PROFESSOR RAM.A.DAYINABOYINA ................Dept of CSE/AIML

 

PROFESSOR ram.a.dayinaboyina ......................

 

SPLIT, REVERSE SPLIT.........ex infibeam.........

 consolidation (also called a reverse stock split) is a corporate action where a company combines multiple existing shares into fewer shares with a higher face value.

How It Works

• Reduction in Quantity: Your total number of shares goes down.

• Increase in Face Value: The face value (nominal value) of each share increases in the same proportion.

• Same Total Value: Your total investment value and ownership percentage remain unchanged right after the consolidation.

• Example (1:4 Ratio): If you own 8,000 shares worth ₹15 each (total ₹1,20,000), a 1:4 consolidation gives you 2,000 shares worth ₹60 each (total ₹1,20,000).

Why Companies Do It

• Boost Share Price: To raise a very low trading price and make the stock look more marketable.

• Meet Listing Rules: To comply with minimum stock exchange price or listing requirements.

• Reduce Speculation: To lower the volatility linked to ultra-low-priced penny stocks.

Key Effects for Investors

• Fractional Shares: If your share count does not divide evenly by the ratio, you may end up with fractional shares, which companies typically settle through cash payouts or a trustee.

• No Fundamental Change: Consolidation does not alter the underlying business value, profits, or market capitalization of the company by itself.

For official regulatory procedures and guidelines on corporate restructuring, check the Companies Act Procedures or review market resources via Zerodha Console.

If you have a specific stock in mind, tell me the consolidation ratio or your current holding quantity, and I can help you calculate your new share count.

machine learning and deep learning...........................

 .......................Machine learning is a subset of artificial intelligence where computers learn patterns from data, and deep learning is a specialized subset of machine learning that uses multi-layer neural networks to extract features automatically.

Key Differences

• Relationship: Deep learning is a subset of machine learning, which is a subset of artificial intelligence.

• Feature Extraction: Machine learning requires humans to manually select and extract features from data; deep learning automatically learns representations and features from raw, unstructured data.

• Data Requirements: Machine learning can work well with small to medium, structured datasets; deep learning requires massive amounts of data (often unstructured like images, audio, and text).

• Hardware & Compute: Machine learning runs easily on standard CPUs and requires less computing power; deep learning needs heavy computational power, usually relying on GPUs or TPUs.

• Interpretability: Machine learning models (such as decision trees or linear regression) are relatively easy to interpret and explain ("white box"); deep learning models function as complex "black boxes" that are difficult to interpret.

• Training Time: Machine learning models train relatively fast; deep learning models take a long time to train due to the depth of the neural networks.

When to Use Which

• Choose Machine Learning when:

• Your data is structured, numeric, or limited in size.

• You need high model transparency and interpretability (such as in healthcare or finance).

• You have limited hardware or computing resources.

• Choose Deep Learning when:

• You are working with large, complex, unstructured datasets (images, speech, natural language).

• You have access to powerful hardware like GPUs or TPUs.

• You need end-to-end pattern recognition without manually engineering features.

telugu maduryam...................

 అరె నీ బుగ్గల్లోన ఏమున్నదో

బాదం హల్వా ముక్క దాగున్నదో అరె నీ చూపుల్లోన ఏమున్నదో పైటని తప్పించేసే పవరున్నదో గుస గుసలాడేయ్ మంది సన్న రైక సొగసుగా పట్టె మంచం బెదురుకుంటే చాప, దిండు ఉందిగా వాళుతున్నవి కళ్ళు, అరె ఊగుతున్నది ఒళ్ళు పైన వెన్నెల జల్లు అరె లోన పుట్టెను జిల్లు కసిగా కవ్విస్తుంది నడుము నీ నడుము ఉసిగా పట్టాలంటే టైము ఇది టైము ఝలకు ఝలకు సిలుకు చీర ఝలకు ఝలకు ఛమకు ఛమకు జిలుగు రైక ఛమకు ఛమకు చల్లగాలి వీస్తుంటే చలి కాచుకో నన్నే దుప్పటిగా పెనవేసుకో ఒడిలో నువ్వుంటే చలి ఏవిటే జతగా ఒకటైతే మతిపోదటే మల్లెపూలు నలిగాయంటే మూడో నెల్లో వేడుకే సత్తావున్న మగాడిచ్చే ముచ్చటైన కానుకే వేడెక్కిపోతుంది పరుపు దాన్ని ఓడించ మంటుంది వలపు కానివ్వు అందాల కొలువు నేడు కన్నెతనానికి సెలవు కసిగా కవ్విస్తుంది నడుము నీ నడుము ఉసిగా పట్టాలంటే టైము ఇది టైము

infosys 5%, target 1060 2.42 pm ........tgt rch ........next 1100 friday little back due to profit book ............ kpit 15% by 31. oct 600 /- tgt increase.............

 

My master's thesis (non ML, coding theory for an university) was 90 first ,100 pages for second ..............for Phd it was 220 pages.....

 89 pages of pdf

• 75 pages from first chapter to appendix notation list

Those are used in the following way:

• 14 pages of math fundamentals (finite field extensions, linearized polynomials, ore extensions, orthogonality defect)

• 19 pages of coding theory fundamentals (RS codes and Gabidulin codes)

• 13 pages of literary review or algorithms overview that I need for comparisson (solve the same problem) or analysis (make the complexity analysis simpler later on)

• 14 pages of primary result (transformation of an algorithm to a new problem, proof or correctness, complexity analysis)

• 3 pages of application of the new algorithm to the chosen domain and its consequences

• 2 pages review of results and conclusion

• 2 pages of introduction (overview of the thesis structure and weak motivation)

You can see that the actual research part takes up little space (~15-20 pages) but all the basics that make it understandable or easy to argue take up a lot of space in this case.