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Biblioteca

MARC Lista (tabellare)
Artificial intelligence, machine learning, and deep learning / / Oswald Campesato
Artificial intelligence, machine learning, and deep learning / / Oswald Campesato
Autore Campesato Oswald
Pubbl/distr/stampa Dulles, Virginia ; ; Boston, Massachusetts ; ; New Delhi : , : Mercury Learning and Information, , [2020]
Descrizione fisica 1 online resource (338 pages)
Disciplina 006.3
Soggetto topico Artificial intelligence
Machine learning
Soggetto genere / forma Electronic books.
ISBN 1-68392-465-7
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Record Nr. UNINA-9910467240303321
Campesato Oswald  
Dulles, Virginia ; ; Boston, Massachusetts ; ; New Delhi : , : Mercury Learning and Information, , [2020]
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Artificial intelligence, machine learning, and deep learning / / Oswald Campesato
Artificial intelligence, machine learning, and deep learning / / Oswald Campesato
Autore Campesato Oswald
Pubbl/distr/stampa Dulles, Virginia ; ; Boston, Massachusetts ; ; New Delhi : , : Mercury Learning and Information, , [2020]
Descrizione fisica 1 online resource (338 pages)
Disciplina 006.3
Soggetto topico Artificial intelligence
Machine learning
ISBN 1-68392-465-7
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Record Nr. UNINA-9910793935203321
Campesato Oswald  
Dulles, Virginia ; ; Boston, Massachusetts ; ; New Delhi : , : Mercury Learning and Information, , [2020]
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Artificial intelligence, machine learning, and deep learning / / Oswald Campesato
Artificial intelligence, machine learning, and deep learning / / Oswald Campesato
Autore Campesato Oswald
Pubbl/distr/stampa Dulles, Virginia ; ; Boston, Massachusetts ; ; New Delhi : , : Mercury Learning and Information, , [2020]
Descrizione fisica 1 online resource (338 pages)
Disciplina 006.3
Soggetto topico Artificial intelligence
Machine learning
ISBN 1-68392-465-7
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Record Nr. UNINA-9910809066003321
Campesato Oswald  
Dulles, Virginia ; ; Boston, Massachusetts ; ; New Delhi : , : Mercury Learning and Information, , [2020]
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
D3 / / Oswald Campesato
D3 / / Oswald Campesato
Autore Campesato Oswald
Pubbl/distr/stampa Dulles, Virginia ; ; Boston, Massachusetts ; ; New Delhi, [India] : , : Mercury Learning & Information, , 2016
Descrizione fisica 1 online resource (300 pages) : illustrations
Disciplina 006.312
Collana Pocket Primer
Soggetto topico Information visualization - Computer programs
Computer graphics - Computer programs
ISBN 1-942270-69-0
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Record Nr. UNINA-9910795935003321
Campesato Oswald  
Dulles, Virginia ; ; Boston, Massachusetts ; ; New Delhi, [India] : , : Mercury Learning & Information, , 2016
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
D3 / / Oswald Campesato
D3 / / Oswald Campesato
Autore Campesato Oswald
Pubbl/distr/stampa Dulles, Virginia ; ; Boston, Massachusetts ; ; New Delhi, [India] : , : Mercury Learning & Information, , 2016
Descrizione fisica 1 online resource (300 pages) : illustrations
Disciplina 006.312
Collana Pocket Primer
Soggetto topico Information visualization - Computer programs
Computer graphics - Computer programs
ISBN 1-942270-69-0
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Record Nr. UNINA-9910829000503321
Campesato Oswald  
Dulles, Virginia ; ; Boston, Massachusetts ; ; New Delhi, [India] : , : Mercury Learning & Information, , 2016
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
D3 : Data Driven Documents
D3 : Data Driven Documents
Autore Campesato Oswald
Pubbl/distr/stampa Bloomfield, : Mercury Learning & Information
ISBN 1-942270-69-0
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Record Nr. UNINA-9910466464703321
Campesato Oswald  
Bloomfield, : Mercury Learning & Information
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Data Structures in Java
Data Structures in Java
Autore Campesato Oswald
Edizione [1st ed.]
Pubbl/distr/stampa Bloomfield : , : Mercury Learning & Information, , 2023
Descrizione fisica 1 online resource (248 pages)
Disciplina 005.133
Soggetto topico Data structures (Computer science)
Java (Computer program language)
COMPUTERS / Programming Languages / Java
Soggetto non controllato XOR
arrays
business
combinatorics
computer science
data analysis
queues
recursion
stacks
strings
ISBN 1-68392-953-5
1-68392-954-3
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Cover -- Title Page -- Copyright -- Dedication -- Contents -- Preface -- Chapter 1: Introduction to Java -- A Very Brief Introduction to Java -- Downloading a Java Release (Short Version) -- Selecting a Version of Java (Detailed Version) -- Java 8 and Java 11 -- Java Version Numbers -- JRE Versus a JDK -- Java Distributions -- Java IDEs -- Data Types, Operators, and Their Precedence -- Java Comments -- Java Operators -- Creating and Compiling Java Classes -- "Hello World" and Working With Numbers -- The Java String Class -- Java Strings With Metacharacters -- The Java New Operator -- Equality of Strings -- Comparing Strings -- Searching for a Substring in Java -- Useful String Methods in Java -- Parsing Strings in Java -- Conditional Logic in Java -- Determining Leap Years -- Finding the Divisors of a Number -- Checking for Palindromes -- Working With Arrays of Strings -- Working With the StringBuilder Class -- Static Methods in Java -- Other Static Types in Java -- Summary -- Chapter 2: Recursion and Combinatorics -- What Is Recursion? -- Arithmetic Series -- Calculating Arithmetic Series (Iterative) -- Calculating Arithmetic Series (Recursive) -- Calculating Partial Arithmetic Series -- Geometric Series -- Calculating a Geometric Series (Iterative) -- Calculating Geometric Series (Recursive) -- Factorial Values -- Calculating Factorial Values (Iterative) -- Calculating Factorial Values (Recursive) -- Calculating Factorial Values (Tail Recursion) -- Fibonacci Numbers -- Calculating Fibonacci Numbers (Recursive) -- Calculating Fibonacci Numbers (Iterative) -- Task: Reverse a String via Recursion -- Task: Check for Balanced Parentheses -- Task: Calculate the Number of Digits -- Task: Determine if a Positive Integer is Prime -- Task: Find the Prime Divisors of a Positive Integer -- Task: Goldbach's Conjecture.
Task: Calculate the GCD (Greatest Common Divisor) -- Task: Calculate the LCM (Lowest Common Multiple) -- What Is Combinatorics? -- Working With Permutations -- Working With Combinations -- The Number of Subsets of a Finite Set -- Task: Subsets Containing a Value Larger Than k -- Summary -- Chapter 3: Strings and Arrays -- Time and Space Complexity -- Task: Maximum and Minimum Powers of an Integer -- Task: Binary Substrings of a Number -- Task: Common Substring of Two Binary Numbers -- Task: Multiply and Divide via Recursion -- Task: Sum of Prime and Composite Numbers -- Task: Count Word Frequencies -- Task: Check if a String Contains Unique Characters -- Task: Insert Characters in a String -- Task: String Permutations -- Task: Check for Palindromes -- Task: Check for Longest Palindrome -- Working With Sequences of Strings -- The Maximum Length of a Repeated Character in a String -- Find a Given Sequence of Characters in a String -- Task: Longest Sequences of Substrings -- The Longest Sequence of Unique Characters -- The Longest Repeated Substring -- Working With 1D Arrays -- Rotate an Array -- Task: Invert Adjacent Array Elements -- Task: Shift Nonzero Elements Leftward -- Task: Sort Array In-Place in O(n) Without a Sort Function -- Task: Generate 0 That Is Three Times More Likely Than a 1 -- Task: Invert Bits in Even and Odd Positions -- Task: Check for Adjacent Set Bits in a Binary Number -- Task: Count Bits in a Range of Numbers -- Task: Find the Right-Most Set Bit in a Number -- Task: The Number of Operations to Make All Characters Equal -- Task: Compute XOR without XOR for Two Binary Numbers -- Task: Swap Adjacent Bits in Two Binary Numbers -- Working With 2D Arrays -- The Transpose of a Matrix -- Summary -- Chapter 4: Search and Sort Algorithms -- Search Algorithms -- Linear Search -- Binary Search Walk-Through.
Binary Search (Iterative Solution) -- Binary Search (Recursive Solution) -- Well-Known Sorting Algorithms -- Bubble Sort -- Find Anagrams in a List of Words -- Selection Sort -- Insertion Sort -- Comparison of Sort Algorithms -- Merge Sort -- Merge Sort With a Third Array -- Merge Sort Without a Third Array -- Merge Sort: Shift Elements From End of Lists -- How Does Quick Sort Work? -- Quick Sort Code Sample -- Shell Sort -- Task: Sorted Arrays and the Sum of Two Numbers -- Summary -- Chapter 5: Linked Lists (1) -- Types of Data Structures -- Linear Data Structures -- Nonlinear Data Structures -- Data Structures and Operations -- Operations on Data Structures -- What Are Singly Linked Lists? -- Tradeoffs for Linked Lists -- Singly Linked Lists: Create and Append Operations -- A Node Class for Singly Linked Lists -- Java Code for Appending a Node -- Finding a Node in a Linked List -- Appending a Node in a Linked List -- Finding a Node in a Linked List (Method 2) -- Singly Linked Lists: Update and Delete Operations -- Updating a Node in a Singly Linked List -- Java Code to Update a Node -- Deleting a Node in a Linked List -- Java Code for Deleting a Node -- Java Code for a Circular Linked List -- Java Code for Updating a Circular Linked List -- Working With Doubly Linked Lists (DLL) -- A Node Class for Doubly Linked Lists -- Appending a Node in a Doubly Linked List -- Java Code for Appending a Node -- Java Code for Inserting a New Root Node -- Java Code for Inserting an Intermediate Node -- Traversing the Nodes in a Doubly Linked List -- Updating a Node in a Doubly Linked List -- Java Code to Update a Node -- Deleting a Node in a Doubly Linked List -- Java Code to Delete a Node -- Summary -- Chapter 6: Linked Lists (2) -- Task: Adding Numbers in a Linked List (1) -- Task: Adding Numbers in a Linked List (2) -- Task: Adding Numbers in a Linked List (3).
Task: Display the First k Nodes -- Task: Display the Last k Nodes -- Reverse a Singly Linked List via Recursion -- Task: Remove Duplicates -- Task: Concatenate Two Lists -- Task: Merge Two Ordered Linked Lists -- Task: Split an Ordered List Into Two Lists -- Task: Remove a Given Node from a List -- Task: Find the Middle Element in a List -- Task: Reverse a Linked List -- Task: Check for Palindrome in a Linked List -- Summary -- Chapter 7: Queues and Stacks -- What Is a Queue? -- Types of Queues -- Creating a Queue Using an Array List -- Creating a Queue Using an Array List -- Other Types of Queues -- What Is a Stack? -- Use Cases for Stacks -- Operations With Stacks -- Working With Stacks -- Task: Reverse and Print Stack Values -- Task: Display the Min and Max Stack Values (1) -- Task: Reverse a String Using a Stack -- Task: Find Stack Palindromes -- Task: Balanced Parentheses -- Task: Tokenize Arithmetic Expressions -- Task: Classify Tokens in Arithmetic Expressions -- Infix, Prefix, and Postfix Notations -- Summary -- Index.
Record Nr. UNINA-9910861061603321
Campesato Oswald  
Bloomfield : , : Mercury Learning & Information, , 2023
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Dealing with Data Pocket Primer
Dealing with Data Pocket Primer
Autore Campesato Oswald
Pubbl/distr/stampa Bloomfield : , : Mercury Learning & Information, , 2022
Descrizione fisica 1 online resource (246 pages)
Disciplina 001.42
Collana Computing
Soggetto topico Quantitative research - Reliability
Quantitative research - Data processing
Soggetto non controllato NLP
Pandas
RDBMS
SQL
computer science
data analytics
data cleaning
data visualization
programming
python
statistics
ISBN 1-5231-4740-7
1-68392-818-0
1-68392-819-9
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Frontmatter -- Contents -- Preface -- Chapter 1: Introduction to Probability and Statistics -- Chapter 2: Working with Data -- Chapter 3: Introduction to Pandas -- Chapter 4: Introduction to RDBMS and SQL -- Chapter 5: Working with SQL and MySQL -- Chapter 6: NLP and Data Cleaning -- Chapter 7: Data Visualization -- Index
Record Nr. UNINA-9910795724303321
Campesato Oswald  
Bloomfield : , : Mercury Learning & Information, , 2022
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Dealing with Data Pocket Primer
Dealing with Data Pocket Primer
Autore Campesato Oswald
Pubbl/distr/stampa Bloomfield : , : Mercury Learning & Information, , 2022
Descrizione fisica 1 online resource (246 pages)
Disciplina 001.42
Collana Computing
Soggetto topico Quantitative research - Reliability
Quantitative research - Data processing
Soggetto non controllato NLP
Pandas
RDBMS
SQL
computer science
data analytics
data cleaning
data visualization
programming
python
statistics
ISBN 1-5231-4740-7
1-68392-818-0
1-68392-819-9
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Frontmatter -- Contents -- Preface -- Chapter 1: Introduction to Probability and Statistics -- Chapter 2: Working with Data -- Chapter 3: Introduction to Pandas -- Chapter 4: Introduction to RDBMS and SQL -- Chapter 5: Working with SQL and MySQL -- Chapter 6: NLP and Data Cleaning -- Chapter 7: Data Visualization -- Index
Record Nr. UNINA-9910823375903321
Campesato Oswald  
Bloomfield : , : Mercury Learning & Information, , 2022
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Managing Datasets and Models
Managing Datasets and Models
Autore Campesato Oswald
Edizione [1st ed.]
Pubbl/distr/stampa Bloomfield : , : Mercury Learning & Information, , 2023
Descrizione fisica 1 online resource (387 pages)
Disciplina 005.133
Soggetto topico Python (Computer program language)
COMPUTERS / Database Management / Data Mining
Soggetto non controllato Data Mining
Computers
ISBN 1-68392-950-0
1-68392-951-9
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Front Cover -- Half-Title Page -- Title Page -- Copyright Page -- Dedication -- Contents -- Preface -- Chapter 1: Working with Data -- Import Statements for this Chapter -- Exploratory Data Analysis (EDA) -- Dealing with Data: What Can Go Wrong? -- Analyzing Missing Data -- Explanation of Data Types -- Data Preprocessing -- Working with Data Types -- What is Drift? -- What is Data Leakage? -- Model Selection and Preparing Datasets -- Types of Dependencies Among Features -- Data Cleaning and Imputation -- Summary -- Chapter 2: Outlier and Anomaly Detection -- Import Statements for this Chapter -- Working with Outliers -- Finding Outliers with NumPy -- Finding Outliers with Pandas -- Finding Outliers with Scikit-Learn (Optional) -- Fraud Detection -- Techniques for Anomaly Detection -- Working with Imbalanced Datasets -- Summary -- Reference -- Chapter 3: Cleaning Datasets -- Prerequisites for this Chapter -- Analyzing Missing Data -- Pandas, CSV Files, and Missing Data -- Missing Data and Imputation -- Skewed Datasets -- CSV Files with Multi-Row Records -- Column Subset and Row Subrange of Titanic CSV File -- Data Normalization -- Handling Categorical Data -- Working with Currency -- Working with Dates -- Working with Quoted Fields -- What is SMOTE? -- Data Wrangling -- Summary -- Chapter 4: Working with Models -- Import Statements for this Chapter -- Techniques for Scaling Data -- Examples of Splitting and Scaling Data -- The Confusion Matrix -- The ROC Curve and AUC Curve -- Exploring the Titanic Dataset -- Steps for Training Classifiers -- Diagram for Partitioned Datasets -- A KNN-Based Model with the wine.csv Dataset -- Other Models with the wine.csv Dataset -- A KNN-Based Model with the bmi.csv Dataset -- A KNN-Based Model with the Diabetes.csv Dataset -- SMOTE and the Titanic Dataset -- EDA and Data Visualization.
What about Regression and Clustering? -- Feature Importance -- What is Feature Engineering? -- What is Feature Selection? -- What is Feature Extraction? -- Data Cleaning and Machine Learning -- Summary -- Chapter 5: Matplotlib and Seaborn -- Import Statements for this Chapter -- What is Data Visualization? -- What is Matplotlib? -- Matplotlib Styles -- Display Attribute Values -- Color Values in Matplotlib -- Cubed Numbers in Matplotlib -- Horizontal Lines in Matplotlib -- Slanted Lines in Matplotlib -- Parallel Slanted Lines in Matplotlib -- Lines and Labeled Vertices in Matplotlib -- A Dotted Grid in Matplotlib -- Lines in a Grid in Matplotlib -- Two Lines and a Legend in Matplotlib -- Loading Images in Matplotlib -- A Checkerboard in Matplotlib -- Randomized Data Points in Matplotlib -- A Set of Line Segments in Matplotlib -- Plotting Multiple Lines in Matplotlib -- Trigonometric Functions in Matplotlib -- A Histogram in Matplotlib -- Histogram with Data from a Sqlite3 Table -- Plot a Best-Fitting Line with ggplot -- Plot Bar Charts -- Plot a Pie Chart -- Heat Maps -- Save Plot as a PNG File -- Working with SweetViz -- Working with Skimpy -- 3D Charts in Matplotlib -- Plotting Financial Data with Mplfinance -- Charts and Graphs with Data from Sqlite3 -- Working with Seaborn -- Seaborn Dataset Names -- Seaborn Built-In Datasets -- The Iris Dataset in Seaborn -- The Titanic Dataset in Seaborn -- Extracting Data from Titanic Dataset in Seaborn (1) -- Extracting Data from Titanic Dataset in Seaborn (2) -- Visualizing a Pandas Data Frame in Seaborn -- Seaborn Heat Maps -- Seaborn Pair Plots -- What is Bokeh? -- Introduction to Scikit-Learn -- The Digits Dataset in Scikit-Learn -- The Iris Dataset in Scikit-Learn (1) -- The Iris Dataset in Scikit-Learn (2) -- Advanced Topics in Seaborn -- Summary -- Appendix: Working with awk -- The awk Command.
Aligning Text with the printf() Statement -- Conditional Logic and Control Statements -- Deleting Alternate Lines in Datasets -- Merging Lines in Datasets -- Matching with Metacharacters and Character Sets -- Printing Lines Using Conditional Logic -- Splitting File Names with awk -- Working with Postfix Arithmetic Operators -- Numeric Functions in awk -- One-Line awk Commands -- Useful Short awk Scripts -- Printing the Words in a Text String in awk -- Count Occurrences of a String in Specific Rows -- Printing a String in a Fixed Number of Columns -- Printing a Dataset in a Fixed Number of Columns -- Aligning Columns in Datasets -- Aligning Columns and Multiple Rows in Datasets -- Removing a Column from a Text File -- Subsets of Column-Aligned Rows in Datasets -- Counting Word Frequency in Datasets -- Displaying Only "Pure" Words in a Dataset -- Working with Multi-Line Records in awk -- A Simple Use Case -- Another Use Case -- Summary -- Index.
Record Nr. UNINA-9910838326303321
Campesato Oswald  
Bloomfield : , : Mercury Learning & Information, , 2023
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui