10 Valuable Artificial Intelligence Certifications for 2024 (analyticsinsight.net)
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Understanding Big Language Models:
1. DALL-E 2 (Open.AI)Topics where user can contribute:
Prompt Engineering Overview:
At the very basic we have interface to interact with a language model, where we pass some instruction and the model passes a response. The response is generated by the language model.
A prompt is composed with the following components:
Settings to keep in mind:
Designing prompts for Different Tasks:
Tasks Covered:
Tools & IDE's : Tools, libraries and platforms with different capabilities and functionalities include:
Example of LLMs with external tools:
Opportunities and Future Directions:
A token is ChatGPT is roughly 4 words.
Some notes on Recurrent Neural Network: A neural network which has a high hidden dimension state. When a new observations comes it updates its high hidden dimension state.
In machine learning there is lot of unity in principles to be applied to different data modalities. We use the same neural net architecture, gradients and adam optimizer to fine tune the gradients. For RNN we use some additional tools to reduce the variance of the gradients. For example: using CNN for image learning or Transformers to NLP problems. Years back in NLP for every tiny problem there was a different architecture.
Question : Where does vision stop and language begin
With Deep learning we are looking at a static problem with a probability distribution and applying the model to the distribution.
Back Propagation is useful algorithm and not go away, because it helps in finding a neural circuit subject to some constraints.
For Natural Language Modelling it is proven that very large datasets work because we are trying to predict the next word by broad strokes and surface level pattern. Once the language model becomes large, it understand the characters, spacing, punctuations, words, and finally the model learns the semantics and the facts.
Transformers is the most important advance in neural networks. Transformers is a combination of multiple ideas in which attention is one in which attention is a key. Transformers is designed in a way that it runs on a really fast GPU. It is not recurrent, thus it is shallow (less deep) and very easy to optimize.
After Transformers to built AGI, research is going on in Self Play and Active Learning.
GAN's don't have a mathematical cost function which it tries to optimize by gradient descent. Instead there is a game in which through mathematical functions it tries to find equilibrium.
Other example of deep learning models without cost function is reinforcement learning with self-play and surprise actions.
Double Descent:
When we make neural network larger it becomes better which is contrarian to statistical ideas. But there is a problem called the double descent bump as shown below;
Double descent occurs for all practical deep learning systems. Take a neural network and start increasing its size slowly while keeping the dataset size fixed. If you keep increasing the neural network size and don't do early stopping then, there is increase in performance and then it gets worse. It the point the model gets worst is precisely the point at which the model gets zero training error or zero training loss and then when we make it larger it start to get better again. It counter-intuitive because we expect the deep learning phenomenon to be monotonic.
The intuition is as follows:
"When we have a large data and a small model then small model is not sensitive to randomness/uncertainty in the training dataset. As the model gets large it achieves zero training error at approximately the point with the smallest norm in that subspace. At the point the dimensionality of the training data is equal to the dimensionality of the neural network model (one-to-one correspondence or degrees of freedom of dataset is same as degrees of freedom of model) at that point random fluctuation in the data worsens the performance (i.e. small changes in the data leads to noticeable changes in the model). But this double descent bump can be removed by regularization and early stopping."
If we have more data than parameters or more parameters than data, then model will be insensitive to the random changes in the dataset.
Overfitting: When model is very sensitive to small random unimportant stuff in the training dataset.
Early Stop: We train our model and monitor our performance and at some point when the validation performance starts to become worse we stop training (i.e. we determine to stop training and consider the model to be good enough)
ChatGPT:
ChatGPT has become a water-shed moment for organization because all companies are inherently language based companies. Whether it is text, video, audio, financial records all can be described as tokens which can be fed to large language models.
A good example of this is when during training of ChatGPT on amazon reviews, they found that after large amount of training the model became an excellent classifier of sentiment. So the model from predicting the next word (token) in a sentence, started to understand the semantics of the sentence and could tell if the review was a positive or negative.
With Advancement of AI, we have a likeness of a particular person as a separate bot, and the particular person will get a say, cut and licensing opportunities of his likeness.
All the material is for for getting certification for Google Universal Analytics or GA3, but the material will also help to prepare for GA4. Unfortunately GA4 is very new and very few people are using it.
Udemy:
https://www.udemy.com/share/101YUA3@1ZQpoeanMxxthiBi3TRUePtvhK8jpKedLNfathrLsI_5x8FtERy5aZusAp5R/
This one is excellent resource before the exam
https://www.udemy.com/share/1057WK3@B0vqy8cXKsPzaotyxGtf8OMJUbk6LabDRa9MvahhOqCaaXBprgawEPRvwRFK/
Google Material
https://skillshop.exceedlms.com/student/catalog/list?category_ids=6431-google-analytics-4
A Wald/Score chi-square test can be used for continuous and categorical variables. Whereas, Pearson chi-square is used for categorical variables. The p-value indicates whether a coefficient is significantly different from zero. In logistic regression, we can select top variables based on their high wald chi-square value.
Gain :Gain at a given decile level is the ratio of cumulative number of targets (events) up to that decile to the total number of targets (events) in the entire data set. This is also called CAP (Cumulative Accuracy Profile) in Finance, Credit Risk Scoring Technique
Interpretation: % of targets (events) covered at a given decile level. For example, 80% of targets covered in top 20% of data based on model. In the case of propensity to buy model, we can say we can identify and target 80% of customers who are likely to buy the product by just sending email to 20% of total customers.
Interpretation: The Cum Lift of 4.03 for top two deciles, means that when selecting 20% of the records based on the model, one can expect 4.03 times the total number of targets (events) found by randomly selecting 20%-of-file without a model.
| Decile Rank | Number of cases | Number of Responses | Cumulative Responses | % of Events | Gain | Cumulative Lift | Number of Decile Score to divide Gain |
| 1 | 2500 | 2179 | 2179 | 44.71% | 44.71% | 4.47% | 10 |
| 2 | 2500 | 1753 | 3932 | 35.97% | 80.67% | 4.03% | 20 |
| 3 | 2500 | 396 | 4328 | 8.12% | 88.80% | 2.96% | 30 |
| 4 | 2500 | 111 | 4439 | 2.28% | 91.08% | 2.28% | 40 |
| 5 | 2500 | 110 | 4549 | 2.26% | 93.33% | 1.87% | 50 |
| 6 | 2500 | 85 | 4634 | 1.74% | 95.08% | 1.58% | 60 |
| 7 | 2500 | 67 | 4701 | 1.37% | 96.45% | 1.38% | 70 |
| 8 | 2500 | 69 | 4770 | 1.42% | 97.87% | 1.22% | 80 |
| 9 | 2500 | 49 | 4819 | 1.01% | 98.87% | 1.10% | 90 |
| 10 | 2500 | 55 | 4874 | 1.13% | 100.00% | 1.00% | 100 |
| 25000 | 4874 |
Detecting Outliers
QR is interquartile range. It measures dispersion or variation. IQR = Q3 -Q1.Some researchers use 3 times of interquartile range instead of 1.5 as cutoff. If a high percentage of values are appearing as outliers when you use 1.5*IQR as cutoff, then you can use the following rule
Lower limit of acceptable range = Q1 - 1.5* (Q3-Q1)
Upper limit of acceptable range = Q3 + 1.5* (Q3-Q1)
Lower limit of acceptable range = Q1 - 3* (Q3-Q1)
Upper limit of acceptable range = Q3 + 3* (Q3-Q1)
Acceptable Range : The mean plus or minus three Standard Deviation
4. Weight of Evidence: Logistic regression model is one of the most commonly used statistical technique for solving binary classification problem. It is an acceptable technique in almost all the domains. These two concepts - weight of evidence (WOE) and information value (IV) evolved from the same logistic regression technique. These two terms have been in existence in credit scoring world for more than 4-5 decades. They have been used as a benchmark to screen variables in the credit risk modeling projects such as probability of default. They help to explore data and screen variables. It is also used in marketing analytics project such as customer attrition model, campaign response model etc.
The weight of evidence tells the predictive power of an independent variable in relation to the dependent variable. Since it evolved from credit scoring world, it is generally described as a measure of the separation of good and bad customers. "Bad Customers" refers to the customers who defaulted on a loan. and "Good Customers" refers to the customers who paid back loan.
Distribution of Goods - % of Good Customers in a particular group
Distribution of Bads - % of Bad Customers in a particular groupln - Natural LogPositive WOE means Distribution of Goods > Distribution of Bads
Negative WOE means Distribution of Goods < Distribution of BadsHint : Log of a number > 1 means positive value. If less than 1, it means negative value.
Logistic regression model is one of the most commonly used statistical technique for solving binary classification problem. It is an acceptable technique in almost all the domains. These two concepts - weight of evidence (WOE) and information value (IV) evolved from the same logistic regression technique. These two terms have been in existence in credit scoring world for more than 4-5 decades. They have been used as a benchmark to screen variables in the credit risk modeling projects such as probability of default. They help to explore data and screen variables. It is also used in marketing analytics project such as customer attrition model, campaign response model etc.
 |
| WOE Calculation |
Distribution of Goods - % of Good Customers in a particular group
Distribution of Bads - % of Bad Customers in a particular group
ln - Natural Log
Hint : Log of a number > 1 means positive value. If less than 1, it means negative value.
WOE = In(% of non-events ➗ % of events)
 |
| Weight of Evidence Formula |
 |
| Weight of Evidence and Information Value Calculation |
Logistic regression model is one of the most commonly used statistical technique for solving binary classification problem. It is an acceptable technique in almost all the domains. These two concepts - weight of evidence (WOE) and information value (IV) evolved from the same logistic regression technique. These two terms have been in existence in credit scoring world for more than 4-5 decades. They have been used as a benchmark to screen variables in the credit risk modeling projects such as probability of default. They help to explore data and screen variables. It is also used in marketing analytics project such as customer attrition model, campaign response model etc.
|
| WOE Calculation |
Distribution of Goods - % of Good Customers in a particular group
Distribution of Bads - % of Bad Customers in a particular group
ln - Natural Log
Hint : Log of a number > 1 means positive value. If less than 1, it means negative value.
WOE = In(% of non-events ➗ % of events)
|
| Weight of Evidence Formula |
|
| Weight of Evidence and Information Value Calculation |
Create 10/20 bins/groups for a continuous independent variable and then calculates WOE and IV of the variable2. Coarse Classing
Combine adjacent categories with similar WOE scores
Categorical independent variables: Combine categories with similar WOE and then create new categories of an independent variable with continuous WOE values. In other words, use WOE values rather than raw categories in your model. The transformed variable will be a continuous variable with WOE values. It is same as any continuous variable.
It is because the categories with similar WOE have almost same proportion of events and non-events. In other words, the behavior of both the categories is same.Why combine categories with similar WOE?
Perform shuffling of predictors' values and join them with the original predictors and then build random forest on the merged dataset. Then make comparison of original variables with the randomised variables to measure variable importance. Only variables having higher importance than that of the randomised variables are considered important.
Major Disadvantages: Boruta does not treat collinearity while selecting important variables. It is because of the way algorithm works.
If a variable has a very low rank for Spearman (coefficient - close to 0) and a very high rank for Hoeffding indicates a non-monotonic relationship.
If a variable has a very low rank for Pearson (coefficient - close to 0) and a very high rank for Hoeffding indicates a non-linear relationship.
