What does a #DataScientist need to consider on a machine learning project?
Via: @cedeeplearning
Credit goes to: http://blog.bidmotion.com
also check our other social media handles:
https://linktr.ee/cedeeplearning
Via: @cedeeplearning
Credit goes to: http://blog.bidmotion.com
also check our other social media handles:
https://linktr.ee/cedeeplearning
Cutting Edge Deep Learning pinned Β«β
10 must-read books for ml and data science 1οΈβ£ Python Data Science Handbook By Jake VanderPlas The book introduces the core libraries essential for working with data in Python: particularly IPython, NumPy, Pandas, Matplotlib, Scikit-Learn, and related packages.β¦Β»
πΉCoding Deep Learning For Beginners
https://towardsdatascience.com/coding-deep-learning-for-beginners-types-of-machine-learning-b9e651e1ed9d
via: @cedeeplearning
https://towardsdatascience.com/coding-deep-learning-for-beginners-types-of-machine-learning-b9e651e1ed9d
via: @cedeeplearning
#Supervised ML VS #Unsupervised ML
In Supervised learning, you #train the machine using data which is well #"labeled." Unsupervised learning is a machine learning technique, where you do not need to supervise the model. Supervised learning allows you to collect data or produce a data output from the previous experience.
via: @cedeeplearning
In Supervised learning, you #train the machine using data which is well #"labeled." Unsupervised learning is a machine learning technique, where you do not need to supervise the model. Supervised learning allows you to collect data or produce a data output from the previous experience.
via: @cedeeplearning
πΉR vs Python: Which One is Better for Data Science?
link: https://statanalytica.com/blog/r-vs-python/
#R
#Python
#Data_science
via: @cedeeplearning
link: https://statanalytica.com/blog/r-vs-python/
#R
#Python
#Data_science
via: @cedeeplearning
πΉMachine Learning tips and tricks #cheatsheet
Bias: The #bias of a model is the difference between the expected #prediction and the correct model that we try to predict for given data points.
Variance: The #variance of a model is the variability of the model prediction for given data points.
Bias/variance #tradeoff: The simpler the model, the higher the bias, and the more complex the model, the higher the variance.
from: stanford.edu
via: @cedeeplearning
Bias: The #bias of a model is the difference between the expected #prediction and the correct model that we try to predict for given data points.
Variance: The #variance of a model is the variability of the model prediction for given data points.
Bias/variance #tradeoff: The simpler the model, the higher the bias, and the more complex the model, the higher the variance.
from: stanford.edu
via: @cedeeplearning
πΉDeep Learning #Cheatsheet
Activation function: #Activation functions are used at the end of a hidden unit to introduce #non-linear #complexities to the model. Here are the most common ones
from: stanford.edu
via: @cedeeplearning
Activation function: #Activation functions are used at the end of a hidden unit to introduce #non-linear #complexities to the model. Here are the most common ones
from: stanford.edu
via: @cedeeplearning
πΉWhat is Data Mining?
#Data_mining is a process of extracting the hidden #predictive information from the extensive database. Data mining is used by the organization to turn #raw_data into useful information.
link: https://statanalytica.com/data-mining-assignment-help
via: @cedeeplearning
#Data_mining is a process of extracting the hidden #predictive information from the extensive database. Data mining is used by the organization to turn #raw_data into useful information.
link: https://statanalytica.com/data-mining-assignment-help
via: @cedeeplearning
πΉTop 10 Guidelines for a Successful Business Intelligence Strategy in 2020
π Via: @cedeeplearning
link: https://www.predictiveanalyticstoday.com/top-guidelines-for-a-successful-business-intelligence/
#business_intelligence
#Strategy
#implementation
#insight
π Via: @cedeeplearning
link: https://www.predictiveanalyticstoday.com/top-guidelines-for-a-successful-business-intelligence/
#business_intelligence
#Strategy
#implementation
#insight
πΉUnderstanding the Data Science Lifecycle
π Via: @cedeeplearning
link: http://sudeep.co/data-science/Understanding-the-Data-Science-Lifecycle/
#data_science
#life_cycle
#machinelearning
π Via: @cedeeplearning
link: http://sudeep.co/data-science/Understanding-the-Data-Science-Lifecycle/
#data_science
#life_cycle
#machinelearning
π»How Does a Data Management Platform Work?
More than half of marketing organizations have deployed a marketing data management platform, yet confusion remains about what these solutions do β and what they donβt.
π Via: @cedeeplearning
link: https://www.gartner.com/en/marketing/insights/articles/how-does-a-data-management-platform-work
#data_management
#platform
#DMP
More than half of marketing organizations have deployed a marketing data management platform, yet confusion remains about what these solutions do β and what they donβt.
π Via: @cedeeplearning
link: https://www.gartner.com/en/marketing/insights/articles/how-does-a-data-management-platform-work
#data_management
#platform
#DMP
πΉ85 Incredible
Data Visualization Examples
Although all kinds of these plots can be made using python or BI Tools like Power BI as well.
π Via: @cedeeplearning
link: https://piktochart.com/data-visualization-examples/
#visualisation
#matplotlib
#python
#powerbi
Data Visualization Examples
Although all kinds of these plots can be made using python or BI Tools like Power BI as well.
π Via: @cedeeplearning
link: https://piktochart.com/data-visualization-examples/
#visualisation
#matplotlib
#python
#powerbi
πΉStatistics Vs. Machine Learning
As an organizationβs information infrastructure matures, the most appropriate next step is to begin adding advanced analytics. We use the specific term advanced analytics with purpose in this context for two few reasons:
π»It assumes migration from historical analytics into current and future based analytics
π»It encompasses statistical analysis as well as machine learning
π Via: @cedeeplearning
link: https://www.rocketsource.co/blog/machine-learning-models/
#statistics
#machinelearning
#modeling
As an organizationβs information infrastructure matures, the most appropriate next step is to begin adding advanced analytics. We use the specific term advanced analytics with purpose in this context for two few reasons:
π»It assumes migration from historical analytics into current and future based analytics
π»It encompasses statistical analysis as well as machine learning
π Via: @cedeeplearning
link: https://www.rocketsource.co/blog/machine-learning-models/
#statistics
#machinelearning
#modeling
πΉSuccessfully Deploying Machine Learning Models
There are various opinions and assertions out there regarding the end-to-end process of building and deploying predictive models. We strongly assert that the deployment process is not a process at all β itβs a lifecycle. Why? Itβs an infinite process of iterations and improvements. Model deployment is in no way synonymous with model completion.
π Via: @cedeeplearning
link: https://www.rocketsource.co/blog/machine-learning-models/
#end_to_end
#deployment
#machine_learning
There are various opinions and assertions out there regarding the end-to-end process of building and deploying predictive models. We strongly assert that the deployment process is not a process at all β itβs a lifecycle. Why? Itβs an infinite process of iterations and improvements. Model deployment is in no way synonymous with model completion.
π Via: @cedeeplearning
link: https://www.rocketsource.co/blog/machine-learning-models/
#end_to_end
#deployment
#machine_learning
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π» Massively Scaling Reinforcement Learning with SEED RL
Reinforcement learning (RL) has seen impressive advances over the last few years as demonstrated by the recent success in solving games such as Go and Dota 2. Models, or agents, learn by exploring an environment, such as a game, while optimizing for specified goals. However, current RL techniques require increasingly large amounts of training to successfully learn even simple games, which makes iterating research and product ideas computationally expensive and time consuming.
π Via: @cedeeplearning
link: https://ai.googleblog.com/
#reinforcement
#RL
#deep_learning
#architecture
#training
Reinforcement learning (RL) has seen impressive advances over the last few years as demonstrated by the recent success in solving games such as Go and Dota 2. Models, or agents, learn by exploring an environment, such as a game, while optimizing for specified goals. However, current RL techniques require increasingly large amounts of training to successfully learn even simple games, which makes iterating research and product ideas computationally expensive and time consuming.
π Via: @cedeeplearning
link: https://ai.googleblog.com/
#reinforcement
#RL
#deep_learning
#architecture
#training
π» Open Images V6 β Now Featuring Localized Narratives
Open Images is the largest annotated image dataset in many regards, for use in training the latest deep #convolutional #neural_networks for #computer_vision tasks. With the introduction of version 5 last May, the Open Images dataset includes 9M images annotated with 36M image-level labels, 15.8M bounding boxes, 2.8M instance #segmentations, and 391k visual relationships. Along with the dataset itself, the associated Open Images Challenges have spurred the latest advances in #object_detection, instance segmentation, and visual relationship detection.
π Via: @cedeeplearning
link: https://ai.googleblog.com/search?updated-max=2020-03-11T09:00:00-07:00&max-results=10
#image_detection
#machinelearning
#deeplearning
Open Images is the largest annotated image dataset in many regards, for use in training the latest deep #convolutional #neural_networks for #computer_vision tasks. With the introduction of version 5 last May, the Open Images dataset includes 9M images annotated with 36M image-level labels, 15.8M bounding boxes, 2.8M instance #segmentations, and 391k visual relationships. Along with the dataset itself, the associated Open Images Challenges have spurred the latest advances in #object_detection, instance segmentation, and visual relationship detection.
π Via: @cedeeplearning
link: https://ai.googleblog.com/search?updated-max=2020-03-11T09:00:00-07:00&max-results=10
#image_detection
#machinelearning
#deeplearning
πΉHow Conversational AI creates new business cases
The era of conversational artificial intelligence is rapidly changing the business of both traditional websites and mobile applications. What are, then, the benefits of βconversational AIβ that new business systems can offer? Well, to begin with: it seems that voice and dialogue interfaces are finally ripe to compete against traditional ones.
π Via: @cedeeplearning
link: https://chatbotsmagazine.com/how-conversational-ai-create-new-business-cases-aed0740903c0
#AI
#business_case
#chatbot
#machine_learning
The era of conversational artificial intelligence is rapidly changing the business of both traditional websites and mobile applications. What are, then, the benefits of βconversational AIβ that new business systems can offer? Well, to begin with: it seems that voice and dialogue interfaces are finally ripe to compete against traditional ones.
π Via: @cedeeplearning
link: https://chatbotsmagazine.com/how-conversational-ai-create-new-business-cases-aed0740903c0
#AI
#business_case
#chatbot
#machine_learning
π»Where chatbots are headed in 2020
Chatbots are on the verge of living up to their hype, with new research commissioned by Intercom indicating where they can have the most impact.
π Via: @cedeeplearning
link: https://chatbotsmagazine.com/where-chatbots-are-headed-in-2020-4e4cbf281fc9
#chatbot
#demand
#business_case
#machinelearning
Chatbots are on the verge of living up to their hype, with new research commissioned by Intercom indicating where they can have the most impact.
π Via: @cedeeplearning
link: https://chatbotsmagazine.com/where-chatbots-are-headed-in-2020-4e4cbf281fc9
#chatbot
#demand
#business_case
#machinelearning