Python for Scientific Computing & Deep Learning (4 Projects)

Learn how assignment statements in Python infer variable types without explicit declarations, unlike C or C++, and see examples of integers, strings, floats, and lists inferred by the interpreter.

Master conditional execution with the if statement, including keywords, conditions, and else branches. Use if, elif, and else to choose actions based on simple comparisons like x equals two.

The lecture explains for loops over a list and with range, printing elements, and demonstrates a while loop that increments i and prints values while i is less than 10.

Explain how the break statement exits a loop when a condition is met, illustrated by a while true loop that increments i and stops at 10.

The continue statement jumps to the next iteration of a loop, skipping the rest of the current iteration. An example shows output skipping five and moving from six to eight.

Learn how to use the try statement in Python to execute code safely, catch exceptions such as division by zero, and print informative error messages for robust error handling.

Explore how the pass statement acts as a placeholder that does nothing. See how it lets code continue to the next instruction without handling an exception.

Explore using raise statements to trigger exceptions for invalid inputs, such as negative numbers, and raise a type error when the input type is not correct.

Explain the Python def statement, including function name, inputs, and a body, with a pass placeholder, and demonstrate a two number function that adds and prints the sum.

Learn how the return statement outputs a value from a function. Use return z to return the sum of two inputs; without return, the function prints and returns nothing.

Explore how assertions use boolean conditions to validate inputs, halt on failure, and raise assertion errors, with a practical Python example verifying x is not zero before inversion.

Explore yield statements that define generators, returning values while preserving local variables across calls. Continue execution from where they were left off, yielding 1, 2, and 3.

Explore Python built-in types and how to use them, including boolean type, bytes type, complex type, dictionary type, flow type, list type, range type, set type, string type, and tuples.

Explore the boolean type by converting values to booleans with a function, recognizing nonzero as true and zero as false, and evaluating simple logical and arithmetic expressions.

Explore the bytearray type, a mutable sequence of integers from 0 to 255, enabling string-to-bytes conversion, size-based initialization, and construction from an iterable list.

Explore the bytes type, an immutable sequence of integers from zero to two hundred fifty six, and learn how to declare a bytes object and print it.

Explore the complex type for representing complex numbers, and learn three ways to declare them. For example, z = complex(-1, 2) yields -1+2j, and complex(1) yields 1+0j.

Explore the dictionary type in Python, declare empty and non-empty dictionaries, and access values using brackets. Add new keys and merge dictionaries with update to combine key-value pairs.

Explore the Python float type as floating point numbers with decimal points. See x = 1.2436 and test if a float is an integer using a built-in check; view hexadecimal representations.

Demonstrate bitwise operations on integers in Python, using X and Y to illustrate or, xor, and, shifts, and bitwise not, with binary representations and practical examples.

Explore the set type, an unordered collection of unique and immutable objects, with duplicates removed on declaration. Practice add, copy, clear, difference, intersection, and superset operations, including pop.

Explore frozenset type as an immutable version of a set, where elements stay the same after creation, convert a set to a frozenset using the frozenset keyword, adding not allowed.

Explore the Python list type, which is ordered and mutable, supports duplicates, and enables indexing, length checks, and list operations such as append, remove, delete by index, and extend.

Learn how the Python list extend method updates a list in place and returns None, illustrating the None type and in-place modification with practical examples.

Explore the range type, an iterable sequence of integers convertible to a list; see zero through nine, negative starts, and a stride of two.

Explore Python strings: declare and print strings with quotes, slice and concatenate, test membership, and apply methods like split, join, replace, partition, and case and whitespace checks.

Explore tuples as ordered, immutable collections that allow duplicates, access elements by indexing, and hold multiple data types, with length and type retrieval.

Learn mathematical operations in Python, including addition, subtraction, modulo, multiplication, exponentiation, division, logic expressions, and rounding.

Explore addition, subtraction, and modulo operations in Python on real numbers, and perform addition and subtraction on complex numbers with example variables.

Multiply and exponentiate real and complex numbers in python using the star and double-star operators; compute products, powers, and complex exponentiations, and print results.

Explore division in code within the Scientific Python & Deep Learning Masterclass, covering real and complex numbers and handling division by zero with a try and except.

Explore core Python logic expressions for branching, including membership tests with in and not in, and boolean operators and equality checks for building robust conditional logic.

Master rounding in Python by converting numbers to the nearest integer, including floor behavior and handling .5 cases, with examples 2.3 -> 2, 2.5 -> 2, and 2.7 -> 3.

Explore NumPy arrays, create and manipulate arrays using ones, zeros, empty, range, linspace, and shape, perform indexing, slicing, concatenation, and stacking, and visualize with matplotlib.

Sort NumPy arrays from smallest to largest using sort methods, then sort 2d arrays by rows or by columns by setting the second parameter to zero for column-wise sorting.

Learn to concatenate numpy arrays using the concatenate method, joining two arrays along axis zero or axis one, while ensuring shapes match.

Learn to obtain an array's shape with the shape attribute. Reshape a 6x4 array into a 1x24, then into a 2x12, to see how new shapes emerge.

Master indexing and slicing of multi-dimensional arrays using slice notation to access elements and entire axes, with examples for rows and columns and higher dimensions.

Learn to concatenate two-dimensional arrays using hstack and vstack to build horizontally and vertically stacked arrays. Explore practical examples of stacking arrays to combine data efficiently.

Perform basic mathematical operations on numpy arrays, such as sum, minimum, maximum, and scalar multiplication. Execute matrix operations: addition, subtraction, element-wise multiplication, dot method product, and transpose.

Explore numpy's unique method to remove duplicates from arrays and optionally return the original indices. See how to reconstruct the original array from indices and apply unique to various arrays.

Save and load numpy arrays with save and load methods to a file, then reload them; use text file methods for 1d or 2d arrays, API format for higher dimensions.

Plot and customize with Matplotlib, using line plots with colored markers, axis limits, and labels. Create subplots and generate scatter plots from X and Y coordinates.

Explore optimization using SciPy to minimize multivariate functions, compare gradient-based methods with and without jacobians, and solve linear programming and root-finding problems.

Demonstrate single-variable integration with quad for a=3 and b=4 on 0 to 1, yielding 2.83879 with tiny error, and show a double integral of sqrt(1 - x^2 - y^2) over the unit disk with bounds y in -1 to 1 and x in -sqrt(1 - y^2) to sqrt(1 - y^2).

Demonstrates discrete convolutions using a convolve function on sample arrays and shows how interpolation and splines fit a polynomial through points and plot with matplotlib.

Explore linear algebra tasks in Python, including matrix inversion, products, determinants, linear systems, eigenvalues, eigenvectors, norms, and circulant matrices.

Create 3-D models with SketchUp and visualize them in Vista, then perform relative flux computations to analyze heat transfer and solar radiation via surface characteristics and mesh triangles.

Learn to visualize SketchUp models in Python by loading a free SketchUp file, plotting the mesh with a visualization library, and inspecting points, cells, and normals of an unstructured grid.

Compute 3d distances between points, calculate the triangle perimeter from edge distances, and derive the triangle surface via the cross product of ab and ac.

Learn to compute radiative flux between mesh cells using form factors derived from cosines of surface normals and center distances, implemented in Python to sum contributions across all cells.

Learn to scrape web pages using the Python requests library and BeautifulSoup, converting HTML to a soup, and extracting links, classes, and attributes with find_all and CSS selectors.

Learn how to apply web scraping techniques to a real estate example, extracting rent prices, locations, titles, and descriptions for multiple listings, with a focus on proxies and the scraper.

Learn how wet proxies mask your IP and enable safe web scraping by routing requests through a proxy pool of multiple IPs with random permutations and user agents.

Build a real estate web scraper using a proxy pool, requests, logging, and Beautiful Soup to extract links, titles, prices, and coordinates across multiple pages.

Learn how to define a Python class, implement a constructor and class methods, and differentiate class versus instance attributes while exploring inheritance, private variables, and registering students.

Explore object oriented programming through class inheritance, creating a child class that inherits from a parent class using super to initialize, and extend with programming language attribute and register method.

Explore object oriented programming with multiple inheritance, combining constructors and attributes from several classes, using super to orchestrate initialization and methods to reveal instance attributes.

Explore private variables and methods in object oriented programming by using double underscores. Access is restricted to the class and outside calls raise attribute errors.

Explore object oriented programming iterators by building a random number generator class. Instantiate the class and use its next method to yield random numbers between zero and one.