PyTorch for Deep Learning Computer Vision Bootcamp 2026

Prepare your system for PyTorch by using free Google Colab resources or a CUDA-enabled local setup, then verify installation by importing torch and printing its version.

Learn to create tensors in PyTorch from a Python list using torch.tensor, and generate ones, zeros, empty, and full tensors; create linspace ranges and identity matrices with eye, specifying dtype.

Learn how NumPy arrays integrate with PyTorch, using from_numpy to create views without copying. See in-place changes propagate and compare addition, subtraction, multiplication, and matrix multiplication performance on cpu.

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.

Learn how to run PyTorch computations on a GPU, compare CPU and GPU speeds, and see how CUDA devices boost tensor operations and matrix multiplication for deep learning.

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.

build a deep neural network with a 10-unit input layer, a 5-unit hidden layer and a sigmoid output using a sequential dense model and gradient descent with the dodge library.

Build and train a deep neural network in PyTorch by defining input, hidden, and output layers, activation, mean squared error loss, backward pass, and sgd optimization.

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.

Visualize batched image data by converting tensors to NumPy and transposing to 32x32x3. Display the images to inspect how the training dataset looks.

Explore how convolution uses a kernel to slide over an image, compute dot products, and learn features, with stride and padding, then apply mean and max pooling.

Explore the first cnn by applying a 2d convolution to color images with a 3x3 kernel, 0 padding, and configurable stride, and observe the forward pass output shapes and channels.

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.

Implement the LeNet model in PyTorch by constructing a convolutional network with conv2d, average pooling, and dense layers, applying flattening and a max-based class prediction.

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.

Prepare your system for hands-on ai learning by installing Jupyter Notebook via the Anaconda distribution, verifying with a hello world program, and using Google Colab for deep learning later.

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.

Master Python programming essentials by exploring variables, data types, strings, data structures, conditional statements, functions, and classes, then complete a mini project to boost confidence for deep learning.

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.

Learn how booleans work in Python by defining true and false variables, checking their type, and using and, or, not, and xor for logical comparisons in code.

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.

Learn how to add comments in Python using the hash symbol for single-line comments and triple single or triple double quotes for multi-line comments that become documentation, with printing examples.

Explore Python lists as core data structures by creating, accessing, modifying, appending, deleting, slicing, and using negative indexing and len on mixed data types.

Explore Python tuples as immutable containers for multiple objects, contrast with lists via indexing and slicing, and learn sets as unordered collections with in membership tests and the add method.

Learn Python dictionaries store unique keys with associated values, retrieve and modify entries, and add or delete items, using unordered, changeable, and indexed structures.

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

Explore while loops in Python by incrementing a counter, testing conditions, and printing values. Show break and continue controlling loop execution and output ranges like 0 to 9.

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.

Master defining and calling functions in Python with the def keyword, passing parameters, returning values, defaults, and lists to demonstrate function behavior.

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 how to implement hangman logic in Python by handling single-letter input, building status hints, validating guesses, and guiding gameplay within a class method.

Test and validate a hangman game implementation in Python by running scenarios with correct and incorrect guesses, tracking remaining chances, and confirming the program meets business requirements.

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

Learn how to resize and reshape numpy arrays to new shapes, see how shape changes affect elements, and use numpy's resize and reshape functions for deep learning workflows.

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 to use the pandas library to manage two-dimensional data tables, creating series and dataframes from lists and dictionaries. Index, select, and update data using label and position access.

Explore pandas dataframes as in-memory tables created from dictionaries or lists, with labeled columns and adjustable indices; learn to load external data and view data frames like excel sheets.

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 handle missing values in data frames by deleting rows or columns with nulls or imputing values, check null counts, and use dropna with axis.

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.

Perform imputation to handle missing values by replacing nulls with the mean in a pandas data frame, preserving data while preparing features for machine learning.