u/Negative_War_65

Hello Folks,

When I started teaching my free online lectures, on Machine Learning, the intent was to help learners understand the topics of Machine Learning in a simple and digestible manner.

When I was a first time learner, started my grad program, books as Probabilistic Machine Learning by Murphy, Bishop, were told to us as excellent text books for Machine Learning, yet seemed always very difficult to read and understand.

To work around that, I started making content based on these foundational textbooks. We covered Introductory concepts, Probabilities and Statistics.

Slowly I started understanding, that the difficulty is faced not just by me, but all the learners. Hence the need.

I do hope that learners will see the importance of core foundational concepts, which are the pillars for modern machine learning, and Probabilistic Machine Learning is that core pillar, without which ML always seemed to me to be some blackbox.

Link: https://youtube.com/@aayushsugandh4036

Code Implementations, explanation of concepts for my Probabilistic Machine Learning Series.

Hello folks,

In this new coding demonstration, we code, and explain the concepts pertaining to:

1.Overfitting, Population Risk & Generalisation Gap.

2. Proxy for Population Risks : Test Set.

3. The No free Lunch Theorem and Inductive Biases.

4. Unsupervised Learning : Density Estimation and Clustering.

5. VAEs(Variational Autoencoder)- Latent factors concepts explained, and VAE architecture explained and coded.

6. Self-Supervised Learning-Masked Predictions.

7.Density Evaluation and Sample Efficiency.

8. Reinforcement Learning Primer : Multi-Armed Bandits.

Implementation Link: https://youtu.be/gbz8smggmRM?si=vR4OIPLfGRHFJ95F

Code Implementations, explanation of concepts for my Probabilistic Machine Learning Series.

Hello folks,

In this new coding demonstration, we code, and explain the concepts pertaining to:

1.Overfitting, Population Risk & Generalisation Gap.

2. Proxy for Population Risks : Test Set.

3. The No free Lunch Theorem and Inductive Biases.

4. Unsupervised Learning : Density Estimation and Clustering.

5. VAEs(Variational Autoencoder)- Latent factors concepts explained, and VAE architecture explained and coded.

6. Self-Supervised Learning-Masked Predictions.

7.Density Evaluation and Sample Efficiency.

8. Reinforcement Learning Primer : Multi-Armed Bandits.

Implementation Link: https://youtu.be/gbz8smggmRM?si=vR4OIPLfGRHFJ95F

Code Implementations, explanation of concepts for my Probabilistic Machine Learning Series.

Hello folks,

In this new coding demonstration, we code, and explain the concepts pertaining to:

1.Overfitting, Population Risk & Generalisation Gap.

2. Proxy for Population Risks : Test Set.

3. The No free Lunch Theorem and Inductive Biases.

4. Unsupervised Learning : Density Estimation and Clustering.

5. VAEs(Variational Autoencoder)- Latent factors concepts explained, and VAE architecture explained and coded.

6. Self-Supervised Learning-Masked Predictions.

7.Density Evaluation and Sample Efficiency.

8. Reinforcement Learning Primer : Multi-Armed Bandits.

Implementation Link: https://youtu.be/gbz8smggmRM?si=vR4OIPLfGRHFJ95F

Code Implementations, explanation of concepts for my Probabilistic Machine Learning Series.

Hello folks,

In this new coding demonstration, we code, and explain the concepts pertaining to:

1.Overfitting, Population Risk & Generalisation Gap.

2. Proxy for Population Risks : Test Set.

3. The No free Lunch Theorem and Inductive Biases.

4. Unsupervised Learning : Density Estimation and Clustering.

5. VAEs(Variational Autoencoder)- Latent factors concepts explained, and VAE architecture explained and coded.

6. Self-Supervised Learning-Masked Predictions.

7.Density Evaluation and Sample Efficiency.

8. Reinforcement Learning Primer : Multi-Armed Bandits.

Implementation Link: https://youtu.be/gbz8smggmRM?si=vR4OIPLfGRHFJ95F

Coding Machine Learning Lecture 2:

Hello folks, in this code implementation, we walk through not just writing code, but understanding the outputs we obtain, and validating the results in mathematics of Machine Learning.

For instance the equivalence of the results of Negative Log likelihood and Mean squared error for gaussian distribution assumptions, makes us feel the beauty behind theory and practice.

We cover L1 and L2 loss curves, The Gaussian Output distribution modelling uncertainty, equivalence of Negative Log likelihood and Mean squared error for that output distribution specifically.

Then, analyzing linear regression, and the convex bowl shaped loss curves, explaining underfitting and overfitting ideas via Polynomial Regression, followed by the need for automatic learning of features through coding a deep neural network.

You will see ideas taught in my Lecture 2 of probabilistic Machine Learning, turn into practice.

Link to Code Implementation: https://youtu.be/6ZTVp70Mf5s?si=2lThR6LOdzLimB1v

Link to Theory Lecture : https://youtu.be/iThI5AapBc0?si=AS-UCi1ar9-yPpg8

Coding Machine Learning Lecture 2:

Hello folks, in this code implementation, we walk through not just writing code, but understanding the outputs we obtain, and validating the results in mathematics of Machine Learning.

For instance the equivalence of the results of Negative Log likelihood and Mean squared error for gaussian distribution assumptions, makes us feel the beauty behind theory and practice.

We cover L1 and L2 loss curves, The Gaussian Output distribution modelling uncertainty, equivalence of Negative Log likelihood and Mean squared error for that output distribution specifically.

Then, analyzing linear regression, and the convex bowl shaped loss curves, explaining underfitting and overfitting ideas via Polynomial Regression, followed by the need for automatic learning of features through coding a deep neural network.

You will see ideas taught in my Lecture 2 of probabilistic Machine Learning, turn into practice.

Link to Code Implementation: https://youtu.be/6ZTVp70Mf5s?si=2lThR6LOdzLimB1v

Link to Theory Lecture : https://youtu.be/iThI5AapBc0?si=AS-UCi1ar9-yPpg8

Hello folks, in this code implementation, we walk through not just writing code, but understanding the outputs we obtain, and validating the results in mathematics of Machine Learning.

For instance the equivalence of the results of Negative Log likelihood and Mean squared error for gaussian distribution assumptions, makes us feel the beauty behind theory and practice.

We cover L1 and L2 loss curves, The Gaussian Output distribution modelling uncertainty, equivalence of Negative Log likelihood and Mean squared error for that output distribution specifically.

Then, analyzing linear regression, and the convex bowl shaped loss curves, explaining underfitting and overfitting ideas via Polynomial Regression, followed by the need for automatic learning of features through coding a deep neural network.

You will see ideas taught in my Lecture 2 of probabilistic Machine Learning, turn into practice.

Link to Code Implementation: https://youtu.be/6ZTVp70Mf5s?si=2lThR6LOdzLimB1v

Link to Theory Lecture : https://youtu.be/iThI5AapBc0?si=AS-UCi1ar9-yPpg8

Hello folks, I am Aayush, and in this code implementation, we walk through not just writing code, but understanding the outputs we obtain, and validating the results in mathematics of Machine Learning.

For instance the equivalence of the results of Negative Log likelihood and Mean squared error for gaussian distribution assumptions, makes us feel the beauty behind theory and practice.

We cover L1 and L2 loss curves, The Gaussian Output distribution modelling uncertainty, equivalence of Negative Log likelihood and Mean squared error for that output distribution specifically.

Then, analyzing linear regression, and the convex bowl shaped loss curves, explaining underfitting and overfitting ideas via Polynomial Regression, followed by the need for automatic learning of features through coding a deep neural network.

You will see ideas taught in my Lecture 2 of probabilistic Machine Learning, turn into practice.

Link to Code Implementation: https://youtu.be/6ZTVp70Mf5s?si=2lThR6LOdzLimB1v

Link to Theory Lecture : https://youtu.be/iThI5AapBc0?si=AS-UCi1ar9-yPpg8

Coding Machine Learning.

Hello Folks, here I present the first coding demonstration lecture, based on my 1st lecture on Probabilistic Machine Learning.

Here I write the code from scratch, discuss and analyze the results, which were covered in details in the whiteboard classes.

What we cover?
-Random Variables, and validating law of large numbers.
-Visualizing a dataset
-Doing an EDA on Iris dataset and understanding the correlation among features.
-Classifier basics
-Empirical Risk Minimization and Generalization.
-Epistemic and Aleatoric Uncertainties.
-Softmax Function and LogSumExp Trick to avoid overflow issues
-Linear Models
-Maximum Likelihood Estimation.
-Simple end to end ML pipeline Function.

While writing the code, my intent is to ensure that concepts are understood with crystal clarity. These code demonstrations are specific to my theory ML lectures, and link is attached.

Theory-Intuition-Code

Implementation Link : https://youtu.be/X_yOlx8Zp4g?si=kh8_tzzndr8609u4

Theory Lecture Link : https://youtu.be/kMkCOrp8te8?si=q7kWr-1qK515bhob

Coding Machine Learning.

Hello Folks, here I present the first coding demonstration lecture, based on my 1st lecture on Probabilistic Machine Learning.

Here I write the code from scratch, discuss and analyze the results, which were covered in details in the whiteboard classes.

What we cover?
-Random Variables, and validating law of large numbers.
-Visualizing a dataset
-Doing an EDA on Iris dataset and understanding the correlation among features.
-Classifier basics
-Empirical Risk Minimization and Generalization.
-Epistemic and Aleatoric Uncertainties.
-Softmax Function and LogSumExp Trick to avoid overflow issues
-Linear Models
-Maximum Likelihood Estimation.
-Simple end to end ML pipeline Function.

While writing the code, my intent is to ensure that concepts are understood with crystal clarity. These code demonstrations are specific to my theory ML lectures, and link is attached.

Theory-Intuition-Code

Implementation Link : https://youtu.be/X_yOlx8Zp4g?si=kh8_tzzndr8609u4

Theory Lecture Link : https://youtu.be/kMkCOrp8te8?si=q7kWr-1qK515bhob

Coding Machine Learning.

Hello Folks, here I present the first coding demonstration lecture, based on my 1st lecture on Probabilistic Machine Learning.

Here I write the code from scratch, discuss and analyze the results, which were covered in details in the whiteboard classes.

What we cover?
-Random Variables, and validating law of large numbers.
-Visualizing a dataset
-Doing an EDA on Iris dataset and understanding the correlation among features.
-Classifier basics
-Empirical Risk Minimization and Generalization.
-Epistemic and Aleatoric Uncertainties.
-Softmax Function and LogSumExp Trick to avoid overflow issues
-Linear Models
-Maximum Likelihood Estimation.
-Simple end to end ML pipeline Function.

While writing the code, my intent is to ensure that concepts are understood with crystal clarity. These code demonstrations are specific to my theory ML lectures, and link is attached.

Theory-Intuition-Code

Implementation Link : https://youtu.be/X_yOlx8Zp4g?si=kh8_tzzndr8609u4

Theory Lecture Link : https://youtu.be/kMkCOrp8te8?si=q7kWr-1qK515bhob

Coding Machine Learning.

Hello Folks, here I present the first coding demonstration lecture, based on my 1st lecture on Probabilistic Machine Learning.

Here I write the code from scratch, discuss and analyze the results, which were covered in details in the whiteboard classes.

What we cover?
-Random Variables, and validating law of large numbers.
-Visualizing a dataset
-Doing an EDA on Iris dataset and understanding the correlation among features.
-Classifier basics
-Empirical Risk Minimization and Generalization.
-Epistemic and Aleatoric Uncertainties.
-Softmax Function and LogSumExp Trick to avoid overflow issues
-Linear Models
-Maximum Likelihood Estimation.
-Simple end to end ML pipeline Function.

While writing the code, my intent is to ensure that concepts are understood with crystal clarity. These code demonstrations are specific to my theory ML lectures, and link is attached.

Theory-Intuition-Code

Implementation Link : https://youtu.be/X_yOlx8Zp4g?si=kh8_tzzndr8609u4

Theory Lecture Link : https://youtu.be/kMkCOrp8te8?si=q7kWr-1qK515bhob

Coding Machine Learning.

Hello Folks, here I present the first coding demonstration lecture, based on my 1st lecture on Probabilistic Machine Learning.

Here I write the code from scratch, discuss and analyze the results, which were covered in details in the whiteboard classes.

What we cover?
-Random Variables, and validating law of large numbers.
-Visualizing a dataset
-Doing an EDA on Iris dataset and understanding the correlation among features.
-Classifier basics
-Empirical Risk Minimization and Generalization.
-Epistemic and Aleatoric Uncertainties.
-Softmax Function and LogSumExp Trick to avoid overflow issues
-Linear Models
-Maximum Likelihood Estimation.
-Simple end to end ML pipeline Function.

While writing the code, my intent is to ensure that concepts are understood with crystal clarity. These code demonstrations are specific to my theory ML lectures, and link is attached.

Theory-Intuition-Code

Implementation Link : https://youtu.be/X_yOlx8Zp4g?si=kh8_tzzndr8609u4

Theory Lecture Link : https://youtu.be/kMkCOrp8te8?si=q7kWr-1qK515bhob

Coding Machine Learning.

Hello Folks, here I present the first coding demonstration lecture, based on my 1st lecture on Probabilistic Machine Learning.

Here I write the code from scratch, discuss and analyze the results, which were covered in details in the whiteboard classes.

What we cover?
-Random Variables, and validating law of large numbers.
-Visualizing a dataset
-Doing an EDA on Iris dataset and understanding the correlation among features.
-Classifier basics
-Empirical Risk Minimization and Generalization.
-Epistemic and Aleatoric Uncertainties.
-Softmax Function and LogSumExp Trick to avoid overflow issues
-Linear Models
-Maximum Likelihood Estimation.
-Simple end to end ML pipeline Function.

While writing the code, my intent is to ensure that concepts are understood with crystal clarity. These code demonstrations are specific to my theory ML lectures, and link is attached.

Theory-Intuition-Code

Implementation Link : https://youtu.be/X_yOlx8Zp4g?si=kh8_tzzndr8609u4

Theory Lecture Link : https://youtu.be/kMkCOrp8te8?si=q7kWr-1qK515bhob

Coding Machine Learning.

Hello Folks, here I present the first coding demonstration lecture, based on my 1st lecture on Probabilistic Machine Learning.

Here I write the code from scratch, discuss and analyze the results, which were covered in details in the whiteboard classes.

What we cover?
-Random Variables, and validating law of large numbers.
-Visualizing a dataset
-Doing an EDA on Iris dataset and understanding the correlation among features.
-Classifier basics
-Empirical Risk Minimization and Generalization.
-Epistemic and Aleatoric Uncertainties.
-Softmax Function and LogSumExp Trick to avoid overflow issues
-Linear Models
-Maximum Likelihood Estimation.
-Simple end to end ML pipeline Function.

While writing the code, my intent is to ensure that concepts are understood with crystal clarity. These code demonstrations are specific to my theory ML lectures, and link is attached.

Theory-Intuition-Code

Implementation Link : https://youtu.be/X_yOlx8Zp4g?si=kh8_tzzndr8609u4

Theory Lecture Link : https://youtu.be/kMkCOrp8te8?si=q7kWr-1qK515bhob

Coding Machine Learning.

Hello Folks, here I present the first coding demonstration lecture, based on my 1st lecture on Probabilistic Machine Learning.

Here I write the code from scratch, discuss and analyze the results, which were covered in details in the whiteboard classes.

What we cover?
-Random Variables, and validating law of large numbers.
-Visualizing a dataset
-Doing an EDA on Iris dataset and understanding the correlation among features.
-Classifier basics
-Empirical Risk Minimization and Generalization.
-Epistemic and Aleatoric Uncertainties.
-Softmax Function and LogSumExp Trick to avoid overflow issues
-Linear Models
-Maximum Likelihood Estimation.
-Simple end to end ML pipeline Function.

While writing the code, my intent is to ensure that concepts are understood with crystal clarity. These code demonstrations are specific to my theory ML lectures, and link is attached.

Theory-Intuition-Code

Implementation Link : https://youtu.be/X_yOlx8Zp4g?si=kh8_tzzndr8609u4

Theory Lecture Link : https://youtu.be/kMkCOrp8te8?si=q7kWr-1qK515bhob

Coding Machine Learning.

Hello Folks, here I present the first coding demonstration lecture, based on my 1st lecture on Probabilistic Machine Learning.

Here I write the code from scratch, discuss and analyze the results, which were covered in details in the whiteboard classes.

What we cover?
-Random Variables, and validating law of large numbers.
-Visualizing a dataset
-Doing an EDA on Iris dataset and understanding the correlation among features.
-Classifier basics
-Empirical Risk Minimization and Generalization.
-Epistemic and Aleatoric Uncertainties.
-Softmax Function and LogSumExp Trick to avoid overflow issues
-Linear Models
-Maximum Likelihood Estimation.
-Simple end to end ML pipeline Function.

While writing the code, my intent is to ensure that concepts are understood with crystal clarity. These code demonstrations are specific to my theory ML lectures, and link is attached.

Theory-Intuition-Code

Implementation Link : https://youtu.be/X_yOlx8Zp4g?si=kh8_tzzndr8609u4

Theory Lecture Link : https://youtu.be/kMkCOrp8te8?si=q7kWr-1qK515bhob

Code implementations for my free Probabilistic Machine Learning lectures.

Hello folks,

I made 14 long form lectures of my Probabilistic Machine Learning series, covering important mathematical foundations(and still an ongoing process) covering around 16+ hours of content on Probabilistic Machine learning, Probability Univariate and Multivariate foundations in detail, and Statistics.

Now I have also started working on code implementations for them. I was happy to see some nice looking results taking concepts of my lecture 1, and using those important concepts to code practical stuff.

I will be adding code, uploading it in my github repo in the coming future, and also try doing a video of code implementation walkthrough, where I code and explain those concepts.

I feel that showing code implementations would make learners feel more confident of what they see in the whiteboard being implemented in practice!

If you have not watched my lectures, you can in the link shared, and I do hope that adding code will make the learning process, more fun, more rewarding!

I will try to upload one code implementation every weekend.

Happy learning.

Link: https://youtube.com/playlist?list=PLDPxj3tOc5TNi6MktTHUZid-yf9nIBxSh&si=u3kyvZnXMs4llC3P