Top Tweets for #1minuteRead
A new brief post (& a new #haiku) on understandarama about #Melbourne’s 6th #COVID19Aus #lockdownextension. A #1MinuteRead.
Cut the engine
sit in the car
enjoy the rain.
#AusHaiku #AusPoetry #LockdownHaiku #LockdownPoem
#MelbHaiku
https://t.co/qFQnU05kqu
The third of my three Micro-Fiction entries in the #storiesspace competition.
I would love for you to drop by and see what you make of it. 😉
#riop #iartg #1minuteread #amwriting
Just published at @storiesspace https://t.co/4ms0mNhWHE . #drinking
New blogpost #1minuteread thanks @Taste_Blasu @RadarforPR Let’s all #keeplocalalive @nwalessocial @networx4biz @MoldReduction
Great to chat with @RadarforPR #1minuteread @nwalessocial @Celtic_FP @networx4biz @DeesideDotCom #keeplocalalive
Moel Famau gin maker @RealFruitSpirit pours effort into home deliveries to survive lockdown. More from @Taste_Blasu at @DeesideDotCom #keeplocalalive
https://t.co/6Pq4M0iBDm
10 Chairs Named After Eminent Women To Be Established In Varsities, Initiative To Motivate Young Girls And Women Towards Higher Studies
By - @shivangis23 and @pragyyaaa
Read Here :
https://t.co/gGBl8cTTYh
#women #highereducation #1minuteread #education #Newsbyte
Daily Readings (58s): No one describes Himalayas better than @RealRuskinBond His fondness reflected in words transports us to the lap of mountains immediately.
Excerpt from Himalaya, anthropology edited by Ruskin Bond and @NamitaGokhale_
#1minuteread #himalayas #dailyread
#day39of100 #1minuteRead #machinelearning #datascience #tipfortoday #100tipsforML
Let's take an example of linear regression to understand the concept of #bias .
->Y=target variable; X=input feature variable; H=Hypothesis function; W1, W2...=weight; C=intercept
(Part 1) #day37of100 #1minuteRead #machinelearning #datascience #tipfortoday #100tipsforML
When does a Convolutional Neural Network classifier (CNN) work #out_of_specs and how to detect it?
- If a CNN is trained to predict between some classes, say, man, bus and bike, and it is
#day35of100 #1minuteRead #machinelearning #datascience #tipfortoday #100tipsforML
->In general, when researching novel #convnet architectures, researchers come up with small models which perform well on small datasets like CIFAR10. Once they have a good architecture,
#day31of100 #1minuteRead #machinelearning #datascience #tipfortoday #100tipsforML
-> Lets talk a little about #Rectified_Adam optimizer. It is a kind of upgraded version of Adam Optimizer which outperforms it in almost every use case.
-> It's a common practice in AI community to
Download this week's God Thoughts here:
https://t.co/9Q1FSmiCBZ
#oneminuteread #1minuteread #coffeebreak #WeeklyGodThoughts #lightacandleforChristmas #followthestar
#day25of100 #1minuteRead #machinelearning #datascience #tipfortoday #100tipsforML
How to reduce #overfitting in neural networks?
-Early Stopping: Stop the training when the evaluation metric reaches the best value in the validation set.
- #L1regression: Add lamda*|w|, for every
#day21of100 #1minuteRead #machinelearning #datascience #tipfortoday #100tipsforML
#computervision #imageclassification #tensorflow_slim
There are many helpful videos on how to make your image classifier using TensorFlow. But once you are through with that and start working on
#day19of100 #1minuteRead #machinelearning #datascience #tipfortoday #100tipsforML
An #exception is an error that happens during execution of a program. Handling these exceptions makes your program #robust.
I am working on a project where I need to extract facial features from
#day16of100 #1minuteRead #machinelearning #datascience #tipfortoday #100tipsforML
A few tips for your #NLP model.
-> Word embeddings are matrices that convert the one-hot encodings of words to embedded vectors that can be given as input to your model. In order to learn word
#day8of100 #1minuteRead #machinelearning #datascience #tipfortoday #100tipsforML
#overfitting
What is overfitting (or high variance)?
Overfitting is a problem that arises when our model fits the training data too well but performs poorly on test set,i.e., any unseen data.
#day7of100 #1minuteRead #machinelearning #datascience #tipfortoday #100tipsforML
#performanceMetrics
Recall = True Positives / (True Positives + False Negatives)
Use Recall when false negatives has serious consequences like in case of tumor detection.
#day2of100 #1minuteRead #machinelearning #datascience #tipfortoday #100tipsforML
#activationfunctions
What "activation function" to use for your basic neural network?
Use "Relu" activation for hidden layers.
For the output layer, use "sigmoid" for binary classification,
#1minuteRead #machinelearning #datascience #tipfortoday #day1of100 #100tipsforML
What is "oscillating loss" and how to get rid of it?
When the loss while training your neural network increases and decreases like in the image shown below, it's called "oscillating loss".
a 1 minute read that will stay with you all day
#1MinuteRead #1MinRead #OneMinuteRead #poem #poetry
https://t.co/ROggQyScAj
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