1/ It's probably not the first thing you think of, but when we started .NET (COM+) in the late 90s, C# didn't exist yet. We were working on it at the same time as the CLR and the framework. So, you might wonder, what language was being used to generate IL and write the BCL?
More from Machine learning
With hard work and determination, anyone can learn to code.
Here’s a list of my favorites resources if you’re learning to code in 2021.
👇
1. freeCodeCamp.
I’d suggest picking one of the projects in the curriculum to tackle and then completing the lessons on syntax when you get stuck. This way you know *why* you’re learning what you’re learning, and you're building things
2. https://t.co/7XC50GlIaa is a hidden gem. Things I love about it:
1) You can see the most upvoted solutions so you can read really good code
2) You can ask questions in the discussion section if you're stuck, and people often answer. Free
3. https://t.co/V9gcXqqLN6 and https://t.co/KbEYGL21iE
On stackoverflow you can find answers to almost every problem you encounter. On GitHub you can read so much great code. You can build so much just from using these two resources and a blank text editor.
4. https://t.co/xX2J00fSrT @eggheadio specifically for frontend dev.
Their tutorials are designed to maximize your time, so you never feel overwhelmed by a 14-hour course. Also, the amount of prep they put into making great courses is unlike any other online course I've seen.
Here’s a list of my favorites resources if you’re learning to code in 2021.
👇
1. freeCodeCamp.
I’d suggest picking one of the projects in the curriculum to tackle and then completing the lessons on syntax when you get stuck. This way you know *why* you’re learning what you’re learning, and you're building things
2. https://t.co/7XC50GlIaa is a hidden gem. Things I love about it:
1) You can see the most upvoted solutions so you can read really good code
2) You can ask questions in the discussion section if you're stuck, and people often answer. Free
3. https://t.co/V9gcXqqLN6 and https://t.co/KbEYGL21iE
On stackoverflow you can find answers to almost every problem you encounter. On GitHub you can read so much great code. You can build so much just from using these two resources and a blank text editor.
4. https://t.co/xX2J00fSrT @eggheadio specifically for frontend dev.
Their tutorials are designed to maximize your time, so you never feel overwhelmed by a 14-hour course. Also, the amount of prep they put into making great courses is unlike any other online course I've seen.
This is a Twitter series on #FoundationsOfML.
❓ Today, I want to start discussing the different types of Machine Learning flavors we can find.
This is a very high-level overview. In later threads, we'll dive deeper into each paradigm... 👇🧵
Last time we talked about how Machine Learning works.
Basically, it's about having some source of experience E for solving a given task T, that allows us to find a program P which is (hopefully) optimal w.r.t. some metric
According to the nature of that experience, we can define different formulations, or flavors, of the learning process.
A useful distinction is whether we have an explicit goal or desired output, which gives rise to the definitions of 1️⃣ Supervised and 2️⃣ Unsupervised Learning 👇
1️⃣ Supervised Learning
In this formulation, the experience E is a collection of input/output pairs, and the task T is defined as a function that produces the right output for any given input.
👉 The underlying assumption is that there is some correlation (or, in general, a computable relation) between the structure of an input and its corresponding output and that it is possible to infer that function or mapping from a sufficiently large number of examples.
❓ Today, I want to start discussing the different types of Machine Learning flavors we can find.
This is a very high-level overview. In later threads, we'll dive deeper into each paradigm... 👇🧵
Last time we talked about how Machine Learning works.
Basically, it's about having some source of experience E for solving a given task T, that allows us to find a program P which is (hopefully) optimal w.r.t. some metric
I'm starting a Twitter series on #FoundationsOfML. Today, I want to answer this simple question.
— Alejandro Piad Morffis (@AlejandroPiad) January 12, 2021
\u2753 What is Machine Learning?
This is my preferred way of explaining it... \U0001f447\U0001f9f5
According to the nature of that experience, we can define different formulations, or flavors, of the learning process.
A useful distinction is whether we have an explicit goal or desired output, which gives rise to the definitions of 1️⃣ Supervised and 2️⃣ Unsupervised Learning 👇
1️⃣ Supervised Learning
In this formulation, the experience E is a collection of input/output pairs, and the task T is defined as a function that produces the right output for any given input.
👉 The underlying assumption is that there is some correlation (or, in general, a computable relation) between the structure of an input and its corresponding output and that it is possible to infer that function or mapping from a sufficiently large number of examples.
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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 chart📊 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 🧵 for you 🙏💞🙏
Keep learning / keep sharing 🙏
@AdityaTodmal
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)
Making thread \U0001f9f5 on trading view scanner by which you can select intraday and btst stocks .
— Vikrant (@Trading0secrets) October 22, 2021
In just few hours (Without any watchlist)
Some manual efforts u have to put on it.
Soon going to share the process with u whenever it will be ready .
"How's the josh?"guys \U0001f57a\U0001f3b7\U0001f483
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 chart📊 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 🧵 for you 🙏💞🙏
Keep learning / keep sharing 🙏
@AdityaTodmal
