize neural networks in TensorFlow 1.0
http://ml.berkeley.edu
Deep Learning with TensorFlow: Understanding Tensors, Computations Graphs, Im...Altoros
1. The elements of Neural Networks: Weights, Biases, and Gating functions
2. MNIST (Hand writing recognition) using simple NN in TensorFlow (Introduce Tensors, Computation Graphs)
3. MNIST using Convolution NN in TensorFlow
4. Understanding words and sentences as Vectors
5. word2vec in TensorFlow
Introduction to Deep Learning, Keras, and TensorFlowSri Ambati
This meetup was recorded in San Francisco on Jan 9, 2019.
Video recording of the session can be viewed here: https://youtu.be/yG1UJEzpJ64
Description:
This fast-paced session starts with a simple yet complete neural network (no frameworks), followed by an overview of activation functions, cost functions, backpropagation, and then a quick dive into CNNs. Next, we'll create a neural network using Keras, followed by an introduction to TensorFlow and TensorBoard. For best results, familiarity with basic vectors and matrices, inner (aka "dot") products of vectors, and rudimentary Python is definitely helpful. If time permits, we'll look at the UAT, CLT, and the Fixed Point Theorem. (Bonus points if you know Zorn's Lemma, the Well-Ordering Theorem, and the Axiom of Choice.)
Oswald's Bio:
Oswald Campesato is an education junkie: a former Ph.D. Candidate in Mathematics (ABD), with multiple Master's and 2 Bachelor's degrees. In a previous career, he worked in South America, Italy, and the French Riviera, which enabled him to travel to 70 countries throughout the world.
He has worked in American and Japanese corporations and start-ups, as C/C++ and Java developer to CTO. He works in the web and mobile space, conducts training sessions in Android, Java, Angular 2, and ReactJS, and he writes graphics code for fun. He's comfortable in four languages and aspires to become proficient in Japanese, ideally sometime in the next two decades. He enjoys collaborating with people who share his passion for learning the latest cool stuff, and he's currently working on his 15th book, which is about Angular 2.
A fast-paced introduction to Deep Learning concepts, such as activation functions, cost functions, back propagation, followed by some TensorFlow features, and then a code sample of training a CNN in tensorflow.js. Basic knowledge of vectors, matrices, and derivatives is helpful in order to derive the maximum benefit from this session.
Explanation on Tensorflow example -Deep mnist for expert홍배 김
you can find the exact and detailed network architecture of 'Deep mnist for expert' example of tensorflow's tutorial. I also added descriptions on the program for your better understanding.
Presentation on Neural Networks in Tensorflow. Code available at https://github.com/nfmcclure/tensorflow_cookbook . Presentation for Open Source Bridge, Portland, 2016.
Introduction to Deep Learning, Keras, and TensorflowOswald Campesato
A fast-paced introduction to Deep Learning concepts, such as activation functions, cost functions, back propagation, and then a quick dive into CNNs. Basic knowledge of vectors, matrices, and derivatives is helpful in order to derive the maximum benefit from this session. Then we'll see how to create a Convolutional Neural Network in Keras, followed by a quick introduction to TensorFlow and TensorBoard.
Video: https://youtu.be/dYhrCUFN0eM
Article: https://medium.com/p/the-gentlest-introduction-to-tensorflow-248dc871a224
Code: https://github.com/nethsix/gentle_tensorflow/blob/master/code/linear_regression_one_feature.py
This alternative introduction to Google's official Tensorflow (TF) tutorial strips away the unnecessary concepts that overly complicates getting started. The goal is to use TF to perform Linear Regression (LR) that has only a single-feature. We show how to model the LR using a TF graph, how to define the cost function to measure how well the an LR model fits the dataset, and finally train the LR model to find the best fit model.
Introduction To TensorFlow | Deep Learning Using TensorFlow | CloudxLabCloudxLab
( Machine Learning & Deep Learning Specialization Training: https://goo.gl/6n3vko )
This CloudxLab TensorFlow tutorial helps you to understand TensorFlow in detail. Below are the topics covered in this tutorial:
1) Why TensorFlow?
2) What are Tensors?
3) What is TensorFlow?
4) Creating your First Graph
5) Linear Regression with TensorFlow
6) Implementing Gradient Descent using TensorFlow
7) Implementing Gradient Descent Using autodiff
8) Implementing Gradient Descent Using an Optimizer
9) Graph Visualization using TensorBoard
10) Name Scopes in TensorFlow
11) Modularity in TensorFlow
12) Sharing Variables in TensorFlow
A fast-paced introduction to Deep Learning concepts, such as activation functions, cost functions, back propagation, and then a quick dive into CNNs, followed by a Keras code sample for defining a CNN. Basic knowledge of vectors, matrices, and derivatives is helpful in order to derive the maximum benefit from this session. Then we'll see a short introduction to TensorFlow 1.x and some insights into TF 2 that will be released some time this year.
Gentlest Introduction to Tensorflow - Part 3Khor SoonHin
Articles:
* https://medium.com/all-of-us-are-belong-to-machines/gentlest-intro-to-tensorflow-part-3-matrices-multi-feature-linear-regression-30a81ebaaa6c
* https://medium.com/all-of-us-are-belong-to-machines/gentlest-intro-to-tensorflow-4-logistic-regression-2afd0cabc54
Video: https://youtu.be/F8g_6TXKlxw
Code: https://github.com/nethsix/gentle_tensorflow
In this part, we:
* Use Tensorflow for linear regression models with multiple features
* Use Tensorflow for logistic regression models with multiple features. Specifically:
* Predict multi-class/discrete outcome
* Explain why we use cross-entropy as cost function
* Explain why we use softmax
* Tensorflow Cheatsheet #1
* Single feature linear regression
* Multi-feature linear regression
* Multi-feature logistic regression
This session for beginners introduces tf.data APIs for creating data pipelines by combining various "lazy operators" in tf.data, such as filter(), map(), batch(), zip(), flatmap(), take(), and so forth.
Familiarity with method chaining and TF2 is helpful (but not required). If you are comfortable with FRP, the code samples in this session will be very familiar to you.
The release of TensorFlow 2.0 comes with a significant number of improvements over its 1.x version, all with a focus on ease of usability and a better user experience. We will give an overview of what TensorFlow 2.0 is and discuss how to get started building models from scratch using TensorFlow 2.0’s high-level api, Keras. We will walk through an example step-by-step in Python of how to build an image classifier. We will then showcase how to leverage a transfer learning to make building a model even easier! With transfer learning, we can leverage other pretrained models such as ImageNet to drastically speed up the training time of our model. TensorFlow 2.0 makes this incredibly simple to do.
A fast-paced introduction to TensorFlow 2 about some important new features (such as generators and the @tf.function decorator) and TF 1.x functionality that's been removed from TF 2 (yes, tf.Session() has retired).
Concise code samples are presented to illustrate how to use new features of TensorFlow 2. You'll also get a quick introduction to lazy operators (if you know FRP this will be super easy), along with a code comparison between TF 1.x/iterators with tf.data.Dataset and TF 2/generators with tf.data.Dataset.
Finally, we'll look at some tf.keras code samples that are based on TensorFlow 2. Although familiarity with TF 1.x is helpful, newcomers with an avid interest in learning about TensorFlow 2 can benefit from this session.
This presentation focuses on Deep Learning (DL) concepts, such as neural neworks, backprop, activation functions, and Convolutional Neural Networks, with a short introduction to D3, and followed by a TypeScript-based code sample that replicates the TensorFlow playground. Basic knowledge of matrices is helpful.
TensorFlow에 대한 분석 내용
- TensorFlow?
- 배경
- DistBelief
- Tutorial - Logistic regression
- TensorFlow - 내부적으로는
- Tutorial - CNN, RNN
- Benchmarks
- 다른 오픈 소스들
- TensorFlow를 고려한다면
- 설치
- 참고 자료
Introduction to TensorFlow, by Machine Learning at BerkeleyTed Xiao
A workshop introducing the TensorFlow Machine Learning framework. Presented by Brenton Chu, Vice President of Machine Learning at Berkeley.
This presentation cover show to construct, train, evaluate, and visualize neural networks in TensorFlow 1.0
http://ml.berkeley.edu
Deep Learning with TensorFlow: Understanding Tensors, Computations Graphs, Im...Altoros
1. The elements of Neural Networks: Weights, Biases, and Gating functions
2. MNIST (Hand writing recognition) using simple NN in TensorFlow (Introduce Tensors, Computation Graphs)
3. MNIST using Convolution NN in TensorFlow
4. Understanding words and sentences as Vectors
5. word2vec in TensorFlow
Introduction to Deep Learning, Keras, and TensorFlowSri Ambati
This meetup was recorded in San Francisco on Jan 9, 2019.
Video recording of the session can be viewed here: https://youtu.be/yG1UJEzpJ64
Description:
This fast-paced session starts with a simple yet complete neural network (no frameworks), followed by an overview of activation functions, cost functions, backpropagation, and then a quick dive into CNNs. Next, we'll create a neural network using Keras, followed by an introduction to TensorFlow and TensorBoard. For best results, familiarity with basic vectors and matrices, inner (aka "dot") products of vectors, and rudimentary Python is definitely helpful. If time permits, we'll look at the UAT, CLT, and the Fixed Point Theorem. (Bonus points if you know Zorn's Lemma, the Well-Ordering Theorem, and the Axiom of Choice.)
Oswald's Bio:
Oswald Campesato is an education junkie: a former Ph.D. Candidate in Mathematics (ABD), with multiple Master's and 2 Bachelor's degrees. In a previous career, he worked in South America, Italy, and the French Riviera, which enabled him to travel to 70 countries throughout the world.
He has worked in American and Japanese corporations and start-ups, as C/C++ and Java developer to CTO. He works in the web and mobile space, conducts training sessions in Android, Java, Angular 2, and ReactJS, and he writes graphics code for fun. He's comfortable in four languages and aspires to become proficient in Japanese, ideally sometime in the next two decades. He enjoys collaborating with people who share his passion for learning the latest cool stuff, and he's currently working on his 15th book, which is about Angular 2.
A fast-paced introduction to Deep Learning concepts, such as activation functions, cost functions, back propagation, followed by some TensorFlow features, and then a code sample of training a CNN in tensorflow.js. Basic knowledge of vectors, matrices, and derivatives is helpful in order to derive the maximum benefit from this session.
Explanation on Tensorflow example -Deep mnist for expert홍배 김
you can find the exact and detailed network architecture of 'Deep mnist for expert' example of tensorflow's tutorial. I also added descriptions on the program for your better understanding.
Presentation on Neural Networks in Tensorflow. Code available at https://github.com/nfmcclure/tensorflow_cookbook . Presentation for Open Source Bridge, Portland, 2016.
Introduction to Deep Learning, Keras, and TensorflowOswald Campesato
A fast-paced introduction to Deep Learning concepts, such as activation functions, cost functions, back propagation, and then a quick dive into CNNs. Basic knowledge of vectors, matrices, and derivatives is helpful in order to derive the maximum benefit from this session. Then we'll see how to create a Convolutional Neural Network in Keras, followed by a quick introduction to TensorFlow and TensorBoard.
Video: https://youtu.be/dYhrCUFN0eM
Article: https://medium.com/p/the-gentlest-introduction-to-tensorflow-248dc871a224
Code: https://github.com/nethsix/gentle_tensorflow/blob/master/code/linear_regression_one_feature.py
This alternative introduction to Google's official Tensorflow (TF) tutorial strips away the unnecessary concepts that overly complicates getting started. The goal is to use TF to perform Linear Regression (LR) that has only a single-feature. We show how to model the LR using a TF graph, how to define the cost function to measure how well the an LR model fits the dataset, and finally train the LR model to find the best fit model.
Introduction To TensorFlow | Deep Learning Using TensorFlow | CloudxLabCloudxLab
( Machine Learning & Deep Learning Specialization Training: https://goo.gl/6n3vko )
This CloudxLab TensorFlow tutorial helps you to understand TensorFlow in detail. Below are the topics covered in this tutorial:
1) Why TensorFlow?
2) What are Tensors?
3) What is TensorFlow?
4) Creating your First Graph
5) Linear Regression with TensorFlow
6) Implementing Gradient Descent using TensorFlow
7) Implementing Gradient Descent Using autodiff
8) Implementing Gradient Descent Using an Optimizer
9) Graph Visualization using TensorBoard
10) Name Scopes in TensorFlow
11) Modularity in TensorFlow
12) Sharing Variables in TensorFlow
A fast-paced introduction to Deep Learning concepts, such as activation functions, cost functions, back propagation, and then a quick dive into CNNs, followed by a Keras code sample for defining a CNN. Basic knowledge of vectors, matrices, and derivatives is helpful in order to derive the maximum benefit from this session. Then we'll see a short introduction to TensorFlow 1.x and some insights into TF 2 that will be released some time this year.
Gentlest Introduction to Tensorflow - Part 3Khor SoonHin
Articles:
* https://medium.com/all-of-us-are-belong-to-machines/gentlest-intro-to-tensorflow-part-3-matrices-multi-feature-linear-regression-30a81ebaaa6c
* https://medium.com/all-of-us-are-belong-to-machines/gentlest-intro-to-tensorflow-4-logistic-regression-2afd0cabc54
Video: https://youtu.be/F8g_6TXKlxw
Code: https://github.com/nethsix/gentle_tensorflow
In this part, we:
* Use Tensorflow for linear regression models with multiple features
* Use Tensorflow for logistic regression models with multiple features. Specifically:
* Predict multi-class/discrete outcome
* Explain why we use cross-entropy as cost function
* Explain why we use softmax
* Tensorflow Cheatsheet #1
* Single feature linear regression
* Multi-feature linear regression
* Multi-feature logistic regression
This session for beginners introduces tf.data APIs for creating data pipelines by combining various "lazy operators" in tf.data, such as filter(), map(), batch(), zip(), flatmap(), take(), and so forth.
Familiarity with method chaining and TF2 is helpful (but not required). If you are comfortable with FRP, the code samples in this session will be very familiar to you.
The release of TensorFlow 2.0 comes with a significant number of improvements over its 1.x version, all with a focus on ease of usability and a better user experience. We will give an overview of what TensorFlow 2.0 is and discuss how to get started building models from scratch using TensorFlow 2.0’s high-level api, Keras. We will walk through an example step-by-step in Python of how to build an image classifier. We will then showcase how to leverage a transfer learning to make building a model even easier! With transfer learning, we can leverage other pretrained models such as ImageNet to drastically speed up the training time of our model. TensorFlow 2.0 makes this incredibly simple to do.
A fast-paced introduction to TensorFlow 2 about some important new features (such as generators and the @tf.function decorator) and TF 1.x functionality that's been removed from TF 2 (yes, tf.Session() has retired).
Concise code samples are presented to illustrate how to use new features of TensorFlow 2. You'll also get a quick introduction to lazy operators (if you know FRP this will be super easy), along with a code comparison between TF 1.x/iterators with tf.data.Dataset and TF 2/generators with tf.data.Dataset.
Finally, we'll look at some tf.keras code samples that are based on TensorFlow 2. Although familiarity with TF 1.x is helpful, newcomers with an avid interest in learning about TensorFlow 2 can benefit from this session.
This presentation focuses on Deep Learning (DL) concepts, such as neural neworks, backprop, activation functions, and Convolutional Neural Networks, with a short introduction to D3, and followed by a TypeScript-based code sample that replicates the TensorFlow playground. Basic knowledge of matrices is helpful.
TensorFlow에 대한 분석 내용
- TensorFlow?
- 배경
- DistBelief
- Tutorial - Logistic regression
- TensorFlow - 내부적으로는
- Tutorial - CNN, RNN
- Benchmarks
- 다른 오픈 소스들
- TensorFlow를 고려한다면
- 설치
- 참고 자료
Literally, Kanban is a Japanese word that means "visual card". At Toyota, Kanban is the term used for the visual & physical signaling system that ties together the whole Lean Production system. Kanban as used in Lean Production is over a half century old. It is being adopted newly to some disciplines as software.
Formal Concept Analysis is a method used for investigating and processing explicitly given information, in order to allow for meaningful and comprehensive interpretation.
Smart Information and Communication Technology (ICT) is a new holistic approach on ICT development, integration and implementation where it harmonizes with Science and Technology, to produce new products, service, enhance workflow and improve human life. With this approach it enables inclusiveness on growth and sustainability on society development, where it enables equal access to technology and its innovations by bridging the gaps on how we do ICT in the past.
presented by Dr. Roland Buresh of International Rice Research Institute during the 2015 AFNR Symposium held last September 30, 2015 at the AIM Makati City.
Cloud security From Infrastructure to People-wareTzar Umang
Understand Cloud Security in every level from infrastructure to people ware via understanding threats, hardening your servers and creating policies that will users be guided on securing themselves.
Kaz Sato, Evangelist, Google at MLconf ATL 2016MLconf
Machine Intelligence at Google Scale: Tensor Flow and Cloud Machine Learning: The biggest challenge of Deep Learning technology is the scalability. As long as using single GPU server, you have to wait for hours or days to get the result of your work. This doesn’t scale for production service, so you need a Distributed Training on the cloud eventually. Google has been building infrastructure for training the large scale neural network on the cloud for years, and now started to share the technology with external developers. In this session, we will introduce new pre-trained ML services such as Cloud Vision API and Speech API that works without any training. Also, we will look how TensorFlow and Cloud Machine Learning will accelerate custom model training for 10x – 40x with Google’s distributed training infrastructure.
A Smart City is a Future Vision of developed urban area, anchored on sustainable and inclusive economic development, and yielding a high quality of life for all by excelling in multiple complementing dimensions; Governance, People, Economy, Mobility, Environment and Living
Jose Leiva, data scientist at Ets Asset Management Factory, gives an accurate and simple introduction to Machine Learning. He explains some of the problems that quantitative managers have to get alpha in the markets, and how to face them using Deep Learning.
Introducton to Convolutional Nerural Network with TensorFlowEtsuji Nakai
Explaining basic mechanism of the Convolutional Neural Network with sample TesnsorFlow codes.
Sample codes: https://github.com/enakai00/cnn_introduction
This fast-paced session starts with an introduction to neural networks and linear regression models, along with a quick view of TensorFlow, followed by some Scala APIs for TensorFlow. You'll also see a simple dockerized image of Scala and TensorFlow code and how to execute the code in that image from the command line. No prior knowledge of NNs, Keras, or TensorFlow is required (but you must be comfortable with Scala).
Need help filling out the missing sections of this code- the sections.docxlauracallander
Need help filling out the missing sections of this code. the sections missing are step 6, 7, and 9.
Step 1: Load the Tox21 Dataset.
import numpy as np
np.random.seed(456)
import tensorflow as tf
tf.set_random_seed(456)
import matplotlib.pyplot as plt
import deepchem as dc
from sklearn.metrics import accuracy_score
_, (train, valid, test), _ = dc.molnet.load_tox21()
train_X, train_y, train_w = train.X, train.y, train.w
valid_X, valid_y, valid_w = valid.X, valid.y, valid.w
test_X, test_y, test_w = test.X, test.y, test.w
Step 2: Remove extra datasets.
# Remove extra tasks
train_y = train_y[:, 0]
valid_y = valid_y[:, 0]
test_y = test_y[:, 0]
train_w = train_w[:, 0]
valid_w = valid_w[:, 0]
test_w = test_w[:, 0]
Step 3: Define placeholders that accept minibatches of different sizes.
# Generate tensorflow graph
d = 1024
n_hidden = 50
learning_rate = .001
n_epochs = 10
batch_size = 100
with tf.name_scope("placeholders"):
x = tf.placeholder(tf.float32, (None, d))
y = tf.placeholder(tf.float32, (None,))
Step 4: Implement a hidden layer.
with tf.name_scope("hidden-layer"):
W = tf.Variable(tf.random_normal((d, n_hidden)))
b = tf.Variable(tf.random_normal((n_hidden,)))
x_hidden = tf.nn.relu(tf.matmul(x, W) + b)
Step 5: Complete the fully connected architecture.
with tf.name_scope("output"):
W = tf.Variable(tf.random_normal((n_hidden, 1)))
b = tf.Variable(tf.random_normal((1,)))
y_logit = tf.matmul(x_hidden, W) + b
# the sigmoid gives the class probability of 1
y_one_prob = tf.sigmoid(y_logit)
# Rounding P(y=1) will give the correct prediction.
y_pred = tf.round(y_one_prob)
with tf.name_scope("loss"):
# Compute the cross-entropy term for each datapoint
y_expand = tf.expand_dims(y, 1)
entropy = tf.nn.sigmoid_cross_entropy_with_logits(logits=y_logit, labels=y_expand)
# Sum all contributions
l = tf.reduce_sum(entropy)
with tf.name_scope("optim"):
train_op = tf.train.AdamOptimizer(learning_rate).minimize(l)
with tf.name_scope("summaries"):
tf.summary.scalar("loss", l)
merged = tf.summary.merge_all()
Step 6: Add dropout to a hidden layer.
Step 7: Define a hidden layer with dropout.
Step 8: Implement mini-batching training.
train_writer = tf.summary.FileWriter('/tmp/fcnet-tox21',
tf.get_default_graph())
N = train_X.shape[0]
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
step = 0
for epoch in range(n_epochs):
pos = 0
while pos N:
batch_X = train_X[pos:pos+batch_size]
batch_y = train_y[pos:pos+batch_size]
feed_dict = {x: batch_X, y: batch_y}
_, summary, loss = sess.run([train_op, merged, l], feed_dict=feed_dict)
print("epoch %d, step %d, loss: %f" % (epoch, step, loss))
train_writer.add_summary(summary, step)
step += 1
pos += batch_size
# Make Predictions
valid_y_pred = sess.run(y_pred, feed_dict={x: valid_X})
Step 9: Use TensorBoard to track model convergence.
include screenshots for the following:
1) a TensorBoard graph for the model, and
2) the loss curve.
.
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
...
In this article you will learn hot to use tensorflow Softmax Classifier estimator to classify MNIST dataset in one script.
This paper introduces also the basic idea of a artificial neural network.
YouTube Link: https://youtu.be/SpZSMvI-keU
** AI & Deep Learning with Tensorflow Training: https://www.edureka.co/ai-deep-learning-with-tensorflow **
This Edureka PPT will provide you with a crisp comparison between the two Deep Learning Frameworks - Theano and TensorFlow and will help you choose the right one for yourself.
Follow us to never miss an update in the future.
YouTube: https://www.youtube.com/user/edurekaIN
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
Castbox: https://castbox.fm/networks/505?country=in
A Tale of Three Deep Learning Frameworks: TensorFlow, Keras, & PyTorch with B...Databricks
We all know what they say – the bigger the data, the better. But when the data gets really big, how do you mine it and what deep learning framework to use? This talk will survey, with a developer’s perspective, three of the most popular deep learning frameworks—TensorFlow, Keras, and PyTorch—as well as when to use their distributed implementations.
We’ll compare code samples from each framework and discuss their integration with distributed computing engines such as Apache Spark (which can handle massive amounts of data) as well as help you answer questions such as:
As a developer how do I pick the right deep learning framework?
Do I want to develop my own model or should I employ an existing one?
How do I strike a trade-off between productivity and control through low-level APIs?
What language should I choose?
In this session, we will explore how to build a deep learning application with Tensorflow, Keras, or PyTorch in under 30 minutes. After this session, you will walk away with the confidence to evaluate which framework is best for you.
What is TensorFlow and why do we use itRobert John
My presentation on Machine Learning using the popular TensorFlow library. I compare an implementation of linear regression without the library, and another implementation using the library.
Machine learning with Go. There are some libraries like gonum and gorgonia that allows you to do machine learning. You can also train your deep learning models with python and TensorFlow and use the TensorFlow libraries to read exported models. Go-ONNX is to come.
Understand Social Engineering on a new perspective, beyond the conventional understanding that we have, learn how we use it on social development and securing the weakest link in cybersecurity
A Different Perspective on Business with Social DataTzar Umang
Do business the intelligent way with Social Data and Analytics, harness the power Social Media and Sentiments and use it to improve your brand and or your current campaign,
Business intelligence for SMEs with Data AnalyticsTzar Umang
Know the importance of Data on your business, how it can shape up your Business Operations and day to day decision making from a narrative representation of your Data.
Neuro-symbolic is not enough, we need neuro-*semantic*Frank van Harmelen
Neuro-symbolic (NeSy) AI is on the rise. However, simply machine learning on just any symbolic structure is not sufficient to really harvest the gains of NeSy. These will only be gained when the symbolic structures have an actual semantics. I give an operational definition of semantics as “predictable inference”.
All of this illustrated with link prediction over knowledge graphs, but the argument is general.
PHP Frameworks: I want to break free (IPC Berlin 2024)Ralf Eggert
In this presentation, we examine the challenges and limitations of relying too heavily on PHP frameworks in web development. We discuss the history of PHP and its frameworks to understand how this dependence has evolved. The focus will be on providing concrete tips and strategies to reduce reliance on these frameworks, based on real-world examples and practical considerations. The goal is to equip developers with the skills and knowledge to create more flexible and future-proof web applications. We'll explore the importance of maintaining autonomy in a rapidly changing tech landscape and how to make informed decisions in PHP development.
This talk is aimed at encouraging a more independent approach to using PHP frameworks, moving towards a more flexible and future-proof approach to PHP development.
GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using Deplo...James Anderson
Effective Application Security in Software Delivery lifecycle using Deployment Firewall and DBOM
The modern software delivery process (or the CI/CD process) includes many tools, distributed teams, open-source code, and cloud platforms. Constant focus on speed to release software to market, along with the traditional slow and manual security checks has caused gaps in continuous security as an important piece in the software supply chain. Today organizations feel more susceptible to external and internal cyber threats due to the vast attack surface in their applications supply chain and the lack of end-to-end governance and risk management.
The software team must secure its software delivery process to avoid vulnerability and security breaches. This needs to be achieved with existing tool chains and without extensive rework of the delivery processes. This talk will present strategies and techniques for providing visibility into the true risk of the existing vulnerabilities, preventing the introduction of security issues in the software, resolving vulnerabilities in production environments quickly, and capturing the deployment bill of materials (DBOM).
Speakers:
Bob Boule
Robert Boule is a technology enthusiast with PASSION for technology and making things work along with a knack for helping others understand how things work. He comes with around 20 years of solution engineering experience in application security, software continuous delivery, and SaaS platforms. He is known for his dynamic presentations in CI/CD and application security integrated in software delivery lifecycle.
Gopinath Rebala
Gopinath Rebala is the CTO of OpsMx, where he has overall responsibility for the machine learning and data processing architectures for Secure Software Delivery. Gopi also has a strong connection with our customers, leading design and architecture for strategic implementations. Gopi is a frequent speaker and well-known leader in continuous delivery and integrating security into software delivery.
UiPath Test Automation using UiPath Test Suite series, part 4DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 4. In this session, we will cover Test Manager overview along with SAP heatmap.
The UiPath Test Manager overview with SAP heatmap webinar offers a concise yet comprehensive exploration of the role of a Test Manager within SAP environments, coupled with the utilization of heatmaps for effective testing strategies.
Participants will gain insights into the responsibilities, challenges, and best practices associated with test management in SAP projects. Additionally, the webinar delves into the significance of heatmaps as a visual aid for identifying testing priorities, areas of risk, and resource allocation within SAP landscapes. Through this session, attendees can expect to enhance their understanding of test management principles while learning practical approaches to optimize testing processes in SAP environments using heatmap visualization techniques
What will you get from this session?
1. Insights into SAP testing best practices
2. Heatmap utilization for testing
3. Optimization of testing processes
4. Demo
Topics covered:
Execution from the test manager
Orchestrator execution result
Defect reporting
SAP heatmap example with demo
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
Connector Corner: Automate dynamic content and events by pushing a buttonDianaGray10
Here is something new! In our next Connector Corner webinar, we will demonstrate how you can use a single workflow to:
Create a campaign using Mailchimp with merge tags/fields
Send an interactive Slack channel message (using buttons)
Have the message received by managers and peers along with a test email for review
But there’s more:
In a second workflow supporting the same use case, you’ll see:
Your campaign sent to target colleagues for approval
If the “Approve” button is clicked, a Jira/Zendesk ticket is created for the marketing design team
But—if the “Reject” button is pushed, colleagues will be alerted via Slack message
Join us to learn more about this new, human-in-the-loop capability, brought to you by Integration Service connectors.
And...
Speakers:
Akshay Agnihotri, Product Manager
Charlie Greenberg, Host
"Impact of front-end architecture on development cost", Viktor TurskyiFwdays
I have heard many times that architecture is not important for the front-end. Also, many times I have seen how developers implement features on the front-end just following the standard rules for a framework and think that this is enough to successfully launch the project, and then the project fails. How to prevent this and what approach to choose? I have launched dozens of complex projects and during the talk we will analyze which approaches have worked for me and which have not.
GraphRAG is All You need? LLM & Knowledge GraphGuy Korland
Guy Korland, CEO and Co-founder of FalkorDB, will review two articles on the integration of language models with knowledge graphs.
1. Unifying Large Language Models and Knowledge Graphs: A Roadmap.
https://arxiv.org/abs/2306.08302
2. Microsoft Research's GraphRAG paper and a review paper on various uses of knowledge graphs:
https://www.microsoft.com/en-us/research/blog/graphrag-unlocking-llm-discovery-on-narrative-private-data/
Smart TV Buyer Insights Survey 2024 by 91mobiles.pdf91mobiles
91mobiles recently conducted a Smart TV Buyer Insights Survey in which we asked over 3,000 respondents about the TV they own, aspects they look at on a new TV, and their TV buying preferences.
LF Energy Webinar: Electrical Grid Modelling and Simulation Through PowSyBl -...DanBrown980551
Do you want to learn how to model and simulate an electrical network from scratch in under an hour?
Then welcome to this PowSyBl workshop, hosted by Rte, the French Transmission System Operator (TSO)!
During the webinar, you will discover the PowSyBl ecosystem as well as handle and study an electrical network through an interactive Python notebook.
PowSyBl is an open source project hosted by LF Energy, which offers a comprehensive set of features for electrical grid modelling and simulation. Among other advanced features, PowSyBl provides:
- A fully editable and extendable library for grid component modelling;
- Visualization tools to display your network;
- Grid simulation tools, such as power flows, security analyses (with or without remedial actions) and sensitivity analyses;
The framework is mostly written in Java, with a Python binding so that Python developers can access PowSyBl functionalities as well.
What you will learn during the webinar:
- For beginners: discover PowSyBl's functionalities through a quick general presentation and the notebook, without needing any expert coding skills;
- For advanced developers: master the skills to efficiently apply PowSyBl functionalities to your real-world scenarios.
Software Delivery At the Speed of AI: Inflectra Invests In AI-Powered QualityInflectra
In this insightful webinar, Inflectra explores how artificial intelligence (AI) is transforming software development and testing. Discover how AI-powered tools are revolutionizing every stage of the software development lifecycle (SDLC), from design and prototyping to testing, deployment, and monitoring.
Learn about:
• The Future of Testing: How AI is shifting testing towards verification, analysis, and higher-level skills, while reducing repetitive tasks.
• Test Automation: How AI-powered test case generation, optimization, and self-healing tests are making testing more efficient and effective.
• Visual Testing: Explore the emerging capabilities of AI in visual testing and how it's set to revolutionize UI verification.
• Inflectra's AI Solutions: See demonstrations of Inflectra's cutting-edge AI tools like the ChatGPT plugin and Azure Open AI platform, designed to streamline your testing process.
Whether you're a developer, tester, or QA professional, this webinar will give you valuable insights into how AI is shaping the future of software delivery.
Accelerate your Kubernetes clusters with Varnish CachingThijs Feryn
A presentation about the usage and availability of Varnish on Kubernetes. This talk explores the capabilities of Varnish caching and shows how to use the Varnish Helm chart to deploy it to Kubernetes.
This presentation was delivered at K8SUG Singapore. See https://feryn.eu/presentations/accelerate-your-kubernetes-clusters-with-varnish-caching-k8sug-singapore-28-2024 for more details.
2. In this Talk
• Introduction to Tensor Flow
• Machine Learning Explained
• Neural Networks Explained
• Codify NN Feed Forward
3. What is Tensorflow?
• It is Google’s AI Engine
• “TensorFlow is an interface for expressing machine learning
algorithms, and an implementation for executing such algorithms.”
• Technically a Symbolic ML
• Dataflow Framework that compiles with your GPU or Native Code
4. Machine Learning
• Type of artificial intelligence (AI) that provides computers with the
ability to learn without being explicitly programmed.
Training Data
Dataset Model Learn
Answer
Predict
5. Artificial Neural Network
• It is a computer system modeled on
human brain
• A family of models inspired by
biological neural networks which are
used to estimate or approximate
functions that can depend on a large
number of inputs and are generally
unknown.
9. NN Model: Feed Forward
b
Y
Variables are state of nodes which
output their current value which is
retained across multiple execution.
- Gradient Descent, Regression and
etc.
Y_pred = Y (Wx * b)
Control / Bias – variable for
discrimination, preference that
affects Input – Weight
Relationship state.
10. NN Model: Feed Forward
b
Y
Placeholders are nodes where its
value is fed in at execution time.
Y_pred = Y (Wx * b)
11. NN Model: Feed Forward
b
Y
Mathematical Operation
W(x) = Multiply Two Matrix or a
Weighted Input
Σ (Add) = Summation elementwise
with broadcasting
Y = Step Function with elementwise
rectified linear function
Y_pred = Y (Wx * b)
12. Codify: Graph
b
Y
Y_pred = Y (Wx * b)
# %% imports
%matplotlib inline
import numpy as np
import tensorflow as tf
import matplotlib.pyplot as plt
# %% Let's create some toy data
plt.ion()
n_observations = 100
fig, ax = plt.subplots(1, 1)
xs = np.linspace(-3, 3, n_observations)
ys = np.sin(xs) + np.random.uniform(-0.5, 0.5, n_observations)
ax.scatter(xs, ys)
fig.show()
plt.draw()
# %% tf.placeholders for the input and output of the network. Placeholders are
# variables which we need to fill in when we are ready to compute the graph.
X = tf.placeholder(tf.float32)
Y = tf.placeholder(tf.float32)
# %% We will try to optimize min_(W,b) ||(X*w + b) - y||^2
# The `Variable()` constructor requires an initial value for the variable,
# which can be a `Tensor` of any type and shape. The initial value defines the
# type and shape of the variable. After construction, the type and shape of
# the variable are fixed. The value can be changed using one of the assign
# methods.
W = tf.Variable(tf.random_normal([1]), name='weight')
b = tf.Variable(tf.random_normal([1]), name='bias')
Y_pred = tf.add(tf.mul(X, W), b)
Implementation of Graph, Plot / Planes, Variables
13. Codify – Rendering Graph
b
Y
• We can deploy this graph with a
session: a binding to a particular
execution context (e.g. CPU, GPU)
Y_pred = Y (Wx * b)
14. Codify - Optimization
b
Y
# %% Loss function will measure the distance between our observations
# and predictions and average over them.
cost = tf.reduce_sum(tf.pow(Y_pred - Y, 2)) / (n_observations - 1)
# %% Use gradient descent to optimize W,b
# Performs a single step in the negative gradient
learning_rate = 0.01
optimizer = tf.train.GradientDescentOptimizer(learning_rate).minimize(cost)
Y_pred = Y (Wx * b)Optimizing Predictions
Optimizing Learning Rate
15. Codify - Optimization
b
Y
# %% We create a session to use the graph
n_epochs = 1000
with tf.Session() as sess:
# Here we tell tensorflow that we want to initialize all
# the variables in the graph so we can use them
sess.run(tf.initialize_all_variables())
# Fit all training data
prev_training_cost = 0.0
for epoch_i in range(n_epochs):
for (x, y) in zip(xs, ys):
sess.run(optimizer, feed_dict={X: x, Y: y})
training_cost = sess.run(
cost, feed_dict={X: xs, Y: ys})
print(training_cost)
if epoch_i % 20 == 0:
ax.plot(xs, Y_pred.eval(
feed_dict={X: xs}, session=sess),
'k', alpha=epoch_i / n_epochs)
fig.show()
plt.draw()
# Allow the training to quit if we've reached a minimum
if np.abs(prev_training_cost - training_cost) < 0.000001:
break
prev_training_cost = training_cost
fig.show()
Y_pred = Y (Wx * b)
Implementation of Session to make the model
ready to be fed with data and show results
16. Codify - Result
b
Y
Y_pred = Y (Wx * b)
Gradient Descent is used to optimize W, b which resulted
to this Decision Vector Plot
17. Basic Flow
1.Build a graph
a.Graph contains parameter specifications, model architecture, optimization
process
2.Optimize Predictions, Loss Functions and Learning
3.Initialize a session
4.Fetch and feed data with Session.run
a.Compilation, optimization, visualization
18. Tensorflow Resources
1.Main Site https://www.tensorflow.org/
2.Tutorials
a. https://github.com/nlintz/TensorFlow-Tutorials/
It is highly recommended to use Dockers when running Tensorflow or
doing some test on your own machine…
19. Thank you!!!
Tzar C. Umang
23o9 Technology Solutions of Tzar Enterprises
GDG Baguio
tzarumang@gmail.com