Day 14 #31DaysofML

ЁЯдФ How to pick the right #GoogleCloud #MachineLearning tool for your application?

Answer these questions
тЭУ What's your teams ML expertise?
тЭУ How much control/abstraction do you need?
тЭУ Would you like to handle the infrastructure components?

ЁЯз╡ ЁЯСЗ

@SRobTweets created this pyramid to explain the idea.
As you move up the pyramid, less ML expertise is required, and you also donтАЩt need to worry as much about the infrastructure behind your model.

To lear more watch this video ЁЯСЙ https://t.co/EqJNDmTfhV

#31DaysofML 2/10
@SRobTweets If youтАЩre using Open source ML frameworks (#TensorFlow) to build the models, you get the flexibility of moving your workloads across different development & deployment environments. But, you need to manage all the infrastructure yourself for training & serving

#31DaysofML 3/10
@SRobTweets Deep Learning VMs provide managed, click-to-deploy VMs for processing data & training the model
ЁЯФ╣ Popular ML frameworks pre-installed
ЁЯФ╣ Reduces the overhead of managing & allocating compute & storage required
ЁЯФ╣ But you figure out how youтАЩll serve those models

#31DaysofML 4/10
@SRobTweets Kubeflow - OS project for deploying ML workloads on #Kubernetes
ЁЯФ╣ Helps configure a multi-step ML pipeline including pre-processing data, training & serving the model
ЁЯФ╣ Run it on-premise or on any cloud
ЁЯФ╣ YouтАЩll still need to configure where itтАЩs managed

#31DaysofML 5/10
@SRobTweets AI Platform - managed service for all custom model needs
ЁЯФ╣ Includes tools for training & serving models, hosted notebooks, a data labeling service & more
ЁЯФ╣ Eg: take notebook code running on-premise with Kubeflow, and run it on GCP with AI Platform Notebooks

#31DaysofML 6/10
@SRobTweets BQML: Brings the power of ML closer to where the data is analyzed & make it accessible to data analysts
ЁЯФ╣ You donтАЩt have to write any of the underlying model code
ЁЯФ╣ Choose model type
ЁЯФ╣ Simple SQL queries to create & train the model & make predictions

#31DaysofML 7/10
@SRobTweets AutoML democratizes ML to build custom ML models regardless of ML expertise.
ЁЯФ╣ Use the UI to upload the data - images, video, text, or structured
ЁЯФ╣ Press "train" button
ЁЯФ╣ Model is available for prediction via an API
ЁЯФ╣ No need to deploy it yourself

#31DaysofML 8/10
@SRobTweets ML APIs: Easiest and fastest way to get started with AI
ЁЯФ╣ DonтАЩt need ML engineers or data scientists just some developers
ЁЯФ╣ Simple API request to pre-trained models for images, video, speech, text & translation
ЁЯФ╣ No need to supply any training data yourself

#31DaysofML 9/10
@SRobTweets ML APIs тЖТ https://t.co/XdR6oS5XrcтАЛ
AutoML тЖТ https://t.co/vbmIBiciLFтАЛ
BQML тЖТ https://t.co/Hs8zz57pcnтАЛ
AI Platform тЖТ https://t.co/zyYRq4HzT5тАЛ
Kubeflow тЖТ https://t.co/DNX7MftUb3тАЛ
Deep Learning VMs тЖТ https://t.co/9MG9KntYXbтАЛ
Tensorflow тЖТ https://t.co/G2xLT68gRXтАЛ

10/10

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Trading view scanner process -

1 - open trading view in your browser and select stock scanner in left corner down side .

2 - touch the percentage% gain change ( and u can see higest gainer of today)


3. Then, start with 6% gainer to 20% gainer and look charts of everyone in daily Timeframe . (For fno selection u can choose 1% to 4% )

4. Then manually select the stocks which are going to give all time high BO or 52 high BO or already given.

5. U can also select those stocks which are going to give range breakout or already given range BO

6 . If in 15 min chartЁЯУК any stock sustaing near BO zone or after BO then select it on your watchlist

7 . Now next day if any stock show momentum u can take trade in it with RM

This looks very easy & simple but,

U will amazed to see it's result if you follow proper risk management.

I did 4x my capital by trading in only momentum stocks.

I will keep sharing such learning thread ЁЯз╡ for you ЁЯЩПЁЯТЮЁЯЩП

Keep learning / keep sharing ЁЯЩП
@AdityaTodmal
THREAD: 12 Things Everyone Should Know About IQ

1. IQ is one of the most heritable psychological traits тАУ that is, individual differences in IQ are strongly associated with individual differences in genes (at least in fairly typical modern environments). https://t.co/3XxzW9bxLE


2. The heritability of IQ *increases* from childhood to adulthood. Meanwhile, the effect of the shared environment largely fades away. In other words, when it comes to IQ, nature becomes more important as we get older, nurture less.
https://t.co/UqtS1lpw3n


3. IQ scores have been increasing for the last century or so, a phenomenon known as the Flynn effect. https://t.co/sCZvCst3hw (N тЙИ 4 million)

(Note that the Flynn effect shows that IQ isn't 100% genetic; it doesn't show that it's 100% environmental.)


4. IQ predicts many important real world outcomes.

For example, though far from perfect, IQ is the single-best predictor of job performance we have тАУ much better than Emotional Intelligence, the Big Five, Grit, etc. https://t.co/rKUgKDAAVx https://t.co/DWbVI8QSU3


5. Higher IQ is associated with a lower risk of death from most causes, including cardiovascular disease, respiratory disease, most forms of cancer, homicide, suicide, and accident. https://t.co/PJjGNyeQRA (N = 728,160)