TensorFlow 2.0 is the best bet for Deep Learning Community.

Eager execution for easy prototyping & debugging along with tf.function() advantage,

Distribution Strategies for distributed Training (including multi node, multi accelerator including TPU pods, also Kubernetes),

Smoother building, training,validation with tf.keras and premade Estimators,

Smart deployment (TensorFlow Serving(A TensorFlow library allowing models to be served over HTTP/REST), TensorFlow Lite(TensorFlow’s lightweight solution for mobile and embedded devices), TensorFlow.js(Enables deploying models in JavaScript environments, such as in a web browser or server side through Node.js), TensorFlow Hub),
Compatiable with TF 1.x (also a conversion tool which updates TensorFlow 1.x Python code to use TensorFlow 2.0 compatible APIs, or flags cases where code cannot be converted automatically )

Also great for researchers ( Model Subclassing API, automatic differentiation, Ragged Tensors, TensorFlow Probability, Tensor2Tensor)

For beginners, TensorFlow, https://lnkd.in/fp3AWKk

#tensorflow #research #deeplearning #pyTorch

✴️ @AI_Python_EN

Code free Deep Learning :D

https://uber.github.io/ludwig/

✴️ @AI_Python_EN

Machine Learning with no code? Its all possible thanks to tools like Uber's Ludwig, Azure, and a few others that i'll demonstrate in this video https://lnkd.in/gagQEfD
Machine Learning with No Code

✴️ @AI_Python_EN

Survey data are sometimes criticized for not being "real data" - just what people say. There are numerous ways to respond to this, for example:

Social media data and customer correspondence, therefore, are not "real data" either.

How real are the digital segments we use?

Even when customer records have been thoroughly cleaned, how clean are they, really? Even when "clean enough" they only show us part of the picture, unless we've been hacking our competitors.

Survey professionals, including scholars and government researchers, have long known that survey data are not exact measurements and usually should be interpreted directionally.

Most importantly, many consumer surveys are concerned with attitudes and opinions, which cannot be measured precisely. If someone says they don't like our snack food brand because it's too salty, do we disregard this?

We should also remember that two people can do the same things for the same reasons, the same things for different reasons, different things for the same reasons, different things for different reasons, and that what we do and why we do it is usually not constant by product category or over time.

Survey research can't do everything, but who ever said it could?

✴️ @AI_Python_EN

Have you ever used a Jupyter notebook? If yes, you know it is a pleasure to use it for interactive programming. If no, you should try it! Or you may be a C++ programmer and thinking Jupyter notebooks are not for you, but wait, imagine our joy when we came across the Xeus-Cling kernel! But what does it do?
https://lnkd.in/gKAazmn

✴️ @AI_Python_EN

It seems like controversial papers or talks in the press about AI being unfair, incomplete or biased is fashionable these days.

We came across this paper which suggests that #selfdrivingcars are more likely to hit a black or dark skinned person.

Here is the paper “Predictive inequality in Object Detection”: https://lnkd.in/ebHbP6f

What do you think?

Are algorithms biased / trained on insufficient data? What can be done to solve this problem?
Photo credit: https://lnkd.in/eEqS39J
#algorithms #deeplearning #ai

✴️ @AI_Python_EN

Launching TensorFlow Lite for Microcontrollers

By Pete Warden: https://lnkd.in/ejcJMVn

#artificialintelligence #deeplearning #microcontrollers #tensorflow

✴️ @AI_Python_EN

OpenAI has created activation atlases (in collaboration with Google researchers), a new technique for visualizing what interactions between neurons can represent.

As AI systems are deployed in increasingly sensitive contexts, having a better understanding of their internal decision-making processes will let us identify weaknesses and investigate failures.

Blog: https://lnkd.in/d4i6xQC
Paper: https://lnkd.in/dGNcd4K
Github: https://lnkd.in/d-2WhfN
Demo: https://lnkd.in/dBiHZv3

#deeplearning #research

✴️ @AI_Python_EN

Alibaba Groups new work on BERT for intent classification and slot filling

https://arxiv.org/abs/1902.10909v1

✴️ @AI_Python_EN

How can AI become biased? 2 papers investigate:

Joy Buolamwini show that AI has a higher error rate when recognizing darker-skinned female faces: http://bit.ly/2C2pxT9

IBM responds to their paper, explaining how they reduced that error: http://bit.ly/2C82u9n #TechRec

✴️ @AI_Python_EN

Data science is not about memorization, it's about making connections and applying your knowledge.

If you can understand a lot of different materials and subjects and see how they connect, then you'll naturally remember these subjects better. This is simply how the brain is structured - everything is connected to something else.

➡️ More connections = more retention.

Connect new information with long-term memories and then reinforce those connections to create new long-term memories.

And begin to apply your knowledge to create something new and you will not only increase your understanding of the subject, but you'll increase your retention as well because the information will have context.

Ironically, this approach is much more effective than *trying* to memorize material.

👉 So if you're ever wondering "should I memorize this," just reframe your perspective to ask:

• how does this information relate to what I already know?
• how can I apply this to a problem I'm facing?
• what can I create with this new knowledge?
• who can I teach this new subject to?

and the ability to remember what you've learned will take care of itself.

#datascience #learning

✴️ @AI_Python_EN

What is the fastest LSTM implementation?!
(cuDNNLSTM)
"an informed choice of deep learning framework and LSTM implementation may increase training speed by up to 7.2x on standard input sizes from ASR"
https://lnkd.in/ea66qyn
https://lnkd.in/eTFXN6Z

✴️ @AI_Python_EN

I often read or hear that 97% (sometimes 98%) of scientists agree with the consensus view on climate change. What is meant by scientists or consensus view is often not made clear and typically no sources are cited.

I have followed this and other environmental issues closely for many years, and this assertion appears to rest mostly on three studies: Oreskes (2004); Cook et al. (2013); and Doran and Zimmerman (2009). You can look up these studies yourself and draw your own conclusions as to how well they support the 97%/98% assertion.

A better source, IMO, are the five surveys Dennis Bray, a sociologist, and Hans Von Storch, a prominent climate scientist, have conducted since the 1990s. Here is the link to the most recent (2016):

https://lnkd.in/f4xG394

This is a very detailed study and I would recommend you read the full report instead of focusing on the questions of most interest to you. As always, please be on the lookout for confirmation bias. :-)

✴️ @AI_Python_EN

Causal Discovery Toolbox: An Open-Source Framework for Causal Inference in Graphs #DataScience #MachineLearning #ArtificialIntelligence http://bit.ly/2Hkqs4B

✴️ @AI_Python_EN

11 Facts about data science in office work


Old fact, but it is still relevant?

Please share us if you would like our channel
#datascience #business

✴️ @AI_Python_EN

Best universities to study machine learning:

University of California, Berkeley
Stanford University
University of Oxford
Université de Montréal
University of Toronto
Carnegie Mellon University
Xi'an Jiaotong University
New York University
Massachusetts Institute of Technology
University of Amsterdam
University of Michigan
Cornell University
Max Planck Society
Tsinghua University
The Chinese University of Hong Kong
University of North Carolina at Chapel Hill
University of Washington

Best places to work in machine learning:

Gartner
Google
Microsoft
Facebook
IBM
AT&T
Alcatel-Lucent
Adobe Systems

#machinelearning

✴️ @AI_Python_EN

We are extremely thrilled to open our doors for Japan with our AI services — for enterprises and community such as Universities & individual professionals.

Welcoming our Country Head Takashi Nishida San to drive growth into this beautiful nation with #BigData a #IoT #Robotics

Our proven tools & methodologies in underlying technologies such as #machinelearning & #deeplearning will be used to :

1)assess AI maturity
2) prescribe & develop the right AI skills program
3) execute data-driven ROI AI projects

to power up the economic growth of our clients.

https://lnkd.in/e45xhVe

✴️ @AI_Python_EN

Google Releases GPipe, an Open Source Library for Efficiently Training Large-scale Neural Network Models
http://bit.ly/2HhH4tZ
#MachineLearning #ArtificialIntelligence #DataScience

✴️ @AI_Python_EN

Concurrent Meta Reinforcement Learning

Parisotto et al.: https://lnkd.in/e6nyRhc

#artificialintelligence #deeplearing #reinforcementlearning

✴️ @AI_Python_EN

Google open sources #Gpipe under #Lingvo library for Sequence Modeling.

What is Lingvo?

Lingvo is the international language Esperanto word for “language”. This naming alludes to the roots of the Lingvo framework — it was developed as a general deep learning framework using TensorFlow with a focus on sequence models for language-related tasks such as machine translation, speech recognition, and speech synthesis.

What is Gpipe?

GPipe is a distributed machine learning library that uses synchronous stochastic gradient descent and pipeline parallelism for training, applicable to any DNN that consists of multiple sequential layers. Importantly, GPipe allows researchers to easily deploy more accelerators to train larger models and to scale the performance without tuning hyperparameters.
Link: https://lnkd.in/ePsTCxw

GitHub: https://lnkd.in/eRwgEZz
Article: https://lnkd.in/e2y4fV2

#deeplearning #machinelearning #tensorflow

✴️ @AI_Python_EN

It is time we shared the dataset with everyone. This is a collection of text from Tamil news articles. Has around 7 millions lines of text, all cleaned up, ready to used for language modelling task, in case anyone want to try. You can use the code from git repo below to get started.

Dataset:
https://lnkd.in/fzg3xyM]
Code:
https://lnkd.in/fezt4M8 #datasets

✴️ @AI_Python_EN