Imagine you’re invited into a small room,
and a person hands you a puzzle to solve. You solve it quickly, and the person
who gave you the puzzle says wow. You got that done fast. You must be really smart. She says let’s try one more. And gives you a choice
between two puzzles. One has the same number
of pieces as the first. The second has twice as many pieces. She asks you,
which puzzle do you want to solve? Which puzzle would you select? This scenario is based
on a real experiment. And if you’re like many
people who participated, you choose the simpler puzzle to solve. Now, with one small change in
the experiment, the situation is reversed. Most of the people in the second group
chose the more challenging puzzle with twice as many pieces. What was the difference
between the two groups? A simple change in response. In the second group, you must be
really smart was replaced with, that looks like it took a lot of effort. In other words,
the people who are praised for their effort push themselves to
try the more challenging task. Those that were labeled smart played
it safe and tried the simpler puzzle. This experiment demonstrates
the difference between a fixed mindset and a growth mindset. A fixed mindset puts a label and
a boundary on someone’s ability. Saying, you’re smart, suggests that you’ve got a fixed amount
of intelligence that never changes. If you feel like your intelligence
can’t change, then a failure, like not being able to
complete that second puzzle, feels like evidence that you’re not so
smart after all. While it might sound like a big compliment
to be called a math whiz or a programming genius, it’s been proven to actually limit
a person’s potential to grow and achieve. Just as labels like stupid or
phrases like, I guess I’m just not the programming type, keep people
from pushing themselves to learn and grow. On the other hand, a growth mindset is one
that sees effort as the key to success. A growth mindset likes challenges and accepts failure as just one
step along the learning path. The truth is,
we aren’t just born smart or stupid. We can always grow our intelligence and
learn new skills, and science backs this up. As we learn, our brains literally change. They grow new neurons in the hippocampus,
the area within the brain responsible for consolidating short term and
long term memory. New cells are created and
new connections made as we spend effort to learn a new skill,
put it into practice, and try new things. In fact, those cells aren’t just there,
just like no one’s just smart. They’re created through effort and
practice. Like a muscle, you can exercise
your brain to make it stronger. How can knowing this help you learn? If you embrace the growth mindset and
understand that learning is a process that requires effort, you’re better
prepared to take on new challenges. For example, when presented with
a new programming concept or language, you’ll rarely just get it. You’ll need to study and
practice to succeed, everyone does. It’s easy to get stuck
in the fixed mindset. So when you start hearing yourself say,
I’m no good at this, ask yourself instead, how can I improve? Instead of, I give up, say,
let me try a different way. Replace, I failed, with,
mistakes are part of learning. And understand when you say, this is too
hard, that learning just takes time. You can’t expect to get stronger
by going to a gym for just a week. And you can’t expect to learn a new
programming language instantly. As you put in the time and effort, your brain, like a muscle,
continues to grow bigger and stronger. So, don’t worry about a final grade. Don’t worry if a code
challenge takes you ten tries. Don’t obsess about the number of badges or
points you have. Just think about your next challenge, what new things you can learn, and
how you can grow as a developer. Because you can, and we all can.
You’ve got your Bachelor of Science or Engineering and you feel like taking your studies in a new direction? The Master in Science, Technology and Policy at ETH Zürich might just be the opportunity you’ve been looking for. Humankind is facing major challenges in trying to deal with many global developments, such as urbanization, the energy transition and the digital transformation. For both national and international policy-makers, your scientific and technical knowledge is more important than ever. While it is certainly rewarding to continue your studies in Science or Engineering in greater depth, the world is also looking for experts who can contribute to solving today’s complex, interdisciplinary societal challenges. As an Institute of ETH Zürich, one of the world’s very best universities, the Institute of Science, Technology and Policy builds a bridge between scientists and engineers, policy-makers and society. The Master in Science, Technology and Policy combines social science courses, focusing on policy analysis and evaluation, with your specific field of interest and takes you beyond the foundation of your Bachelor’s degree. The skills you will acquire in our Master’s programme are high in demand, particularly in government agencies, tech, life science and consulting firms, international organizations, NGOs and academia. Interested? Get in touch with the ISTP community now!
Mummy is calling us its gonna be evening…quickly come up You Mummy is calling Wait..I’ll show you.. and you’ve spoiled my daughter sure we kids gonna relate with this video and even others too will relate don’t forget to share this video with your family members do Subscribe to my channel and also press that bell icon Anantya..come down let’s play Anantya Babli is asking you to play Mumma I’m playing but she’s calling you outside Mumma tell her that I’m not at home leave it…all the time remain busy with this phone wanna get a picture no what rubbish you’ve done its rubbish
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IBM Watson Machine Learning: Build a multiclass classification model by running an AutoAI experiment
February 14, 2020
This video shows you how to build a Multiclass classification model that evaluates and rates car purchases based on typical criteria. For this video, you’ll use a Data Set called Car Evaluation which you’ll find in the IBM Watson Gallery. The data set is sourced from the University of California Irvine Machine Learning Repository. It includes 6 attributes someone would consider when purchasing a vehicle: the purchase price, the maintenance costs, number of doors, passenger capacity, size of the luggage area, and safety ratings. The last column is the evaluation for each vehicle: acceptable, good, unacceptable, or very good.
Add this data set to the Watson Machine Learning project, and then view the project. On the assets tab, you’ll see the data set.
Now you’re ready to add to the project, an AutoAI experiment. This project already has the Watson Machine Learning service associated. If you haven’t done that yet, first watch the video showing how to run an AutoAI experiment based on a sample. Just provide a name for the experiment, and then click Create.
Next, the AutoAI Experiment Builder displays. You first need to load the training data. In this case, the data set will be from the project. Select the CarEvaluation CSV file from the list. AutoAI reads the data set, and lists the columns found in the data set. In this case, you want to select Evaluation as the column to predict.
Now edit the experiment settings. First look at settings for the Data Source. If you have a large data set, you can run the experiment on a subsample of rows. And you can configure how much of the data will be used for training and how much will be used for evaluation. The default is a 90%-10% split where 10% of the data is reserved for evaluation. You can also select which columns from the data set to include when running the experiment. On the Prediction panel, you can select a prediction type. In this case, the Evaluation column includes four possible outcomes, so a multiclass classification model is most suitable. The default metric for a multiclass classification is accuracy. If you’d like, you can choose specific algorithms to consider for this experiment. On the General panel, you can review other details about the experiment.
Now save the settings. Now run the experiment, and wait as the Pipeline leaderboard fills in to show the generated pipelines using different estimators, such as the XGBoost classifier, or enhancements, such as hyperparameter optimization and feature engineering, with the pipelines ranked based on the Accuracy metric. Hyperparameter Optimization is a mechanism for automatically exploring a search space of potential Hyperparameters, building a series of models and comparing the models using metrics of interest. Feature engineering attempts to transform the raw data into the combination of features that best represents the problem to achieve the most accurate prediction.
It looks like AutoAI has chosen as the top algorithm, the LGBM Classifer which is a gradient boosting framework that uses leaf-wise (or horizontal) tree-based learning algorithm. Okay! The run has completed. View the progress map to see details of the run. It looks like pipeline 4 is ranked as the #1 pipeline. Viewing that pipeline in the leaderboard, you can see the model evaluation measures, and the ROC curve. You can also compare the pipelines. This chart provides metrics for the four pipelines viewed by cross-validation score or holdout score. And you can see the pipelines ranked based on other metrics such as F1 Weighted which is a weighted average of the precision and recall. You can select an individual pipeline to review the model evaluation which includes the ROC curve. During AutoAI training, your data set is split into two parts: training data and hold-out data. The training data is used by the AutoAI training stages to generate the model pipelines and cross-validation scores are used to rank them. After training, the hold-out data is used for the resulting pipeline model evaluation and computation of performance information such as ROC curves and confusion matrices. You can also view the precision recall curve, threshold chart, model information, and feature importance. This pipeline had the highest ranking, so you can save this as a machine learning model. Just accept the defaults, and save the model. Now view the model. The Overview tab shows a model summary and the input schema. On the Deployments tab, add a deployment. This will be a Web Service deployment with the specified name. When you’re ready, save the new deployment. When the model deployment is complete, view the deployment. The Overview tab shows the basic deployment information. On the Test tab, you can test the model prediction. You can either enter test input data, or paste JSON input data, and click Predict. This shows that the first car evaluation is good with 95% probability, the second is acceptable with 99.9% probability, the third is unacceptable with 99.9% probability, and the fourth is very good with 99.9% probability. On the Implementation tab, you’ll find the scoring end point for future reference. You’ll also find code snippets for various programming languages to utilize this deployment from your application. You can also view the API specification from here. And back on the Assets tab in the project. you’ll find the AutoAI experiment and the model, and on the Deployments tab, you’ll find the deployment. Find more videos in the IBM Watson Data and AI Learning Center. http://ibm.biz/ibm-machine-learning.
sometimes you might wonder: is googling the new thinking? you probably think printing out parts of
the internet belongs to the past. but what if the future means the Internet is
putting out parts of you? and what will it be like getting nursed
by this guy? speaking of robots can self-driving cars with a license? how
natural is the nature we engineer? and will this some day become the way we elect our government? technology is changing. society too. this might make you wonder. you know what: we wonder too.
This video shows you how to provision the IBM Cloud services necessary to work with the Watson Machine Learning service. You can to train, test, and deploy models using Watson Studio tools, or through the Watson Machine Learning Python client library or the IBM Cloud command line interface. If you plan to use Watson Studio tools, then you should provision the necessary services through Watson Studio. If you plan to work with the Watson Machine Learning service through the Python client libraries or the IBM Cloud command line, then you set up the services through IBM Cloud.
From the IBM Cloud Catalog, in the AI category, select the Watson Machine Learning service. First select a plan. The Lite plan is free and a great place to start. Then provide a unique name for the service. When you’re ready, click Create.
On the service launch page, you see links to IBM Watson Studio where you can use the tools to create Watson machine learning models, or you can check out the documentation to use Watson Machine Learning through the command line or Python Client Libraries. The Service credentials panel shows you the credentials you’ll need to access the service programmatically. Go back to the catalog, and under the Storage category, you’ll find Object Storage. Provision that service in the same way. To work with files in IBM Cloud Object Storage using the command line, you’ll need HMAC (or hash-based message authentication code) credentials for authentication. On the Service credentials panel, you can create new credentials. For the name, it makes sense to indicate these are HMAC credentials. Select Writer for the access role, and then select Auto Generate for the service ID. Check the box to include HMAC credentials which will fill in the space for inline configuration parameters with {“HMAC”:true} in JSON format. Then click Add. When you view the credentials, you’ll need the access_key_id, secret_access_key, and instance CRN with the IBM Cloud CLI. IBM Cloud Object Storage uses buckets to organize your data. Start by creating a bucket. You can either create a custom bucket where you can choose all of the advanced options, or you can create a standard bucket which uses the most common options. In this case, create a bucket with the standard options. The bucket name must be unique across the IBM Cloud Object Storage system. You can accept the suggested name or provide a custom name. With the standard options, the resiliency, location, and storage class are selected for you. When you continue to the next screen, you now have the option to upload files to the bucket. If you choose not to do that now, you can always add files to the bucket later. In this case, browse to upload a local CSV file to the bucket. Next, you have the option to test out the bucket with some JavaScript code.
And the next screen provides a summary. From here, you can view the objects in the bucket which shows the CSV file you uploaded. On the Configuration panel, you’ll find the bucket instance CRN along with the endpoints you’ll need to access the objects in the bucket. The documentation contains more information on building Watson Machine Learning models with Watson Studio tools, the IBM Cloud command line, and Python Client libraries. Find more videos in the IBM Watson Data and AI Learning Center at http://ibm.biz/learning-centers.
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IBM Watson Machine Learning: Binary Classification model using an AutoAI Experiment
February 11, 2020
In this video, you will see how to build a binary classification model that assesses the likelihood that a customer of an outdoor equipment company will buy a tent. This video uses a data set called GoSales which you’ll find in the IBM Watson Gallery. Add this data set to the Machine Learning project, and then go to the project. You’ll find the GoSales.csv file listed with your other Data Assets. View the data set. The feature columns are gender, age, marital_status, and profession and contain the attributes on which the machine learning model will base predictions. The label columns are Is_tent, product_line, and purchase_amounts and contain historical outcomes that the models could be trained to predict. Go back to the Assets tab, and add to the project, an AutoAI experiment. This project already has the Watson Machine Learning service associated. If you haven’t done that yet, first watch the video showing how to run an AutoAI experiment based on a sample. Just provide a name for the experiment, and then click Create. The AutoAI Experiment Builder displays. You first need to load the training data. In this case, the data set will be from the project. Select the GoSales CSV file from the list. AutoAI reads the data set, and lists the columns found in the data set. Since you want the model to predict the likelihood that a given customer will purchase a tent, select IS_TENT as the column to predict. Now edit the experiment settings. First look at settings for the Data Source. If you have a large data set, you can run the experiment on a subsample of rows. And you can configure how much of the data will be used for training and how much will be used for evaluation. The default is a 90%-10% split where 10% of the data is reserved for evaluation. You can also select which columns from the data set to include when running the experiment. On the Prediction panel, you can select a prediction type. In this case, AutoAI analyzed your data and determined that the IS_TENT column contains True/False information, making this data suitable for a binary classification model. And the positive class is True, and the default metric for a binary classification is ROC/AUC which balances precision, accuracy, and recall. If you’d like, you can choose specific algorithms to consider for this experiment. On the General panel, you can review other details about the experiment. In this case, accepting the default settings makes the most sense. Now run the experiment, and wait as the Pipeline leaderboard fills in to show the generated pipelines using different estimators, such as the XGBoost classifier, or enhancements, such as hyperparameter optimization and feature engineering, with the pipelines ranked based on the ROC AUC metric. Hyperparameter Optimization is a mechanism for automatically exploring a search space of potential Hyperparameters, building a series of models and comparing the models using metrics of interest. Feature engineering attempts to transform the raw data into the combination of features that best represents the problem to achieve the most accurate prediction. Okay! The run has completed. View the progress map to see details of the run.
Scroll down to view the pipeline leaderboard, You may want to start with comparing the pipelines. This chart provides metrics for the four pipelines viewed by cross-validation score. You can see the pipelines ranked based on other metrics such as accuracy or average precision. You can select an individual pipeline to review the model evaluation which includes the ROC curve. During AutoAI training, your data set is split into two parts: training data and hold-out data. The training data is used by the AutoAI training stages to generate the model pipelines and cross-validation scores are used to rank them. After training, the hold-out data is used for the resulting pipeline model evaluation and computation of performance information such as ROC curves and confusion matrices. You can also view the confusion matrix, precision recall curve, model information, and feature importance. This pipeline had the highest ranking, so you can save this as a machine learning model. Just accept the defaults, and save the model.
Now that you’ve trained the model, you’re ready to view the model, and deploy it. The Overview tab shows a model summary and the input schema. On the Deployments tab, add a deployment. This will be a Web Service deployment with the specified name. When you’re ready, save the new deployment. When the model deployment is complete, view the deployment. The Overview tab shows the basic deployment information. On the Test tab, you can test the model prediction. You can either enter test input data, or paste JSON input data, and click Predict. This shows that there’s a very high probability that the first customer will buy a tent, and a very high probability that the second customer will not buy a tent. On the Implementation tab, you’ll find the scoring end point for future reference. You’ll also find code snippets for various programming languages to utilize this deployment from your application. You can also view the API specification from here. And back in the project. you’ll find the AutoAI experiment and the model on the Assets tab, and the deployment on the Deployments tab. Find more videos in the IBM Watson Data and AI Learning Center. http://ibm.biz/ibm-machine-learning.
As a woman of color, I am statistically an
outlier in the tech field. In 2008, I was a young man, separated from the military, with little money and no clear path in front of me. In addition to all those things I started
to develop an addiction issue that was hindering my maturity and my rational decision-making. And all those things combined bought my ticket
on the next greyhound back to Baltimore. I refused to ask for help. But I kept thinking it was me. That I wasn’t committed enough and I wasn’t
trying hard enough. I began to believe that an immigrant from
the Dominican Republic, with only five years in the United States and a history of mediocre
public education could never compete with the well-educated, affluent students who
were set up as my competition. And I’m just going to start with a single
word: merit. Success is not vital. Failure is not fatal. It is the courage to continue that counts. That sums up my life, pretty much, in a nutshell. I had to reconsider my reasons why, and now
my reason why is simply because my life matters and my story is not over, it’s just starting
to unfold. I’m not only in a position to look out for
my own success and to just look out for myself, but I look out for three little kids that
call me their mentor, their big brother. The fact that I have the ability to put them
in a position to succeed. Oh, class 3, that is what defines me. NPower as a program gave me a reason to live
and made me want to give back to my community. The program gave me purpose and turned me
into a functioning citizen in society. It was also a form of therapy for me. My success in this program helped me to realize
why I was so depressed in the previous years of my life. I was so down and out because I had no direction. I had no purpose and felt disconnected from
the community around me. This can be a cold feeling. NPower gave me a second chance and a family. Today I get to reclaim a portion of my confidence
that was lost when I dropped out of college. Back then I didn’t know myself and didn’t
understand where my passion laid. But today I stand before you at this commencement
ceremony with complete confidence because I know where my heart, how my heart feels when I see meticulous codes spring forth beautiful artwork we call websites. Being provided the opportunity to perform
in work environments where minorities like myself, are underrepresented, has allowed
me to discover the leadership qualities I possess and my ability to produce results. And as a result I was able to, once again, make.. Excuse me. I’m a little emotional right now. Once again I was able to make my parents proud
of me. And I’m thankful and grateful for what NPower has done for my life and the lessons I’ve learned. And I’ll forever be loyal and use the tools
that were taught to me from NPower to make every decision for the future. Living on the edge, as I said, I needed extra
attention. And the NPower team sprang into action with
practical help for things like electricity, things like rent, food. It’s not enough to say thank you to these
people because I had no hope of things changing. I was doing everything I could, with all the
contacts I had, everyone i knew, to fight, fight through. Just like the quote that I started with: it’s
the courage to continue that counts. Never give up. Never give up. Each and every one of you, there is only greatness
from here and we’re destined because we all have merit. Remember your journey does not end here. The only thing you’re doing is you’re
getting a book and you’re getting a pen and you’re just writing the next chapter.
Articles, Blog
Rodolpho Zannin Feijó, Master of Science in Public Policy and Human Development, cohort 2015-2016
February 9, 2020
I’m working currently as Head of International Affairs for the city of Curitiba, a city with around 2 million inhabitants. We are responsible for setting the international strategy of the municipality. We’re also responsible for making cooperation agreements with international actors such as multilateral organisations and foreign
governments. One of my responsibilities is also to represent the city at international
events and international negotiations, such as the United Nations Economic Commission for Latin America and the Caribbean.