AI, Python, Cognitive Neuroscience

Confuse When Reading Symbols on Machine Learning Book?

Sometimes, we not always remember probability and statistics symbols when brush up machine learning knowledge. This is Probability and Statistics Symbols that can make you remember the symbols, made by coolmathposters and I found it on deepkapha.ai.

How to implement statistics into business?After you good at machine learning you can implement on some cases?

✴️ @AI_Python_EN

Differentiable Beam search Decoder (DBD): training a speech recognition system by backpropagating through the decoder leads to smaller models and better word error rates...

https://ai.facebook.com/blog/combining-acoustic-and-language-model-training-for-speech-recognition/

Why Should you Learn AI and Machine Learning

Why Machine Learning Fascinates Me?

Supervised Machine Learning

Do you know what is Machine Learning All About?

The Science of Machine Learning is about Learning the Models that Generalize Well Machine learning is an area of artificial intelligence and computer science This includes the development of software and algorithms that can make predictions based on data.

Data Science Enthusiasts, I have Created a Community for Us to Learn Together🗝

Interested people let me know in the Comments and I will send you the invite link to our Community🎟🗣

#reinforcementlearning #machinlearning #Datascience #ArtificialIntelligence #gans
#SupervisedMachineLearning #ML #dl #iot #bigdata

✴️ @AI_Python_EN

DeepBundle: Fiber Bundle Parcellation with Graph Convolution Neural Networks

Paper: http://ow.ly/Nk6Y50uAmII
#artificialinteligence #ai #ml #machinelearning #bigdata #deeplearning #technology

✴️ @AI_Python_EN

Get ready to do some natural language processing in Course 3 of the deeplearning.ai TensorFlow Specialization, available June 20! While you’re waiting, check out the first two courses: http://bit.ly/2Zkij5Z
✴️ @AI_Python_EN

Dear all,

We are seeking a full-time Research Associate/Research Assistant in AI and Robotics to join the Human-AI Teaming (HAT) lab at King's College London, in the Department of Informatics. The post is for up to 36 months, starting from September 1st 2019 or soon thereafter.
The salary is up to £44,015 per annum depending on experience.

More info about the post here:
https://my.corehr.com/pls/kingrecruit/erq_jobspec_version_4.display_form?p_company=1&p_internal_external=E&p_display_in_irish=N&p_process_type=&p_applicant_no=&p_form_profile_detail=&p_display_apply_ind=Y&p_refresh_search=Y&p_recruitment_id=014946

The vision of the HAT lab is that humans should work together with the AI as a team. To achieve this, we focus on new research and challenges around Safe, Trusted and Explainable AI, that will allow humans to trust what the AI systems are about to do and to interact with the AI systems to co-create solutions.

We focus on AI Planning and Machine/Reinforcement Learning, and the main application area is the control of robotics and autonomous systems, where AI is used to control (teams of) moving robots as well as to provide robots with more autonomy (for example in manufacturing, space robotics, or satellites).

HAT lab webpage: https://www.human-ai-teaming.com/

We are looking for a candidate who can drive our research agenda. You will be responsible for adapting existing as well as developing new techniques, and will be driving its applications in scenarios involving physical robots in our lab. You will also be involved in co-directing the research of PhD students and to engage with collaborators and industrial partners.

King's College London is one of the best places where this type of research can be done, also thanks to the recently funded Centre for Doctoral Training in Safe and Trusted AI (https://www.kcl.ac.uk/nms/depts/informatics/stai), that aims to make King's one of the leading institutions for the research in this space.

The candidate is expected to have a PhD (or be near completion) in AI or Robotics, together with a strong track record. Expertise on Explainable AI, AI Planning, ROS, Machine Learning, Reinforcement Learning would be a plus.

Please email me if you have any question.

Many thanks and kind regards,
Dan

Call for Speakers - Boston's Field Guide to Data Science & Emerging Tech - Correct Link

Would love for you to consider presenting at our Boston conference (Google auto corrected earlier link.- sorry about that).

https://forms.gle/APNF48rXfATJLJjq9

Aug 22 at BU.

Designed as an organic regional community event it is authentic, accessible, & engaging.

Expect 700 participants and about 40 sessions.

Thanks.

btw: Also doing a Food, Ag, Sustainability, & Supply Chain in Tech Conference Dec 9th in St. Paul, MN. Home of Cargil, General Mills, Land O Lakes, Ecolab,...

✴️ @AI_Python_EN

Data science is an ever-evolving field. As data scientists, we need to have our finger on the pulse of the latest algorithms and frameworks coming up in the community.

So, if you’re a:
Data science enthusiast
Machine learning practitioner
Data science manager
Deep learning expert

or any mix of the above, this article is for you.

Shubham Singh loved putting together this month’s edition given the sheer scope of topics we have covered.

Link : https://bit.ly/2IDa5iP

What did you think of this month’s collection? Any data science libraries or discussions I missed out on? Hit me up in the comments section below and let’s discuss!

#machinelearning #datascience #deeplearning #deeplearning

✴️ @AI_Python_EN

Weight Agnostic Neural Networks 🦎

Inspired by precocial species in biology, we set out to search for neural network architectures that can already (sort of) perform various tasks even when they use random weight values.

Discussion: https://lnkd.in/gyCrAkf
Article: https://lnkd.in/gZR5Z2j
PDF: https://lnkd.in/g_ERXQR
✴️ @AI_Python_EN

My first blog post: An introduction to Visual Question Answering.
https://anikem.github.io/vanilla-vqa/

✴️ @AI_Python_EN

📚📖 Python Machine Learning Tutorial 📖📚

➡️ Python Machine Learning – Tasks and Applications ( https://lnkd.in/fZcs-xE)
➡️ Python Machine Learning Environment Setup – Installation Process (https://lnkd.in/fJHwbjr)
➡️ Data Preprocessing, Analysis & Visualization (https://lnkd.in/fVz58kJ)
➡️ Train and Test Set (https://lnkd.in/fq_GXjn)
➡️ Machine Learning Techniques with Python (https://lnkd.in/fjdsQzd)
➡️ Top Applications of Machine Learning (https://lnkd.in/f-CNyK2)
➡️ Machine Learning Algorithms in Python – You Must Learn (https://lnkd.in/fTxCA23)

#python #machinelearning #datascience #data #dataanalysis #artificialintelligence #ai #visualization #algorithms

✴️ @AI_Python_EN

Recurrent Neural Networks cheatsheet By Afshine Amidi and Shervine Amidi

#deeplearning

✴️ @AI_Python_EN

PyTorch has launched "PyTorch Hub" similar to TensorFlow Hub for publishing and reusing pre-trained models like ResNet, BERT etc.

This is in beta mode currently. More details in the below link:
https://lnkd.in/fs4YyFw
✴️ @AI_Python_EN

Master the Classification and Regression

Easily learn an End to End data science process and steps in two days!

📚Contents:
✅Exploratory data analysis and visualization
Numeric descriptive statistics
Interpreting descriptive statistics
Understanding the distribution
Histograms
Boxplots and IQR
Correlation
heatmaps for co-relation
Analyzing the target variable
✅Pre-processing data
Dealing with missing values
Treatment of categorical values
Normalize the data
Split the data
✅ #MachineLearning algorithm
Defining/instantiating the model
Fitting the model
Define the evaluation metric
Predict scores against our test set and assess
✅Evaluation metrics for classification
Improving a model – from baseline models to final models
Understanding cross-validation
Feature engineering
#Regularization to prevent overfitting
#Ensembles – typically for classification
Test alternative models
#Hyperparameter tuning
✅Regression #Code
✅Classification #Code

✴️ @AI_Python_EN

We're now available via #linkedin :)

link :
https://www.linkedin.com/groups/13723396/


✴️ @AI_Python_EN