What is Hacktoberfest 2020?

Hacktoberfest is a month-long global celebration for open-source contributions presented by DigitalOcean, Intel and DEV. It is celebrated in the month of October and everyone in the global community can participate. Whether you’re a developer, student learning to code, event host, or the company of any size, you can help drive the growth of open source and make positive contributions to an ever-growing community. All backgrounds and skill levels are encouraged to complete the challenge.

Rules to participate

To participate and for successful completion of Hacktoberfest 2020 challenge, one must abide by the following rules to earn the limited edition Hacktoberfest tee or tree reward.

1. You must register and make four valid pull requests (PRs) between October 1–31.
2. PRs can be made to participating public repos on GitHub, those that have the Hacktoberfest topic.
3. If a maintainer reports your pull request as spam or behaviour not in line with the project’s code of conduct, you will be ineligible to participate.
4. This year, the first 70,000 participants who successfully complete the challenge will be eligible to receive a prize.

Why do you think there is a need to celebrate Hacktoberfest?

Being an Open Source enthusiast, I see Hacktoberfest as bringing together two values, ‘Appreciation’ and ‘Inauguration’. Appreciation to the work of millions of open-source contributors and project maintainers who help in the constant improvement of free Open-Source Softwares (OSS). And, Inauguration to the Open-Source journeys of all new contributors who happened to submit their first pull request during Hacktoberfest events. DigitalOcean and its partners award these contributions by sending a token of appreciation i.e. your Hacktoberfest Tee with some awesome stickers.

What’s new this year?

This year, DigitalOcean is helping the environment by giving us the option to plant a tree rather than receive a shirt. Also, to limit the carbon impact, they are paying for carbon offsets.

Fun fact: Hacktoberfest shirts flew 336 million miles internationally last year. In total, it adds up to a fully loaded 747 flying 676 miles.

What can you contribute?

To get started with Open-Source you need not be a coder. You can even contribute to the documentation, spelling fixing, opening an issue etc. Though a basic knowledge of Git and GitHub is required to make contributions. You can get yourself familiar with Git commands by this cheatsheet by GitHub Education.

How to complete the Hacktoberfest challenge?

To complete the Hacktoberfest Challenge, you first need to have a GitHub account. Sign up to GitHub here: https://github.com/

Now, register at the official website of Hacktoberfest 2020 to make your pull requests count.

Now, search for a project on GitHub which has a ‘hacktoberfest’ topic to it by either typing ‘topic:hacktoberfest’ on GitHub search or just directly click here. You can filter repositories/projects by languages or sort them as required.

For the purpose of this blog, we’ll use the below repository:

WomenWhoCodeDelhi/Hacktoberfest2020

The first thing that is to be done while contributing to any project is to go through the README and CONTRIBUTING file. After reading through them, you will get to know what is to be done. For this repository, you need to add a markdown file inside the ‘participants’ folder with some of your basic details. You can complete this task by simply following the steps given in the CONTRIBUTING.md file.

To know more about the used commands, you can read this blog.

After you have completed the above steps, creating a pull request is pretty simple. Go to the repository on your profile and then click the “Compare & pull request” option.

It will direct you to the original repository. Update the topic of the pull request and add adescription. Then click on “Create pull request”

Voila!! You made a pull request. To complete the challenge you need to make at least 4 such pull requests.

Check your progress

To check if you have made at least 4 pull requests, eligibility for your contributions, you can check your status on the Hacktoberfest website.

https://hacktoberfest.digitalocean.com/

Just click on the Profile button on the top menu, and this will show you your pull requests and how many you have made so far.

There is a maturing period of 14 days. It means that your PR’s will be reviewed and if passed they’ll become valid — which means you’ll have successfully completed this year’s challenge.

Beyond Hacktoberfest!

Once you complete the challenge don’t stop contributing. Look for some ‘good first issues’ in Open Source projects that are used by millions of users. The pleasure of getting the PR merged in production into such organizations is incomparable.

Keep learning & Keep Contributing!

Hacktoberfest and How to contribute as a beginner? was originally published in Women Who Code Delhi on Medium, where people are continuing the conversation by highlighting and responding to this story.

Python is currently the most popular programming language for research and development in Machine Learning. According to Google Trends, the interest in Python for Machine Learning has spiked to an all-new high with other ML languages such as R, Java, Scala, Julia, etc. lagging far behind.

If Python is so much popular, then one must think Why Python? What does Python provide that other languages do not? What additional tools are present in Python that are not present in other development languages? Well let’s see and clear out doubts.

Reasons Why Python is chosen as the best programming language for Machine Learning?

To implement your ML and AI aspirations, you should use a programming language that is stable, flexible, and has tools available. Python offers all of this, which is why we see lots of Python AI projects today.

So let’s see the qualities of Python 🐍

Python is Simple to Understand and is Consistent 😃

Nobody likes excessively complicated things to learn and apply and so the ease of using Python is one of the main reasons why it is so popular for Machine Learning. It is simple with an easily readable syntax and that makes it well-loved by both seasoned developers and experimental students.

While complex algorithms and versatile workflows stand behind machine learning and AI, Python’s simplicity allows developers to write reliable systems. Developers get to put all their effort into solving an ML problem instead of focusing on the technical nuances of the language.

In addition to this, Python is also supremely efficient. It allows developers to complete more work using fewer lines of code. Many programmers point out the frameworks, libraries, and extensions that simplify the implementation of different functionalities. It’s generally accepted that Python is suitable for collaborative implementation when multiple developers are involved.

Since Python is a general-purpose language, it can do a set of complex machine learning tasks and enable you to build prototypes quickly that allows you to test your product for machine learning purposes.

You can see, it’s just one feature we are talking about for Python and this only feature is making us love Python more!

Python has multiple Libraries and Frameworks

Implementing AI and ML algorithms can be tricky and requires a lot of time. It’s vital to have a well-structured and well-tested environment to enable developers to come up with the best coding solutions.

The ML algorithms are very complex, but Python is the rescue with an extensive range of libraries and frameworks. A software library is a pre-written code that is used by developers during a common programming task. These libraries help the programmer and reduce development time. Python has a rich technology stack and has a different set of libraries for Machine learning.

• Keras, TensorFlow, and Scikit-learn for machine learning
• SciPy for advanced computing
• Seaborn for data visualization
• Pandas for general-purpose data analysis
• NumPy for scientific computing and data analysis

Scikit-learn features various classification, regression, and clustering algorithms, including support vector machines, random forests, gradient boosting, k-means, and DBSCAN, and is designed to work with the Python numerical and scientific libraries NumPy and SciPy.

With these solutions, you can develop your product faster. Also, the developers can use the existing libraries to implement necessary features.

Python is Flexible 😌

Python is known as the most flexible language in machine learning. It provides various options for users. The flexibility factor reduces the possibility of errors. It let the programmers take the situation completely under control, and work on it comfortably.

• Programmers can combine Python with other languages to get the desired results.
• You do not require recompiling source code. Programmers can implement their changes in coding and see the results quickly.

Python is Platform Independent

Platform independence refers to a programming language or framework allowing developers to implement things on one machine and use them on another machine without any (or with only minimal) changes. One key to Python’s popularity is that it’s a platform-independent language. Python is supported by many platforms including Linux, Windows, and macOS. Python code can be used to create standalone executable programs for most common operating systems, which means that Python software can be easily distributed and used on those operating systems without a Python interpreter.

What’s more, developers usually use services such as Google or Amazon for their computing needs. However, you can often find companies and data scientists who use their machines with powerful Graphics Processing Units (GPUs) to train their ML models. And the fact that Python is platform-independent makes this training a lot cheaper and easier.

Python has Great Community and Corporate Support

Python has been around since 1990 and that is ample time to create a supportive community. Because of this support and the simplicity of this language, Python learners can easily improve their Machine Learning knowledge and the implementation, which only leads to increasing popularity.

In the Developer Survey 2018 by Stack Overflow, Python was among the top 10 most popular programming languages, which ultimately means that you can find and create a development company with the necessary skill set to build your AI-based project. If you look closely at the image below, you’ll see that Python is the language that people Google more than any other.

Also, Corporate support is a very important part of the success of Python for ML. Many top companies such as Google, Facebook, Instagram, Netflix, Quora, etc use Python for their products. Google is single-handedly responsible for creating many of the Python libraries for Machine Learning such as Keras, TensorFlow, etc.

It’s a well-known fact that the Python AI community has grown across the globe. There are Python forums and an active exchange of experience related to machine learning solutions. For any task you may have, the chance is pretty high that someone else out there has dealt with the same problem. You can find advice and guidance from developers. You won’t be alone and are sure to find the best solution to your specific needs if you turn to the Python community.

So these are some of the reasons we choose Python for Machine Learning. I hope you find this blog informative. I would greatly appreciate it if you kindly give me some feedback.

Also, these are some of the links I referred to while writing this blog:

1. https://steelkiwi.com/blog/python-for-ai-and-machine-learning/
2. https://hackernoon.com/why-python-used-for-machine-learning-u13f922ug
3. https://www.geeksforgeeks.org/why-is-python-the-best-suited-programming-language-for-machine-learning/

Thank you for reading and Happy Learning! 😀

Python ? Why Python ? was originally published in Women Who Code Delhi on Medium, where people are continuing the conversation by highlighting and responding to this story.

People often use these three terms interchangeably but they do not quite refer to the same things.

Refer to the diagram shown below for this.

As per the diagram, AI seems to be the superset of ML, and ML seems to be the superset of DL. Let’s dive into these terms one by one.

So What is Artificial Intelligence?

As the name suggests, artificial intelligence can be loosely interpreted to mean incorporating human intelligence to machines.

Artificial intelligence (AI) refers to the simulation of human intelligence in machines that are programmed to think like humans and mimic their actions. The term may also be applied to any machine that exhibits traits associated with a human mind such as learning and problem-solving.

Some of the activities computers with artificial intelligence are designed for include: Speech recognition, Learning, Planning.

For example, such machines can move and manipulate objects, recognize whether someone has raised the hands, or solve other problems. These are the examples of General AI.

There’s Narrow AI as well. The narrow intelligence AI machines can perform specific tasks very well, sometimes better than humans — though they are limited in scope. The technology used for classifying images on Pinterest is an example of narrow AI.

What is Machine Learning?

As the name suggests, machine learning can be loosely interpreted to mean empowering computer systems with the ability to “learn”. ML Intends to enable machines to learn by themselves using the provided data and make accurate predictions.

ML is a subset of artificial intelligence. It is a method of training algorithms such that they can learn how to make decisions.

For example, here is a table that identifies the type of fruit-based on its characteristics:

As you can see in the table above, the fruits are differentiated based on their weight and texture. However, the last row gives only the weight and texture, without the type of fruit. Therefore, a machine learning algorithm can be developed to try to identify whether the fruit is an orange or an apple.

After the algorithm is fed with the training data, it will learn the different characteristics between an orange and an apple. Therefore, if provided with data of weight and texture, it can predict accurately the type of fruit with those characteristics.

Finally, What is Deep Learning?

As earlier mentioned, deep learning is a subset of ML; in fact, it’s simply a technique for realizing machine learning. In other words, DL is the next evolution of machine learning.

Just like we use our brains to identify patterns and classify various types of information, deep learning algorithms can be taught to accomplish the same tasks for machines.

The brain usually tries to decipher the information it receives. It achieves this through labeling and assigning the items into various categories. Whenever we receive new information, the brain tries to compare it to a known item before making sense of it — which is the same concept deep learning algorithms employ.

Deep learning networks can be successfully applied to big data for knowledge discovery, knowledge application, and knowledge-based prediction.

For example, Automated Driving: Automotive researchers are using Deep Learning to automatically detect objects such as stop signs and traffic lights. Besides, Deep Learning is used to detect pedestrians, which helps decrease accidents.

I hope you get a clear reference of these terms. Here I’ve added some references that I’ve referred to while writing this blog.

1. https://towardsdatascience.com/clearing-the-confusion-ai-vs-machine-learning-vs-deep-learning-differences-fce69b21d5eb
2. https://www.quora.com/What-s-the-difference-between-AI-ML-and-DL
3. https://www.analyticsvidhya.com/blog/2017/04/comparison-between-deep-learning-machine-learning/

Thank you for reading and Happy Learning!

What’s exactly the difference between Artificial Intelligence, Machine Learning and Deep Learning ? was originally published in Women Who Code Delhi on Medium, where people are continuing the conversation by highlighting and responding to this story.

Neuron

Neurons are cells within the nervous system that transmit information to other nerve cells, muscle, or gland cells. Most neurons have a cell body, an axon, and dendrites.

The cell body contains the nucleus and cytoplasm. The axon extends from the cell body and often gives rise to many smaller branches before ending at nerve terminals. Dendrites extend from the neuron cell body and receive messages from other neurons. The dendrites are covered with synapses formed by the ends of axons from other neurons.

Neurons are connected to one another, but they do not actually touch each other. Instead they have tiny gaps called synapses. These gaps are chemical synapses or electrical synapses which pass the signal from one neuron to the next.

In order for neurons to communicate, they need to transmit information both within the neuron and from one neuron to the next.

There are about 86 billion neurons in the human brain, which is about 10% of all brain cells.

Our brain processes information using a network of neurons. They receive input, process it, and accordingly output electric signals to the neurons it is connected to.

You might be wondering why we need to study neurons in deep learning. We will now explain that in the remaining article.

After studying the hierarchical arrangement of neurons in biological sensory systems, scientists modeled artificial neurons. These are represented as the nodes in an artificial neural network and the connections between the nodes are shown by means of layers.

Artificial neuron :

An artificial neuron is a mathematical function conceived as a model of biological neurons, a neural network.

It computes the weighted average of its input, and this sum is passed through a nonlinear function, often called the activation function, such as the sigmoid, and produces the output.

When a neuron processes the input it receives, it decides whether the output should be passed on to the next layer as input. The decision of whether or not to send information on is called bias and it’s determined by the threshold value of the activation function built into the system.

Every neuron has input connections and output connections. These connections simulate the behavior of the synapses in the brain. The same way that synapses in the brain transfer the signal from one neuron to another, connections pass information between artificial neurons.

To understand neuron functioning with an example, refer to this blog:

What is an Artificial Neuron?

Hebb’s Rule

For biological neuron :

Hebb’s Rule demonstrates that in the brain, the learning is performed by a change in synaptic gaps of biological neurons.

Hebb’s rule states that when the axon of a cell A is close enough to excite a B cell and takes part on its activation in a repetitive and persistent way, some type of growth process or metabolic change takes place in one or both cells so that increases the efficiency of cell A in the activation of B.

In simple words, if two neurons on either side of the synapse (connection) are activated simultaneously, then the strength of that synapse is selectively increased. Due to this, information is passed between these neurons and vice versa.

On the account of this, Donald Hebb also stated a famous phrase that “neurons that fire together wire together”

For Artificial neuron :

Hebb’s rule demonstrates that when a synaptic weight contributes to firing (or activating ) an artificial neuron, that weight is increased and vice versa.

It forms the basis of the algorithm that consists of updating the weight of the neuronal connection. Hebb’s principle can also be described as a method of determining how to alter the weights between neurons based on their activation.

Hebb’s rule states that “the weight vector is found to increase proportionality to the product of the input and the learning signal”

Weight ( new) = weight (old) + input signal X learning signal

In the end, the learning signal becomes the neuron’s output. Hebb’s rule can be used for pattern association, pattern categorization, pattern classification, and over a range of other areas.

MP Neuron (McCulloch-Pitts Neuron)

It is the first mathematical model proposed by Warren McCulloch (neuroscientist) and Walter Pitts (logician) in 1943. It is a neuron of a set of inputs and one output. The input, as well as the output, can be either a 0 or a 1. The MP neuron model is also known as the linear threshold gate model.

In the above figure,

x1,x2……xm are the inputs in which w1, w2, w3, w4….wm are its weights respectively

g is the Adder function which performs a summation of weighted inputs

f is the Activation function which takes the decision

y is the final output

This is the mathematical function of g and y. Here b is the threshold value which decides whether the output should be ‘0’ or ‘1’.

Let’s understand MP neuron by example :

This is the example of a Movie planning scenario where we find output(whether go for a movie or not). The weights, w1 and w2, as well as the threshold value are decided by the hit and trial method. But here, for simplicity, we are assuming w1 and w2 as 1 and threshold value as 2.
In the output, 1 is used for true value/yes and 0 is used for false value/no.

Steps to follow to find output:

Step 1: Input values for features X1 and X2 for different conditions where each row represents 1 condition.

Step 2: Find their weights w1 and w2 by hit and trial method.

Step 3: Find their Adder value or Weighted Sum for each row.

Step 4: Find the threshold value by the brute force method.

Step 5: With the help of the activation function we decide whether our Adder value reached the threshold or not.

Step -6: If it reaches the threshold, the output is 1 (Go for a movie ) else it is 0 (Not go for a movie).

I hope this blog could help you get a better understanding of neuron, its communication in the brain, both in biological and mathematical terms and a small introduction about MP Neuron.

We will study Adder function, Threshold value, Activation Function in the next blog.

Thanks for your time!

Intuition Behind Deep Learning was originally published in Women Who Code Delhi on Medium, where people are continuing the conversation by highlighting and responding to this story.

What is Deep Learning?

Deep Learning involves a computation model that is composed of multiple processing layers, which learns representations of raw data by multiple levels of abstraction.

Now let’s dive deeper into the details:-

Deep Learning is a subset of artificial intelligence that imitates the working of the human brain (in the form of a neural network) in processing data and creating patterns for use in decision making.

In deep learning, we don’t need to explicitly program everything. Deep learning learns from a vast amount of unstructured data that would normally take humans decades to understand and process. Deep learning models can solve complex problems.

In simple words we can say that deep learning is the type of machine learning algorithm that divides the input into layers and it classifies, generates, and predicts data with much greater efficiency.

One very remarkable fact about deep learning can be understood from the following quote given by Ilya Sutskever :

“ If you have a large big dataset and you train a very big neural network, then success is guaranteed! ”

Deep Learning learns through examples, like a human baby learning from its surrounding world.

Applications of Deep Learning :

• Self-driving car
• Automatic text generation
• Virtual Assistant
• Natural language processing
• Pattern Recognition
• Computer Vision and much more

Types of networks used to develop Deep Learning models :

1. Convolution neural network:

It is a type of neural network containing filters that are not hardcoded, rather learned during the training process in order to extract relevant features from the input distribution.

2. Recurrent neural network:

It is a type of neural network that contains loops, allowing information to be stored within the network. In short, Recurrent Neural Networks use their reasoning from previous experiences to inform the upcoming events.

Advantages of Deep Learning :

• Maximum utilization of unstructured data
• Elimination of the need for feature engineering
• Ability to deliver high-quality results
• Elimination of unnecessary costs
• Elimination of the need for data labeling

Disadvantages :

• Require a large amount of data
• Expensive to implement its model

Following is the link to some slides to get a better understanding of neural networks:

https://docs.google.com/presentation/d/1IrM0MU2u5tJZLeTPX2qx3HreBh__JhOq7dPGB3fcC4U/mobilepresent?slide=id.g6ad5e89358_1_209

Ml and DL used web apps:

1. https://recto- accessum.000webhostapp.com/FLAPPY%20FIXED/FLAPPY_FILES/index.html

This is the demonstration of flappy bird game in which bird learns to play the game using reinforcement learning(a part of deep learning) and trains to get higher score in each round.

2. https://quickdraw.withgoogle.com/shared/vlNg0sr2MhFm

This is a fun web app in which we trained a large dataset of doodle images with the help of neural networks and we have asked to draw a particular doodle and our model predicts it is correct or not.

Deep Learning over Machine Learning

1. Why I can’t get the efficiency of 90%
2. How to fit my model for unlabelled data

To answer these doubts, deep learning comes into the picture !!

In order to find out when and where deep learning proves to be a better choice over machine learning, let’s first understand a bit about machine learning.

Machine Learning is a set of algorithms that parse data, learn from them, and then apply what they’ve learned to make intelligent decisions. Example: Facebook recognizes your friend’s face in a digital photo

Whereas Deep Learning is a neural network which studies data, classifies it, and develops patterns to make predictions.

What Deep learning can do but machine learning can’t?

• It can develop fake new data from existing real data
• It can develop games by reinforcement learning
• It generates a model of greater efficiency

The idea behind machine learning is that the machine can learn without human intervention. The machine needs to find a way to learn how to solve a task given the data.

Deep learning is a breakthrough in the field of artificial intelligence. When there is enough data to train on, deep learning achieves impressive results, especially for image recognition and text translation. The main reason is the feature extraction is done automatically in the different layers of the network.

Decision making procedure in ML and DL:

Machine learning uses algorithms to parse data, learn from that data, and make informed decisions based on what it has learned.

Deep learning structures algorithms in layers to create an “ artificial neural network ” that can learn and make intelligent decisions on its own.

Have a look at the following blog to know more about how deep learning is used in practice:

https://www.forbes.com/sites/bernardmarr/2018/08/20/10-amazing-examples-of-how-deep-learning-ai-is-used-in-practice/#22674b99f98a

Two simple scenarios to show where ML, DL is used

Scenario-1 :

You are given the following dataset (showing only 5 rows of it, suppose the dataset has 200 rows )

This dataset is used to predict whether a person will have a fever or not.

Training data = Given table data

Testing data= (Sanya,yes,yes,yes)

Output Predicted = yes

Did this problem be solved by machine learning or deep learning? So to decide this, notice carefully a few things :

• The given dataset is quite small.
• After studying the table, we note that features like “ consume cold drinks ”, “set AC at low temp ”, “has cold” have a great influence on whether the person would have a fever or not while rest features don’t seem to have any relation with the target variable.
• Since the dataset is small and we have less no of the important features mentioned, Machine learning can be employed in this case.

Scenario-2 :

You are given the following dataset ( showing only 5 rows and 11 features, suppose the dataset has 2,00,000 rows and 500 features)

This dataset is used to predict whether a person will have a fever or not.

Training data = Given table data

Testing data= (Sanya,yes,yes,yes,no,45,no,no,yes,12,day,…..)

Output Predicted = no

Should this problem be solved by using machine learning or by employing deep learning? To decide this, a few things can be noted:

• You can see this is quite a large dataset as compared to the previous one.
• Also, it’s difficult to decide which feature is more contributing to the target variable.
• Therefore deep learning is used in such cases.

Hope this blog could help you get a better understanding of deep learning, its importance, and its need in this digital era.

Thanks for your time!

Deep Learning vs Machine Learning was originally published in Women Who Code Delhi on Medium, where people are continuing the conversation by highlighting and responding to this story.

Now, let’s state what Machine Learning actually is!

What is Machine Learning?

Machine learning is an application of artificial intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed. Machine learning focuses on the development of computer programs that can access data and use it to learn for themselves.

Types of Machine Learning

Machine Learning can be classified into four types:

1. Supervised Learning
2. Semi-Supervised Learning
3. Unsupervised Learning
4. Reinforcement Learning

Supervised Learning

Supervised learning is the most practical and widely adopted form of machine learning. It is where you have input variables (X) and an output variable (Y) and you use an algorithm to learn the mapping function from the input to the output.

Y = f(X)

The goal is to approximate the mapping function so well that when you have new input data (x) that you can predict the output variables (Y) for that data.

For example, we want an algorithm to predict house prices. To accomplish this, we would pass a set of training data with each row of data containing the features of the house like its size, number of bedrooms, the year the house was built, and the value we want to predict, namely the price. We pass many rows of this training data to the algorithm. The algorithm analyzes the features and the resultant price. It determines the relationship between the features and the price and creates a model that is trained to predict the price of the house based on the features present. Then, when a trained model is presented with the data for a new house, it executes its logic and accurately predicts the price for the new house.

Further classification of Supervised Learning:

Classification algorithms are used to predict/Classify the discrete values such as Male or Female, True or False, Spam or Not Spam, etc.

Regression algorithms are used to predict the continuous or quantitative values such as price, salary, age, etc.

Semi-Supervised Learning

Semi-supervised learning is actually the same as supervised learning except that of the training data provided, only a limited amount is labeled.

For example, In Image recognition, we might provide the system with several labeled images containing objects we wish to identify, then process many more unlabelled images in the training process.

Unsupervised Learning

In Unsupervised learning, we focus on clusters of like data. The algorithm analyzes input data and identifies the group of data that share the same traits.

For example, Look at the picture below.

An input data with the mixture of animal species is given as an input to an unsupervised machine learning algorithm. The algorithm helps in classifying the animal data separately into individual species.

Reinforcement Learning

Reinforcement Learning is a type of machine learning technique that enables an agent to learn in an interactive environment by trial and error using feedback from its actions and experiences.

For example, Imagine a baby is given a TV remote control at your home (environment). In simple terms, the baby (agent) will first observe and construct his/her representation of the environment (state). Then the curious baby will take certain actions like hitting the remote control (action) and observe how the TV responds (next state). As a non-responding TV is dull, the baby dislikes it (receiving a negative reward) and will take fewer actions that will lead to such a result(updating the policy*) and vice versa. The baby will repeat the process until he/she finds a policy (what to do under different circumstances) that he/she is happy with (maximizing the total (discounted) rewards).

*- Policy here refers to the change of state i.e, whether the TV is on or off.

I hope I helped you with understanding these terms well. A better understanding of these terms is the most essential prerequisite for understanding further ML concepts.

You guys can refer to the below sites to learn more about the growing world of Machine Learning:

1. https://www.geeksforgeeks.org/introduction-machine-learning/
2. https://towardsdatascience.com/introduction-to-machine-learning-for-beginners-eed6024fdb08
3. https://towardsdatascience.com/machine-learning-an-introduction-23b84d51e6d0
4. https://www.pluralsight.com/courses/python-understanding-machine-learning

Happy Learning!

Introduction to Machine Learning was originally published in Women Who Code Delhi on Medium, where people are continuing the conversation by highlighting and responding to this story.

You now start searching for ways to go live with a pre-recorded video and a lot of software application ads pop up. You decide to install one of them and later find out that all of them allow free streaming only for max 5 mins and the longer videos and content requires a paid subscription.

If all this is true for you, I completely understand what you might be feeling right now! :P

If you’re just here without going through all of the above mentioned problems, I’m happy I could save some of your time! :P

So first things first. Let’s begin by how to record a webinar or discussion!

So I used whereby.com for the group video call. It’s free version allows you to get 1 meeting room and add upto 4 participants by simply sharing the invite link with them.

Next, to record it, you don’t need a specific application to be installed if you’re using Windows 10. It has a hidden feature to record your screen along with your voice(even I didn’t know about this feature :P).

Go to your search bar and type in “game bar.” You’ll see an Xbox Game Bar application. It is basically an Xbox app Game DVR feature that makes it simple to take control of your gaming activities — such as broadcasting, capturing clips, and sharing captures to Twitter,etc.

All you need to do is place your cursor on the google application window where your whereby app is open and then click on the start button indicated with red in the above image.

Now, you can click anywhere on the screen and a small recording bar will be there on the side of your screen to stop the recording whenever you are done.

One quick note here is that even if you open another Google window to refer to some content or script, this application will still keep recording only your whereby tab (amazing, right? :P).

Once you are done with the recording, click on “Show all captures” indicated in blue in the above figure to view your recordings!

Alright, next you need to install the OBS studio app for streaming the video. The entire process for that has been beautifully explained here-

Once you go through this video, you might wonder why a still image would be required along with a video in a separate scene. The reason for that is while you connect and click on “Go Live” on your social media, you can set the image scene and once you’re all done with the set up, simply switch to scene 2 containing the video and your video will start playing live!

Here is the Facebook Live video I did after learning all the above- https://www.facebook.com/womenwhocodedelhi/videos/277402749936740/

That’s all! I’m glad you could make it work for you finally!

Let me know if you have any other questions!

Thank you for our time :D

Going Live with a Pre-recorded video was originally published in Women Who Code Delhi on Medium, where people are continuing the conversation by highlighting and responding to this story.

— Oprah Winfrey

I believe each one of us needs a mentor at some point in life. It helps one to connect with the right people and resources which helps in one’s growth and development. Being a part of the Mentorship program 1.0 by Women Who Code Delhi as a mentee had a huge impact on me. Believe me when I say that this program was a boon for me as it opened the door for so many new opportunities. I was greatly inspired by this program and wanted to organize it next year as well for girls who lack such mentorship and need guidance. Mentorship program 1.0 was a huge success and since the day it ended we received a lot of queries regarding when we will launch the second edition of the same. Hence in January 2020 we decided to launch the second edition of this program. I am grateful to Stuti Verma for letting me lead the program 😃

What is Mentorship Program?

Mentorship Program is an initiative by Women Who Code Delhi which serves as a platform to connect undergraduate women in STEM fields to women leaders in technology with an objective of furthering their professional development, expanding their professional network and gaining support in the field and their workplace.

It is a 5 week program with an agenda for each week. This year we had 10 mentors and 30 mentees. Each mentor was assigned 3 mentees.

Each week, each mentee had at least one call with her mentor, who answered her questions, and shared her journey, as well as assigned simple tasks in accordance with the agenda of that week. The agenda was flexible and could be changed as per mentees requirements.

On 11th January 2020, we officially launched the second edition of the Mentorship Program in our event ‘You, Opportunities and Everything in Between!’ held at Google office, Gurgaon and opened mentee registrations. We received an overwhelming response with 200+ registrations within just 5 days. It felt amazing to see even international students applying for the program! On 19th January 2020 we conducted an Introductory Hangouts call for all the mentees.

Week 1

Week 1 majorly focused upon mentors getting to know their mentees and their individual goals and expectations from this program.

Week 2

Week 2 majorly focused on Internships & Placements with mentors telling their mentees about various types of internships possible, their application process, how to prepare for placements and also conducted a mock interview with their mentees.

Week 3

Week 3 majorly focused on Scholarships and various open source programs. The mentors guided their mentees about the various scholarships available for underrepresented communities in STEM such as Women Techmakers scholarship program, GHC scholarship by Anita B.org to name a few. They also guided them through the application process for each scholarship.

Week 4

Week 4 majorly focused on Open Source Contributions/Machine Learning/Blockchain or any topic of study. Mentee was free to choose any topic as per her convenience.

Week 5

The last week focused on “Creating Your Presence” where each mentee had a myriad of topics to discuss with her mentor such as having a website showcasing their projects, the importance of keeping an updated LinkedIn profile etc.

At the end of each week, each mentee wrote a blog article summarizing what she learnt in that week, therefore creating an environment of collaborative learning where all of them can learn through each other’s experience.

My experience

I have been a part of Women Who Code Delhi for almost 2 years now and I can’t say enough how amazing this community is. This community has given me a lot, and has helped me discover a new version of myself. The Mentorship Program is really close to my heart. From being a mentee in the first edition to leading the second edition, I have learnt a lot.

Being the lead organizer for this program was a phenomenal experience filled with learning and growing into a better version of myself. From designing the timeline for the program, reaching out to all the mentors, launching the program, selecting the mentees, conducting the introductory call, to coordinating with all the mentors and mentees it was a wonderful experience!

I would like to thank all the Mentors for taking out time from their busy schedule and volunteering for this program, putting so much effort in it by conducting weekly calls and answering mentees doubts with utmost patience. Also cheers to all the mentees for participating in this and making it a success!

Thank you to the amazing team Stuti Verma, Shreya Gupta, Mansi Breja, Brihi Joshi, Jap Leen Kaur Jolly for motivating, guiding me and most importantly believing in me when at times even I didn’t believed in myself. For providing me constant feedback and helping me improve at each step. This is not just a team but a family ❤

Mentorship Program 2.0 was originally published in Women Who Code Delhi on Medium, where people are continuing the conversation by highlighting and responding to this story.

We, at WWCode Delhi, are super excited to share with you all the glimpses of one of our most memorable event, ‘You, Opportunities And Everything in Between’ held at Google Gurgaon, this Saturday.

The event began with an introduction to the WWCD community and its vision, by Mansi Breja. She briefed us all about the low numbers and ratios of women in technology and the importance of communities in bringing more women to the STEM field.

Then we had a brief Mentorship Program Open House, wherein Pallavi Bishnoi, very enthusiastically, introduced Mentorship Program 2.0 and invited each mentor to introduce themselves.

Open Source Opportunities

Following this, we had a panel discussion about Open Source Opportunities hosted by Stuti Verma, one of the WWCD Directors. The panellists included Isha Gupta, who cleared GSoC’19 at Public Lab; Lavanya Gaur, who cleared GSoC’19 at JBoss and Akshita Aggarwal, RGSoC’18 scholar at Probot. They discussed what keeps them motivated to contribute to Open Source and what road blockers they faced during the entire program period. The panellists also shared the tips to grab such opportunities, throwing light on the application procedure. Our audience found the discussion informative and enlightening.

To bring some waves of laughter among the audience, the WWCD team hosted a fun event-cum-tournament, ‘Join my Train’ and explained the rules of the game and gave a demo. While some attendees participated in the activity, the others cheered for the probable winner. The winner was given cool WWCD schwags at the end of the event for the same.

Scholarship and Retreat Opportunities

Then came the group talk about Scholarship and Retreat Opportunities! The house was full of scholarship recipients, Stuti and Swati Singh, Facebook F8 scholars, Lavanya and Shreya, Google Women Techmakers Scholars and Parul Aggarwal, Brihi Joshi and Khyati were Grace Hopper Conference India scholars. The recipients talked about the application timelines of respective scholarships, the procedure of application and the key takeaways from their experience. They all encouraged everyone to apply for scholarship opportunities.

Next, the attendees grabbed lunch and networked among themselves.

Internship Opportunities

Moving forward with the event, we had Internship Opportunities which included industrial internships, short term and long term research internship and internship in the Government sector. For this session, we invited Jap Leen, Muskan Hussain, Mansi, Shreya and Brihi to share their internship experiences and guide the audience for the same.

Jap Leen, who had been Google STEP intern, briefed the attendees about this amazing opportunity and its perks. She also talked about how to build one’s profile and how she prepared for the coding interviews. She also shared her enriching and fun experience interning at Google.

Muskan, who did her internship at Ministry of Electronics and Information Technology under Government of India shared a lot of Government opportunities available for college students like NITI Aayog and Digital India Internship Scheme. She also explained the application requirements, eligibility criteria and selection process with some snapshots of her experience. She concluded her talk with the pros and cons of doing an internship in a government firm.

Mansi, who interned at Goldman Sachs explained its application process and her detailed interview experience. She shared her preparation strategy and final tips and tricks for the interview. She also described the work culture and the flat hierarchy at Goldman Sachs.

Shreya discussed in great detail about her short term research internship at IPAM, UCLA and how did she prepare for it. In her talk, she has emphasised on developing a research profile and maintaining a really good CGPA to outshine the competition. She shared with us lots of snapshots of all her outings, must-visit places, cafes and how she taught her fellow mates volleyball.

Last but not least, Brihi, who had completed her Long term research internship at Snap Inc. explained the application and interview process. She also shared why did she choose Snap how she managed her college and internship together by extending her degree.

Finally, we officially launched The Mentorship Program 2.0. Pallavi and Jap Leen briefed the audience about all the details, the agenda, the mentors, the importance of a mentor in shaping the career curve of a mentee and finally opened the registrations for mentees on all our social media platforms, encouraging everyone to apply.

To sum up the event, Stuti delivered a vote of thanks to all of the attendees, speakers, Saurabh Rajpal from Google team, Samraj for the amazing posters and our super active volunteers Akanksha, Kamalpreet, Shruti and Soumya for helping us throughout the event. This event wouldn’t have been this grand without the hard work and efforts of everyone. Thank you!

PS: Apply here to be a mentee for the Mentorship Program 2.0 and follow this for slides of this event.

You, Opportunities and Everything in Between! was originally published in Women Who Code Delhi on Medium, where people are continuing the conversation by highlighting and responding to this story.

I got the chance to attend the meetup, held at Coding Blocks Pitampura , on Friday, 1st November, 2019 and it was indeed a great learning experience.

Session 1: Introduction to Women Techmakers Scholarship Program by Sashrika Kaur

The speaker of this session was Sashrika Kaur, a recipient of Google APAC Women Techmakers Scholar’19 and pre-final year computer science student of IGDTUW , Delhi. She has also been awarded with WeTech Goldman Sachs Mentorship and Scholarship Program Award’19. Apart from this she is also a core team member of WooTech.

Sashrika talked about in detail on how to get through the Women Techmakers Scholarship Application. She explained about the application process right from the eligibility criteria till the submission of the application form. She guided the attendees about essay questions of the application form. Being the past recipient of this scholarship she also shared her experience on it. She also discussed about the interview round of this scholarship and how to prepare for it and also shared her best experience of this scholarship i.e the retreat which hosted by Google globally. This session was concluded with questions and queries raised by the attendees.

Session 2: #IamRemarkable by Ambika and Shubhangi Gupta

The speakers of this session were Ambika and Shubhangi Gupta, pre-final year computer science students of IGDTUW and #I am remarkable facilitators.

#IamRemarkable is a Google initiative empowering women and underrepresented groups to celebrate their achievements in the workplace and beyond.

The objective of this initiative is to improve the self promotion motivation and skills of women and underrepresented groups and also challenge the social perception around self promotion.

This session was basically a workshop ,where attendees were told to write about Why #I am remarkable. After this all of the attendees had to speak what they have written beginning from #I am remarkable because…..

After this workshop , we had lunch and networking session .

Session 3: Community Impact Panel: Paving the way, women taking lead, making an impact! by WomenWhoCode Delhi, WiMLDS and WooTech.

There was a panel discussion that included the representatives from different women in tech communities ,namely Women Who Code Delhi ,Women in Machine Learning and Data Science and WooTech. The panelists and representatives of the respective communities were Jap Leen Kaur Jolly, Arushi Chauhan and Sashrika Kaur.

The questions from the audience were taken and were shortlisted for the panelists in panel discussion by Akanksha Tanwar ,member of Women Who Code Delhi.

The panel discussion was about how the communities can impact your career and shape your future and how they can help you in different domains of technology .The panelists also discussed on how we can become part of the above mentioned communities even if we are not part any women in tech related society or community in college or otherwise.They encouraged in joining communities and explained the importance of strong networking and how it helps in making you aware of various technologies in different domains and other opportunities. It is beneficial to be part of society or community both in college and outside. They also discussed on how to deal with Imposter Syndrome .

The questions regarding on how to take initial step in finding a suitable project and on how to keep going while learning a new technology , were also asked. The panelists discussed the sources for project and also motivated on clearing the doubts no matter how small it is. We have to keep moving and never quit , that’s how one never looses interest while pursuing or learning a new technology. The questions related to the data science were also answered by Arushi Chauhan.

The panelists also discussed about how to keep the track of the on going scholarships and opportunities and on what goes in the mind of the reviewer of the scholarship application.They also encouraged girls to always apply for the scholarship and not to give into the fear of rejection. There was also an enlightening discussion on personal touch and on how the technology can help you in stimulating your past experiences.

In conclusion , it was an amazing meetup with so many inspiring young females in technology and I am looking forward to attend similar events in the future!

Women Techmakers Scholars Meetup 1.0 was originally published in Women Who Code Delhi on Medium, where people are continuing the conversation by highlighting and responding to this story.