PyTorch for Deep Learning Computer Vision Bootcamp 2026

Explore how cuda unlocks gpu-accelerated parallel computing, enabling general-purpose calculations on cuda-enabled GPUs like Tesla K80, with steps to enable in Colab and verify availability and device details.

Explore autograd in PyTorch to understand automatic differentiation, gradients, and backpropagation for neural networks, and how to compute derivatives and update weights using backward pass and loss optimization.

Implement gradient descent with autograd to train a linear model on random inputs using squared error loss, updating weights and bias through gradients across epochs to approach 1.8 and 32.

Learn to build a custom neural network module by subclassing a base module, defining init with layer parameters, and overriding forward to compose fully connected layers and sigmoid activation.

Learn to load CIFAR-10 data for convolutional networks, including 60,000 32×32 color images in 10 classes, with train and test splits, transforms, and batched data loading.

Learn to build a deep CNN with two 3x3 convolutions, expanding channels from 3 to 10 and then to 5, followed by mean and max pooling to reduce spatial size.

Explore the lean LeNet architecture for digit recognition, using two convolutional layers with average pooling, followed by three fully connected layers and a softmax classifier for 10 digits.

Configure and evaluate a PyTorch computer vision model by loading train and test data, batch size 256, training for 20 epochs with cross-entropy loss and Adam optimizer, and tracking performance.

Learn why a computer programming language is essential to communicate effectively with a computer and how Python has become a practical choice for deep learning and computer vision.

Discover how Python is easy to learn through a hands-on demo that reads a user letter and uses a simple if statement to classify it as a vowel or consonant.

Master Jupyter notebook basics, from renaming and saving notebooks to using code and markdown cells, edit and command modes, and executing cells with familiar shortcuts in Google Colab.

Learn how Python handles variables, constants, keywords, and case-sensitive naming, including dynamic typing, memory locations, and print examples.

Learn how to use Python's print function to display variables and values, print multiple objects with commas, and understand last-expression output in Jupyter notebooks.

Explore Python numeric data types—integers, floats, and complex numbers—and use type() and print() to inspect them. Practice basic arithmetic operations and order of operations with a sample equation.

Explore the string data type in Python, including creating strings with single or double quotes, concatenation with +, length with len, and common methods like strip, upper, and lower.

Explains implicit and explicit type conversion in Python, showing int to float promotions to avoid data loss and explicit casting with int, float, and str.

Master Python conditional statements with if, else, and elif; control flow and nested conditions through indentation, showcasing comparisons and simple decision making.

Explore two fundamental Python inbuilt functions, range and input, and learn how range generates start-to-end sequences with an optional step, including converting input to integers for checks.

Learn how to use Python for loops to iterate over ranges, lists, tuples, dictionaries, and strings, and control flow with break and continue, illustrated by print-based examples.

Learn Python’s object oriented programming by using the class keyword to define classes as blueprints for objects, with attributes, methods, and the __init__ constructor, leading to a hands-on mini project.

Develop a hangman game in Python from scratch, where a random word is chosen, the number of letters is hinted, and players guess letters or the word within 10 chances.

Create a Python class hangman with an initialized words list and a new_game method; start by selecting a random word with random.choice and converting it to uppercase.

Continue building a hangman game in Python by selecting a random word, initializing the letters list with a for loop, and managing guesses, hints, and game flow.

Build a hangman-style logic using a while loop, input handling, and guess tracking with a Python list, validating length and already-guessed inputs, normalizing to uppercase, and tracking remaining chances.

Learn to work with numpy arrays, create 1d and 2d arrays, inspect shape and dtype, and generate empty, ones, zeros, random, and identity matrices.

Explore slicing on num pieties, including indexing from zero and the inclusive start, exclusive end rule. Apply step sizes to both one-dimensional and multi-dimensional arrays to select specific elements.

Master broadcasting to perform elementwise operations on arrays by applying the three core rules for dimensions and shapes, and verify results using practical Python examples.

Explore numpy operations on arrays such as X and Y, including arithmetic, trig, dot product, and aggregation (sum, mean, min, max, median, std), with axis guidance.

Learn how to load external data into a pandas dataframe, set column names and index, inspect with head and info, rename columns, and drop duplicates.

Learn to slice a pandas dataframe by reading required elements, selecting single or multiple columns, and filtering rows with boolean conditions and single-line expressions.