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GenAI – Training LLMs
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GenAI – Advanced Prompting Techniques
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GenAI – How Do You Optimize Cost For OpenAI Uses ?
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GenAI -Adapter Fusion Tuning.
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GenAI – Types Of Prompting Techniques
GenAI – Types Of Prompting Techniques. Table Of Contents: Zero Short Prompting. Few Short Prompting. Chain Of Thoughts Prompting. Role Prompting. System Prompting. Instruction Tuning. Self Consistency Prompting. (1) Zero Shot Prompting Example – 1 Example – 2 (2) Few Shot Prompting Example – 1 Example – 2 Example – 3 Example – 4 Example – 5 Example – 6 (3) Chain Of Thoughts Prompting Example – 1 Example – 2 Example – 3 Example – 4 Example – 5 How You Will Pass The Questins To The LLM In Chain Of Thoughts ? (4) Role Prompting You are a
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GenAI – What Is Hallucination In GenAI ?
GenAI – What Is Hallucination In GenAI ? Table Of Contents: What Is Hallucination In GenAI ? Examples Of GenAI Model Hallucination . Why Does Hallucination Happen ? How To Fix Hallucination ? (1) What Is Hallucination In GenAI ? (2) Examples Of GenAI Model Hallucination. (3) Why Does Hallucination Happen ? (4) How To Fix Hallucination In GenAI ?
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Linear Regression – Assumption – 3(How To Detect & Avoid Non Normal Distribution Of Error Term ?)
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How To Convert DataFrame To HDF File?
How To Convert DataFrame To HDF File? Table Of Contents: Syntax ‘to_hdf( )’ Method In Pandas. Examples ‘to_hdf( )’ Method. (1) Syntax: DataFrame.to_hdf(path_or_buf, key, mode=’a’, complevel=None, complib=None, append=False, format=None, index=True, min_itemsize=None, nan_rep=None, dropna=None, data_columns=None, errors=’strict’, encoding=’UTF-8′) Description: Write the contained data to an HDF5 file using HDFStore. Hierarchical Data Format (HDF) is self-describing, allowing an application to interpret the structure and contents of a file with no outside information. One HDF file can hold a mix of related objects which can be accessed as a group or as individual objects. In order to add another DataFrame or Series to an
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How To Find Non Missing Values In A DataFrame?
How To Find Non Missing Values In A DataFrame? Table Of Contents: Syntax ‘notna( )’ Method In Pandas. Examples ‘notna( )’ Method. (1) Syntax: DataFrame.notna() Description: Detect existing (non-missing) values. Return a boolean same-sized object indicating if the values are not NA. Non-missing values get mapped to True. Characters such as empty strings ” or numpy.inf are not considered NA values (unless you set pandas.options.mode.use_inf_as_na = True). NA values, such as None or numpy.NaN, get mapped to False values. Returns: DataFrame – Mask of bool values for each element in DataFrame that indicates whether an element is not an NA value. (2) Examples Of notna() Method: Example-1: df
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How To Drop Duplicate Rows From DataFrame?
How To Drop Duplicate Rows From DataFrame? Table Of Contents: Syntax ‘drop_duplicates( )’ Method In Pandas. Examples ‘drop_duplicates( )’ Method. (1) Syntax: DataFrame.drop_duplicates(subset=None, *, keep=’first’, inplace=False, ignore_index=False) Description: Return DataFrame with duplicate rows removed. Considering certain columns is optional. Indexes, including time indexes, are ignored. Parameters: subset: column label or sequence of labels, optional – Only consider certain columns for identifying duplicates, by default use all of the columns. keep: {‘first’, ‘last’, False}, default ‘first’ – Determines which duplicates (if any) to keep. – first : Drop duplicates except for the first occurrence. – last : Drop duplicates except for the last occurrence. –
