How 20th Century Fox uses ML to predict a movie audience
Google Cloud Blog
http://bit.ly/2N3I7SC
#AI #DeepLearning #MachineLearning #DataScience
✴️ @AI_Python_EN
Google Cloud Blog
http://bit.ly/2N3I7SC
#AI #DeepLearning #MachineLearning #DataScience
✴️ @AI_Python_EN
Google Cloud Blog
How 20th Century Fox uses ML to predict a movie audience | Google Cloud Blog
Success in the movie industry relies on a studio’s ability to attract moviegoers—but that’s sometimes easier said than done. Moviegoers are a diverse group
What's the purpose of statistics?
"Do you think the purpose of existence is to pass out of existence is the purpose of existence?" - Ray Manzarek
The former Doors organist poses some fundamental questions to which definitive answers remain elusive. Happily, the purpose of statistics is easier to fathom since humans are its creator. Put simply, it is to enhance decision making.
These decisions could be those made by scientists, businesspeople, politicians and other government officials, by medical and legal professionals, or even by religious authorities. In informal ways, ordinary folks also use statistics to help make better decisions.
How does it do this?
One way is by providing basic information, such as how many, how much and how often. Stat in statistics is derived from the word state, as in nation state and, as it emerged as a formal discipline, describing nations quantitatively (e.g., population size, number of citizens working in manufacturing) became a fundamental purpose. Frequencies, means, medians and standard deviations are now familiar to anyone.
Often we must rely on samples to make inferences about our population of interest. From a consumer survey, for example, we might estimate mean annual household expenditures on snack foods. This is known as inferential statistics, and confidence intervals will be familiar to anyone who has taken an introductory course in statistics. So will methods such as t-tests and chi-squared tests which can be used to make population inferences about groups (e.g., are males more likely than females to eat pretzels?).
Another way statistics helps us make decisions is by exploring relationships among variables through the use of cross tabulations, correlations and data visualizations. Exploratory data analysis (EDA) can also take on more complex forms and draw upon methods such as principal components analysis, regression and cluster analysis. EDA is often used to develop hypotheses which will be assessed more rigorously in subsequent research.
These hypotheses are often causal in nature, for example, why some people avoid snacks. Randomized experiments are generally considered the best approach in causal analysis but are not always possible or appropriate; see Why experiment? for some more thoughts on this subject. Hypotheses can be further developed and refined, not simply tested through Null Hypothesis Significance Testing, though this has been traditionally frowned upon since we are using the same data for multiple purposes.
Many statisticians are actively involved in designing research, not merely using secondary data. This is a large subject but briefly summarized in Preaching About Primary Research.
Making classifications, predictions and forecasts is another traditional role of statistics. In a data science context, the first two are often called predictive analytics and employ methods such as random forests and standard (OLS) regression. Forecasting sales for the next year is a different matter and normally requires the use of time-series analysis. There is also unsupervised learning, which aims to find previously unknown patterns in unlabeled data. Using K-means clustering to partition consumer survey respondents into segments based on their attitudes is an example of this.
Quality control, operations research, what-if simulations and risk assessment are other areas where statistics play a key role. There are many others, as this page illustrates.
The fuzzy buzzy term analytics is frequently used interchangeably with statistics, an offense to which I also plead guilty.
"The best thing about being a statistician is that you get to play in everyone's backyard." - John Tukey
#ai #artificialintelligence #ml #statistics #bigdata #machinelearning
#datascience
❇️ @AI_Python_EN
"Do you think the purpose of existence is to pass out of existence is the purpose of existence?" - Ray Manzarek
The former Doors organist poses some fundamental questions to which definitive answers remain elusive. Happily, the purpose of statistics is easier to fathom since humans are its creator. Put simply, it is to enhance decision making.
These decisions could be those made by scientists, businesspeople, politicians and other government officials, by medical and legal professionals, or even by religious authorities. In informal ways, ordinary folks also use statistics to help make better decisions.
How does it do this?
One way is by providing basic information, such as how many, how much and how often. Stat in statistics is derived from the word state, as in nation state and, as it emerged as a formal discipline, describing nations quantitatively (e.g., population size, number of citizens working in manufacturing) became a fundamental purpose. Frequencies, means, medians and standard deviations are now familiar to anyone.
Often we must rely on samples to make inferences about our population of interest. From a consumer survey, for example, we might estimate mean annual household expenditures on snack foods. This is known as inferential statistics, and confidence intervals will be familiar to anyone who has taken an introductory course in statistics. So will methods such as t-tests and chi-squared tests which can be used to make population inferences about groups (e.g., are males more likely than females to eat pretzels?).
Another way statistics helps us make decisions is by exploring relationships among variables through the use of cross tabulations, correlations and data visualizations. Exploratory data analysis (EDA) can also take on more complex forms and draw upon methods such as principal components analysis, regression and cluster analysis. EDA is often used to develop hypotheses which will be assessed more rigorously in subsequent research.
These hypotheses are often causal in nature, for example, why some people avoid snacks. Randomized experiments are generally considered the best approach in causal analysis but are not always possible or appropriate; see Why experiment? for some more thoughts on this subject. Hypotheses can be further developed and refined, not simply tested through Null Hypothesis Significance Testing, though this has been traditionally frowned upon since we are using the same data for multiple purposes.
Many statisticians are actively involved in designing research, not merely using secondary data. This is a large subject but briefly summarized in Preaching About Primary Research.
Making classifications, predictions and forecasts is another traditional role of statistics. In a data science context, the first two are often called predictive analytics and employ methods such as random forests and standard (OLS) regression. Forecasting sales for the next year is a different matter and normally requires the use of time-series analysis. There is also unsupervised learning, which aims to find previously unknown patterns in unlabeled data. Using K-means clustering to partition consumer survey respondents into segments based on their attitudes is an example of this.
Quality control, operations research, what-if simulations and risk assessment are other areas where statistics play a key role. There are many others, as this page illustrates.
The fuzzy buzzy term analytics is frequently used interchangeably with statistics, an offense to which I also plead guilty.
"The best thing about being a statistician is that you get to play in everyone's backyard." - John Tukey
#ai #artificialintelligence #ml #statistics #bigdata #machinelearning
#datascience
❇️ @AI_Python_EN
New tutorial! Traffic Sign Classification with #Keras and #TensorFlow 2.0
- 95% accurate
- Includes pre-trained model
- Full tutorial w/ #Python code
http://pyimg.co/5wzc5
#DeepLearning #MachineLearning #ArtificialIntelligence #DataScience #AI #computervision
❇️ @AI_Python_EN
- 95% accurate
- Includes pre-trained model
- Full tutorial w/ #Python code
http://pyimg.co/5wzc5
#DeepLearning #MachineLearning #ArtificialIntelligence #DataScience #AI #computervision
❇️ @AI_Python_EN
Data science is not #MachineLearning .
Data science is not #statistics.
Data science is not analytics.
Data science is not #AI.
#DataScience is a process of:
Obtaining your data
Scrubbing / Cleaning your data
Exploring your data
Modeling your data
iNterpreting your data
Data Science is the science of extracting useful information from data using statistics, skills, experience and domain knowledge.
If you love data, you will like this role....
solving business problems using data is data science. Machine learning/statistics /analytics may come as a way of the solution of a particular business problem. Sometimes we may need all to solve a problem and sometimes even a crosstabs may be handy.
➡️ Get free resources at his site:
www.claoudml.com
❇️ @AI_Python_EN
Data science is not #statistics.
Data science is not analytics.
Data science is not #AI.
#DataScience is a process of:
Obtaining your data
Scrubbing / Cleaning your data
Exploring your data
Modeling your data
iNterpreting your data
Data Science is the science of extracting useful information from data using statistics, skills, experience and domain knowledge.
If you love data, you will like this role....
solving business problems using data is data science. Machine learning/statistics /analytics may come as a way of the solution of a particular business problem. Sometimes we may need all to solve a problem and sometimes even a crosstabs may be handy.
➡️ Get free resources at his site:
www.claoudml.com
❇️ @AI_Python_EN
François Chollet (Google, Creator of Keras) just released a paper on defining and measuring intelligence and a GitHub repo that includes a new #AI evaluation dataset, ARC – "Abstraction and Reasoning Corpus".
Paper: https://arxiv.org/abs/1911.01547
ARC: https://github.com/fchollet/ARC
#AI #machinelearning #deeplearning
❇️ @AI_Python_EN
Paper: https://arxiv.org/abs/1911.01547
ARC: https://github.com/fchollet/ARC
#AI #machinelearning #deeplearning
❇️ @AI_Python_EN
Optimizing Millions of Hyperparameters by Implicit Differentiation Lorraine et al.:
https://arxiv.org/abs/1911.02590
#ArtificialIntelligence #MachineLearning
❇️ #AI_Python_EN
https://arxiv.org/abs/1911.02590
#ArtificialIntelligence #MachineLearning
❇️ #AI_Python_EN
ai.pdf
2.1 MB
Who is winning the #AI race : China 🇨🇳, Europe🇪🇺 or US 🇺🇸?
Interesting 106 page report from Aug 2019 by "Center for data innovation"
I personally believe that innovation will come from a true borderless exchange of technology & talent in democratic and responsible societies.
#artificialintelligence #machinelearning
❇️ @AI_Python_EN
Interesting 106 page report from Aug 2019 by "Center for data innovation"
I personally believe that innovation will come from a true borderless exchange of technology & talent in democratic and responsible societies.
#artificialintelligence #machinelearning
❇️ @AI_Python_EN
A good introduction to #MachineLearning and its 4 approaches:
https://towardsdatascience.com/machine-learning-an-introduction-23b84d51e6d0?gi=10a5fcd4decd
#BigData #DataScience #AI #Algorithms #ReinforcementLearning
❇️ @AI_Python_EN
https://towardsdatascience.com/machine-learning-an-introduction-23b84d51e6d0?gi=10a5fcd4decd
#BigData #DataScience #AI #Algorithms #ReinforcementLearning
❇️ @AI_Python_EN
DEBATE : Yoshua Bengio | Gary Marcus Pre-readings recommended to the audience before the Debate :
Yoshua Bengio | Gary Marcus
This Is The Debate The #AI World Has Been Waiting For
❇️ @AI_Python_EN
Yoshua Bengio | Gary Marcus
This Is The Debate The #AI World Has Been Waiting For
❇️ @AI_Python_EN
Very interesting use of #AI to tackle bias in the written text by substituting words automatically to more neutral wording. However, one must also consider the challenges and ramifications such technology could mean to the written language as it can not only accidentally change the meaning of what was written, it can also change the tone and expression of the author and neutralize the point-of-view and remove emotion from language.
#NLP
https://arxiv.org/pdf/1911.09709.pdf
❇️ @AI_Python_EN
#NLP
https://arxiv.org/pdf/1911.09709.pdf
❇️ @AI_Python_EN
Deep Speech, a good #Persian podcsts about #AI
We will talk about #ArtificialIntelligence, #MachineLearning and #DeepLearning news.
https://castbox.fm/channel/Deep-Speech-id2420707?country=us
❇️ @AI_Python_EN
We will talk about #ArtificialIntelligence, #MachineLearning and #DeepLearning news.
https://castbox.fm/channel/Deep-Speech-id2420707?country=us
❇️ @AI_Python_EN
Castbox
Deep Speech | Listen Free on Castbox.
We will talk about artificial intelligence, machine learning and deep learning news.Millions of podcasts for all topics. Listen to the best free podcast...
Best of arXiv.org for #AI, #MachineLearning, and #DeepLearning – November 2019
https://bit.ly/36OWsaD
❇️ @AI_Python_EN
https://bit.ly/36OWsaD
❇️ @AI_Python_EN
Decision trees are extremely fast when it comes to classify unknown records. Watch this video to know how Decision Tree algorithm works, in an easy way - http://bit.ly/2Ggsb9l
#DataScience #MachineLearning #AI #ML #ReinforcementLearning #Analytics #CloudComputing #Python #DeepLearning #BigData #Hadoop
#DataScience #MachineLearning #AI #ML #ReinforcementLearning #Analytics #CloudComputing #Python #DeepLearning #BigData #Hadoop
Breast cancer classification with Keras and Deep Learning
To analyze the cellular structures in the breast histology images we were instead leveraging basic computer vision and image processing algorithms, but combining them in a novel way.
Researcher: Adrian Rosebrock
Paper & codes : http://ow.ly/yngq30qjLye
#artificialintelligence #ai #machinelearning #deeplearning #bigdata #datascience
❇️ @AI_Python_EN
To analyze the cellular structures in the breast histology images we were instead leveraging basic computer vision and image processing algorithms, but combining them in a novel way.
Researcher: Adrian Rosebrock
Paper & codes : http://ow.ly/yngq30qjLye
#artificialintelligence #ai #machinelearning #deeplearning #bigdata #datascience
❇️ @AI_Python_EN
ANNOUNCING PYCARET 1.0.0 - An amazingly simple, fast and efficient way to do machine learning in Python. NEW OPEN SOURCE ML LIBRARY If you are a DATA SCIENTIST or want to become one, then this is for YOU....
PyCaret is a NEW open source machine learning library to train and deploy ML models in low-code environment.
It allows you to go from preparing data to deploying a model within SECONDS.
PyCaret is designed to reduce time and efforts spent in coding ML experiments. It automates the following:
- Preprocessing (Data Preparation, Feature Engineering and Feature Selection)
- Model Selection (over 60 ready-to-use algorithms)
- Model Evaluation (50+ analysis plots)
- Model Deployment
- ML Integration and Monitoring (Power BI, Tableau, Alteryx, KNIME and more)
- ..... and much more!
Watch this 1 minute video to see how PyCaret can help you in your next machine learning project.
The easiest way to install pycaret is using pip. Just type "pip install pycaret" into your notebook.
To learn more about PyCaret, please visit the official website https://www.pycaret.org
#datascience #datascientist #machinelearning #ml #ai #artificialintelligence #analytics #pycaret
❇️ @AI_Python_EN
PyCaret is a NEW open source machine learning library to train and deploy ML models in low-code environment.
It allows you to go from preparing data to deploying a model within SECONDS.
PyCaret is designed to reduce time and efforts spent in coding ML experiments. It automates the following:
- Preprocessing (Data Preparation, Feature Engineering and Feature Selection)
- Model Selection (over 60 ready-to-use algorithms)
- Model Evaluation (50+ analysis plots)
- Model Deployment
- ML Integration and Monitoring (Power BI, Tableau, Alteryx, KNIME and more)
- ..... and much more!
Watch this 1 minute video to see how PyCaret can help you in your next machine learning project.
The easiest way to install pycaret is using pip. Just type "pip install pycaret" into your notebook.
To learn more about PyCaret, please visit the official website https://www.pycaret.org
#datascience #datascientist #machinelearning #ml #ai #artificialintelligence #analytics #pycaret
❇️ @AI_Python_EN
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Acme: A new framework for distributed reinforcement learning by DeepMind
Intro:
https://deepmind.com/research/publications/Acme
Paper:
https://github.com/deepmind/acme/blob/master/paper.pdf
Repo:
https://github.com/deepmind/acme
#reinforcementlearning #ai #deepmind #deeplearning #machinelearning
🗣 @AI_Python_arXiv
✴️ @AI_Python_EN
❇️ @AI_Python
Intro:
https://deepmind.com/research/publications/Acme
Paper:
https://github.com/deepmind/acme/blob/master/paper.pdf
Repo:
https://github.com/deepmind/acme
#reinforcementlearning #ai #deepmind #deeplearning #machinelearning
🗣 @AI_Python_arXiv
✴️ @AI_Python_EN
❇️ @AI_Python
Lecture Notes in Deep Learning: Feedforward Networks — Part 3 | #DataScience #MachineLearning #ArtificialIntelligence #AI
https://bit.ly/2Z2GgQY
https://bit.ly/2Z2GgQY
Medium
Feedforward Networks — Part 3
The Backpropagation Algorithm
In future #AI hiring other AI be like: Job Profile: *human baby sitter*
- Experience : trained on 100 years of past data.
- Test Accuracy : 99.9999
- Precision: blah
- recall : blah
- AUC : blah blah
- Inference time: A.C
- Trained on : Latest "alien" TPUs and GPUs
- Bias : blah Note: AI trained on old TPUs will not be considered. And then AI will gossip with each other about bias and discrimination they have to go through compared to others like:
- "Wouldn't I be considered if I am trained on X country's data?"
- "Why was she considered even though she has outliers in the data?"
- "I am trained on old TPUs, I won't be considered? What!" LOL #artificialintelligence #machinelearning
- Experience : trained on 100 years of past data.
- Test Accuracy : 99.9999
- Precision: blah
- recall : blah
- AUC : blah blah
- Inference time: A.C
- Trained on : Latest "alien" TPUs and GPUs
- Bias : blah Note: AI trained on old TPUs will not be considered. And then AI will gossip with each other about bias and discrimination they have to go through compared to others like:
- "Wouldn't I be considered if I am trained on X country's data?"
- "Why was she considered even though she has outliers in the data?"
- "I am trained on old TPUs, I won't be considered? What!" LOL #artificialintelligence #machinelearning
Forwarded from ساعیان ارتباط
استخدام کارشناس هوش مصنوعی در حوزه پردازش تصویر (در شرکت دانش بنیان ساعیان ارتباط آینده پیشرو)
* حداقل یک سال سابقه برنامهنویسی در حوزه هوش مصنوعی و بینایی ماشین
* تجربه عملیاتی کار با مدلهای موجود در حوزه تصویر و ویدیو و دانش آموزش این مدلها بر دیتاست اختصاصی
* تسلط به پایتون و کتابخانههای پردازش تصویر و یادگیری عمیق
* تسلط به لینوکس و Git
* سابقه فعالیت صنعتی در زمینه پردازش تصویر و یادگیری عمیق امتیاز ویژه محسوب میشود.
ارسال رزومه :
https://senatelecom.com/careers
#استخدام #هوش_مصنوعی #پردازش_تصویر #ai #hiring
* حداقل یک سال سابقه برنامهنویسی در حوزه هوش مصنوعی و بینایی ماشین
* تجربه عملیاتی کار با مدلهای موجود در حوزه تصویر و ویدیو و دانش آموزش این مدلها بر دیتاست اختصاصی
* تسلط به پایتون و کتابخانههای پردازش تصویر و یادگیری عمیق
* تسلط به لینوکس و Git
* سابقه فعالیت صنعتی در زمینه پردازش تصویر و یادگیری عمیق امتیاز ویژه محسوب میشود.
ارسال رزومه :
https://senatelecom.com/careers
#استخدام #هوش_مصنوعی #پردازش_تصویر #ai #hiring