Deep Thought Data Science

Data science needs deep thought #DataScience #BigData #Analytics #AI #ML #IoT #Business #DeepLearning #DigitalTransformation #Blockchain #Python #R #Cloud #BI

[Deep Thought Data Science @deepthoughtdasc]

Life, The Universe & Everything: 42 or 4He2?
#BigData #DataScience #AI #ML #IoT #DeepLearning #Business
https://t.co/4gDr8dmfgN

[Lénaïc Chizat @LenaicChizat]

This video shows the evolution of the parameters (left) and the predictor (right) for a wide 2-layer ReLU neural network (gradient descent on logistic loss). I explain a bit of what's going on in this virtual talk I gave at MSRI (jt. work w/@BachFrancis) https://t.co/GjC8NPNKfX

[Deep Thought Data Science @deepthoughtdasc]

Google AI Blog:Towards a Conversational Agent that Can Chat About…Anything https://t.co/wEBX2zUS9u via @googleai

[Deep Thought Data Science @deepthoughtdasc]

An Idea From Physics Helps AI See in Higher Dimensions https://t.co/yXWlprO1dP via @QuantaMagazine

[Deep Thought Data Science @deepthoughtdasc]

Best Machine Learning Research of 2019 https://t.co/QBREwHCMhV

[Deep Thought Data Science @deepthoughtdasc]

Using neural networks to solve advanced mathematics equations https://t.co/2YQyvGm0yG

[Deep Thought Data Science @deepthoughtdasc]

Uber Open-Sourced ‘Manifold’: A Visual Debugging Tool for Machine Learning https://t.co/ihlugn1PBs via @MarkTechPost

[Analytics Vidhya @AnalyticsVidhya]

Learn the concepts of #transferlearning and leverage it in NLP to build incredibly accurate models using popular #fastai library. Also explore #ULMFiT framework - a method to enable transfer learning for any NLP task & achieve great results. https://t.co/1kFjLxjJqd #DeepLearning

[KDnuggets @kdnuggets]

How YouTube is Recommending Your Next Video - KDnuggets https://t.co/MQT06wfBU3

[KDnuggets @kdnuggets]

12 Things I Learned During My First Year as a Machine Learning Engineer https://t.co/kFVMF3ZGMs

[KDnuggets @kdnuggets]

This New Google Technique Help Us Understand How Neural Networks are Thinking https://t.co/j4BHfLBoHF

[ODSC (Open Data Science Conference) AI @_odsc]

Optimizing hyperparameters for machine learning models is a key step in making accurate predictions, as they define characteristics of the model that can impact model accuracy and computational efficiency. #DataScience #MachineLearning https://t.co/JYHs0KiFB4

[Deep Thought Data Science @deepthoughtdasc]

Analyze BigQuery data with Kaggle Kernels notebooks @google https://t.co/sMbH0IhTQg

[KDnuggets @kdnuggets]

NLP vs. NLU: from Understanding a Language to Its Processing https://t.co/ZQ9fDfdACI

[KDnuggets @kdnuggets]

Google launches TensorFlow.Text library for language AI models https://t.co/cDEecGblbH

[T.X. St Visconti @txspectre]

How to Build Ethical Artificial Intelligence | Psychology Today https://t.co/PnvUJFAKln

[Thomas Harrer @Thomas_Harrer]

Google has created a maths AI that has already proved 1200 theorems.
@welcomeai
#artificialintelligence #google #maths #ai #industry #strategy #algebra #datascience #machinelearning #data #thinkatibm #ibm
🎥
TY, @marcusborba
cc: @andi_staub @SpirosMargaris

[Dr. Ganapathi Pulipaka 🇺🇸 @gp_pulipaka]

Benchmarking 20 Machine Learning Models Accuracy, Speed, and Scale. #BigData #Analytics #DataScience #AI #MachineLearning #IoT #IIoT #PyTorch #Python #RStats #TensorFlow #JavaScript #ReactJS #GoLang #CloudComputing #Serverless #DataScientist #Linux
https://t.co/zlNtSAX5fM

[KDnuggets @kdnuggets]

An introduction to explainable AI, and why we need it #KDN https://t.co/Bw2I5oTxtd

[Christopher Manning @chrmanning]

This paper gives some really nice insights and mathematical depth to what had previously (for us) been “the mystery of squared distance” in revealing the representation of parse trees in deep contextual representations (BERT, ELMo, etc.). Great to read!

[KDnuggets @kdnuggets]

https://t.co/ZF102OC4Xe Lesson 1 on Google Colab (#Free GPU) #KDN https://t.co/VQ6H25mYZX