Complete Computer Vision Bootcamp With PyTorch & Tensorflow

Learn to install Anaconda and VS Code, configure environments and path, and launch Jupyter notebooks through Anaconda Navigator to start Python data science projects.

Set up Python development in VS Code, create and activate a conda environment with Python 3.12, and run code and Jupyter notebooks with ipykernel.

Clarify Python syntax and semantics through practical examples, covering comments, case sensitivity, indentation, line continuation, and multi-statement lines, with hands-on variable assignment and type inference.

Learn how to declare and assign variables in Python, apply naming conventions, explore data types, perform type checking and conversion, and work with practical examples.

Explore Python data types, including integers, floating point numbers, strings, and booleans, and learn type conversion, type casting, and common type-related errors.

Explore Python operators through practical examples. Master arithmetic operations (addition, subtraction, multiplication, division, flow division, modulus, exponentiation), comparison operators, and logical operators (and, or, not) for practical examples.

Explore Python control flow by implementing conditional statements using if, elif, and else, including nested conditions and real‑world examples like voting eligibility, number parity, leap years, and common debugging tips.

Master Python loops, including for and while, range and step, with break, continue, and pass. Explore nested loops, string traversal, and practical examples like summing numbers and prime detection.

Explore Python lists, from creating and accessing items to slicing and modifying them with append, insert, remove, and pop. Master list comprehension and iteration for concise code.

Explore real-world Python list applications, including a to-do list manager, organizing grades with average, max, min, managing inventory, and analyzing user feedback using append, remove, and membership checks.

Learn Python sets, a built-in data type for unique items. Create with curly braces, test membership, and use add, remove, discard, and pop; perform union, intersection, difference, and symmetric difference.

Explore dictionaries in Python by creating and accessing key-value pairs, modifying elements, and using methods like keys, values, and items, including nested dictionaries and dictionary comprehension.

Explore the Python tuple data structure by creating, accessing, and manipulating immutable sequences, including packing and unpacking, nested tuples, and common methods such as count and index.

Learn how to define and call functions in Python, explore parameters, default values, positional and keyword arguments, and return statements to organize and reuse code.

Explore practical Python function examples: temperature conversion between Celsius and Fahrenheit, password strength checks, cart cost calculation, palindrome checks, recursion for factorial, file word frequency, and email validation with regex.

Explore Python lambda functions, anonymous one-expression functions with any number of arguments, and practical examples for addition, even checks, and using map with squared numbers.

Learn how the Python map function applies a given function to every item in an iterable, returning a map object, with examples using square, lambda, multiple iterables, and converting types.

Explore how the Python filter function builds an iterator from elements that meet a condition, filtering lists and dictionaries with functions or lambdas, including multiple criteria.

Learn how to import modules and packages in Python, using math and NumPy, with aliasing and from-import techniques. Create custom packages with __init__.py and install dependencies via pip and requirements.

Explore the Python standard library, including array, math, random, os, json, csv, date time, time, and re, with practical examples on file operations, data serialization, and basic data handling.

Master file operations in Python by creating, writing, reading, and appending text and binary files. Learn line-by-line reading, stripping newlines, and using seek to reread contents.

Learn to manage files and directories in Python with the os module. Create directories, list items, join paths, and check existence and type to work with absolute and relative paths.

Learn Python exception handling with try, except, else, and finally blocks to gracefully manage errors and keep programs running, covering name errors, zero division, and file not found.

Learn how classes and objects in Python model real-world scenarios with constructors, attributes, and instance methods, illustrated by bank accounts and dogs.

Explore inheritance in Python with single and multiple inheritance, using car and Tesla examples to show attributes, init method, super, and drive behavior.

Explore polymorphism in Python and object-oriented programming by using method overriding and interfaces and abstract base classes, illustrated with animal sounds and shape areas.

Explore how encapsulation bundles data and methods into a single unit, restricts direct access, and uses getters and setters to manage private variables, with public, protected, and private modifiers.

Explore abstraction in Python through abstract base classes and abstract methods, hiding complex details while exposing essential features, illustrated with a vehicle example and a washing machine.

Explore Python magic methods (dunder methods) that define object behavior for built-in operations such as arithmetic and comparison. Override __init__, __str__, and __repr__ to customize initialization and object representations.

Override python's magic methods to customize operators for a vector class with x and y coordinates. Overload addition, subtraction, multiplication, true division, and equality.

Create and use custom exceptions in Python by defining a generic error class and a date of birth exception, then validate ages 20 to 30 for exam form using try-except.

Explore iterators in Python, using lazy loading for efficient looping and memory management. Learn how to use next and stop iteration to iterate over lists and strings.

Use generators in Python to extend iterators with yield, lazily producing values and saving memory. Leverage generators to perform efficient file processing and demonstrate iteration with next and for loops.

Explore decorators in Python, including function copy and closures, and learn to create and use decorators to modify function behavior without changing original code in the advanced Python course.

Master NumPy basics for scientific computing in Python, including creating and reshaping arrays and matrices, vectorized operations, indexing and slicing, and statistical and universal functions for data analysis.

Explore pandas fundamentals by mastering series and data frames, creating data frames from dictionaries and lists, reading CSV data, and performing core data manipulation and selection tasks.

Explore data manipulation with pandas and numpy to perform exploratory data analysis, handle missing values, and master grouping, aggregation, and merging techniques.

Explore reading data with pandas from json, csv, html, and excel using inbuilt functions like read_json, read_csv, read_html, and read_excel, including orient options and conversion between dataframes and json.

Explore practical Python logging, configure basic settings, use levels such as debug, info, warning, error, and write logs to files like app.log with custom formats.

Explore logging with multiple loggers in Python. Create module-specific loggers and assign distinct levels for clear, targeted messages.

Learn practical Python logging with real-world examples by configuring basic settings, file and stream handlers, and multi logger support to track arithmetic operations.

Explore artificial intelligence, machine learning, and deep learning, and see how neural networks, cnn, rnn, and transformers enable object detection, image segmentation, and natural language processing with TensorFlow.

Discover why deep learning has surged in popularity as data grows from social media to big data, powered by GPUs and open frameworks like TensorFlow and PyTorch.

Explore how a perceptron functions as a simple single-layer neural network with input and hidden layers, weights, bias, and activation functions for binary classification.

Explore the advantages and disadvantages of single-layer perceptrons, why multilayer perceptrons and backpropagation unlock deep neural networks, and how loss functions, activations, and optimizers enable learning.

Master how multilayer perceptrons use forward and backward propagation to train deep neural networks, with loss functions, activation functions like sigmoid, and weight updates from backward propagation.

Understand backward propagation and weight updation in a two-layer neural network. Learn how gradient descent uses the learning rate with loss functions to update weights and biases.

Explore the chain rule of derivatives in backpropagation, showing how loss gradients flow through layers to update weights via learning rate and activation functions.

Explain vanishing gradient problem in deep neural networks caused by sigmoid derivatives, and compare activation functions such as tanh, ReLU, pre relu, and swish as alternatives to improve backpropagation.

Examine the sigmoid activation function, its 0–1 output after weights and bias, and how its 0–0.25 derivative causes vanishing gradients and slows backpropagation in deep nets.

Explore how the sigmoid activation function forwards values to 0–1 during forward propagation, and why derivative 0–0.25 causes vanishing gradients and zero-centered outputs. Consider tanh as an alternative.

Explore the tanh activation function, its zero-centered output from -1 to 1, and its derivative 0-1, comparing it to sigmoid to address vanishing gradients in neural networks.

Explore the ReLU activation function, its forward max(0, x) behavior, and its zero-or-one derivative to address vanishing gradients, while noting risks of dead neurons and faster computation.

Explore leaky ReLU and parametric ReLU to combat dead ReLU by ensuring nonzero derivatives and reducing vanishing gradients, with alpha as a tunable hyperparameter guiding backpropagation.

Explore the ELU activation function, addressing dead ReLU and zero-centered issues, and compare it with leaky ReLU and parametric ReLU in forward and backward propagation.

Learn how to apply the softmax activation function in the output layer for multi-class classification, generating probabilities for multiple classes such as cat, dog, monkey, and horse.

Select activation functions by task: sigmoid for binary classification and softmax for multi-class, while using ReLU or variants in hidden layers to avoid vanishing gradient and dead neurons.

Understand the difference between loss function and cost function in neural networks, including forward and backward propagation, mean squared error, and weight updates via optimizers like gradient descent.

Explore regression loss functions including mean squared error, mean absolute error, huber loss, and root mean squared error, and learn how gradient descent optimizes weights amid outliers.

Explore loss functions for classification, including binary cross entropy, categorical cross entropy, and sparse categorical cross entropy; learn when to apply sigmoid or softmax and one-hot encoding.

Match output activation to the task: binary cross entropy with sigmoid for binary classification; softmax with categorical or sparse cross entropy for multi-class; and MSE, huber, or RMSE for regression.

Learn how gradient descent optimizers update neural network weights to minimize loss through forward and backward propagation, exploring epochs, iterations, learning rate, and resource considerations.

This lecture explains stochastic gradient descent, updating weights after each data point within an epoch, reducing resource needs but increasing time and loss fluctuations, and introduces mini batch SGD.

Explore mini batch SGD and batch size to reduce iterations and noise, speeding convergence and enabling efficient resource use compared with stochastic gradient descent.

Discover how SGD with momentum reduces noise in mini-batch gradients by using an exponential weighted average and a beta-controlled smoothing, speeding up convergence in weight updates.

Examine Adagrad, an adaptive gradient descent method that uses a dynamic learning rate scaled by alpha_t and epsilon to accelerate early convergence while reducing updates over time.

Learn how adadelta and rmsprop adjust the learning rate using s dw, an exponential weighted average of squared gradients, to smooth updates and prevent vanishing steps.

Explore the Adam optimizer, combining SGD with momentum and RMSprop features to create dynamic, smooth weight and bias updates for faster convergence.

Examine the exploding gradient problem caused by large weight initialization and its impact on backpropagation, contrasting with vanishing gradients, and preview weight initialization techniques to stabilize learning.

Explore weight initialization techniques: uniform distribution, Xavier/Glorot, and Kaiming He, emphasizing small, diverse weights with proper variance to prevent exploding gradients.

Discover how dropout layers reduce overfitting in multilayer networks through feature sampling and random neuron deactivations during training, then scale weights at test time for stable predictions.

Explore convolutional neural networks for image classification, object detection, and segmentation, linking CNN workflows to brain processing from input images to visual cortex layers v1–v5.

Compare the brain's visual cortex processing from retina to occipital lobe with CNNs that use filters to detect edges, colors, and complex patterns for object recognition.

Discover the basics of image representation in convolutional neural networks, including rgb and grayscale images, channels, and pixel values from 0 to 255, with examples of image dimensions.

Discover how convolution operations in a cnn apply a 3x3 filter to a 6x6 grayscale image, revealing vertical and horizontal edges through normalization, stride, and output 4x4 results.

Learn how padding in convolutional neural networks prevents information loss by adding one-layer padding to six-by-six images, yielding six-by-six outputs after a three-by-three filter, using zero or neighbor padding.

Compare how CNNs operate with convolution, filters, ReLU, and pooling, and how ANN updates weights through forward and backward propagation for learning.

Explore max pooling, min pooling, and average pooling within CNNs and learn how pooling with ReLU and convolution layers produce location-invariant features.

demonstrates how a convolutional neural network classifies mnist digits using convolution and max pooling, the flattening layer, and a fully connected layer for nine outputs.

Demonstrates an rgb cnn with three convolution–relu–pooling blocks, flattening, and a fully connected layer for softmax classification; highlights feature maps, feature extraction, and forward/backward propagation.

Explore how OpenCV powers basic computer vision tasks—from read/write images and edge detection to tracking and color filtering—within a hybrid system that pairs OpenCV with PyTorch and TensorFlow.

Read, display, and write images with OpenCV in Python. Guide setting up a conda environment in VSCode, loading an image, verifying it, and saving a modified output.

Learn to work with video files using OpenCV by understanding frames, fps, and codecs, then read, display, and save videos with cv2 video capture and video writer.

Explore color spaces in OpenCV, and learn how to convert between rgb, bgr, grayscale, hsv, and lab, to support image processing and color-based segmentation.

Explore color thresholding in OpenCV, using hsv color space to segment colors by hue, and generate a binary mask to replace or isolate targeted colors.

Learn to resize and scale images with OpenCV, understand the impact on image quality, and choose interpolation methods—nearest, linear, cubic, area, and Lanczos—for your use case.

Master basic image manipulations—flipping, rotating, and cropping—to support data augmentation and improve generalization in deep learning for computer vision.

Explore how OpenCV uses a Cartesian coordinate system with origin at the top-left, where x increases left to right and y increases top to bottom, for cropping, drawing, and text.

Draw lines and shapes with OpenCV on a canvas, including rectangles, circles, ellipses, and polygons, and learn color handling and cv2 drawing functions for annotation and visualization.

learn how to add text to images in OpenCV by using the coordinate system, creating a blank canvas, converting color channels, and using putText with fonts, sizes, and thicknesses.

Explore affine and perspective transformations to rotate, scale, shear, translate, and correct distortions, mapping image coordinates with transformation matrices for robust augmentation and alignment.

Explore image filters in OpenCV and create custom kernels to extract edge features, blur, or sharpen images through convolution, with stride and padding handled automatically.

Explore OpenCV blur filters, including average, Gaussian, and median blurs, and learn how local neighborhood averaging reduces noise and smooths images.

Detect edges in images using Sobel, Canny, and Laplacian methods to extract features, analyze shapes, and pre-process data for CNN or AI models.

Explore histograms as a fundamental image processing tool, revealing brightness, contrast, and dynamic range; plot rgb and hsv histograms to guide equalization and enhancement.

Balance brightness and contrast using histogram equalization, compute histograms and the cumulative distribution function, map intensities to the full range, and apply to grayscale and color images with OpenCV.

Apply CLAHE, contrast limited adaptive histogram equalization, to image tiles for localized contrast enhancement, with clip limits and seam-free tiling, including luminance-only processing in RGB and YUV color spaces.

Learn how to detect object boundaries with contours using OpenCV, including finding contours, counting objects, shape analysis, and drawing bounding boxes for object detection.

Learn how to perform image segmentation with OpenCV, separating foreground from background and generating semantic and instance masks using thresholding, adaptive thresholding, k-means clustering, and the watershed method.

Detect faces in images using Haar cascades, Haar-like features, and a cascade of classifiers, implemented with OpenCV to run in real time from a webcam.

Discover the basics of PyTorch, an open-source, pythonic deep learning framework. Learn how PyTorch enables tensor operations on GPUs, autograd, and production-ready TorchScript.

Explore tensors in PyTorch, from scalar and vector to 3D tensors, and learn tensor creation, shape, size, dtype, and device, with GPU acceleration via CUDA.

Learn to index and slice PyTorch tensors, access and modify elements, apply boolean and fancy indexing, perform advanced selection with index select, and flip tensors by dimension.

Learn to generate random numbers with PyTorch, including uniform, random integers, and normal distributions, then create grayscale and RGB noise images and plot them with matplotlib.

Create zeros and ones tensors in PyTorch to initialize models, placeholders, and testing data. Inspect shapes and dimensions, and visualize them as grayscale images with matplotlib using vmin and vmax.

Explore PyTorch tensor data types and their impact on performance, memory, and precision. Learn how float, int, bool, and complex types influence memory and speed, with conversions between dtypes.

Explore core tensor manipulation techniques in PyTorch, including reshaping with reshape and view, slicing, joining and splitting with cat, stack, chunk, and split, and transposing, permuting, cloning, and detaching.

Explore matrix aggregation with PyTorch, learning to compute sum, mean, min, max, and median, and perform dimension-wise, cumulative sum and cumulative prod, masking, and normalization on tensors.

Compare view and reshape to change tensor shapes, highlighting contiguity: view works only on contiguous memory, while reshape handles non-contiguous data by copying when needed.

Learn to stack two tensors along rows or columns with torch.stack, creating new dimensions and 2d or 3d results, and compare to concatenation with torch.cat.

Explore PyTorch neural network components, including forward propagation, loss, backpropagation, and optimization using nn.Module, linear layers, ReLU, cross entropy loss, and Adam, with functional and sequential approaches.

Build a linear regression model using PyTorch, mastering the full machine learning workflow from data gathering and preprocessing to feature engineering, model training, and evaluation on the insurance dataset.

Learn to perform multi-class classification with PyTorch using custom neural networks on the iris dataset with three classes (setosa, versicolor, virginica), covering data preparation, model design, training, evaluation, and inference.

Build a custom dataset and wrap it with a PyTorch data loader for efficient mini-batch training. Explore batching, memory management, and how shuffle enhances training.

Define a custom image dataset loader for PyTorch to handle unstructured image data, organize train and test folders (cat, dog, person), apply transforms, and load batched data for classification.

Train a cnn on a custom image dataset with three classes using pytorch, build custom data loaders with transforms, design the architecture, train, evaluate, and perform inference.

Identify front end, back end, model, and deployment as core machine learning app components, and build an image classification web app with preprocessing, inference, and post-processing for real time predictions.

Deploy an AI application by moving from a local run to the cloud, hosting online with Hugging Face Spaces, Streamlit Cloud, or AWS, GCP, and Azure.

Explore tools to create interactive demos, compare UI-enabled options (Plotly Dash, Streamlit, Gradio) with API-focused frameworks (Flask, FastAPI), and learn deployment with Hugging Face spaces.

Explore hosting platforms for machine learning models, comparing Streamlit, Hugging Face Spaces, and cloud services like SageMaker, Lambda, and Cloud Run, to choose production-ready or demo deployments.

Set up a local gradio app with vscode and conda, create the radio_app_temp_one env in python 3.11, install gradio via requirements.txt, and run app.py to test a classifier user interface.

Implement a Gradio app inference backend for image classification using a CNN with torch, loading weights, applying transforms, and annotating predictions on images for a reusable front-end.

Deploy your Gradio image classification app to Hugging Face spaces by creating a space with the Gradio SDK and pushing code via git with an access token.

Deploy your gradio app on hugging face spaces to make predictions accessible online, by cloning the repository, pushing code, installing requirements, and launching the app for public access.

Explore how cnn architectures outperform ann models in image understanding, detailing convolution and pooling layers and the progression from edges to textures, patterns, and object parts.

Explore how the CNN explainer visualizes convolution on RGB input using kernels and activation functions, showing feature maps through pooling, flattening, and softmax logits for classification.

Explore cnn visualizations in tensor space, using LeNet to illustrate convolution, max pooling, and feature maps; track how inputs of 28×28 transform through layers to hidden representations and final output.

Explore cnn filters, kernels, and feature extractors, and learn how filter size like 3x3, 5x5, 7x7 and depth shape feature maps through convolution, with stride and padding affecting output.

Build your own 3x3 filters to detect vertical, horizontal, and diagonal edges. Explore how kernel values shape edge features and try cv2.filter2d for practical feature extraction.

Learn to calculate feature map sizes for CNN layers and max pooling using padding, stride, and kernel sizes, verify results with model summaries and understand output shapes.

Learn how to calculate trainable parameters in a cnn, using conv and dense layers with kernels, input channels, and biases, verified to 404,218 parameters in Keras and PyTorch.

Explore receptive fields in CNNs, from local 3x3 regions to growing global receptive fields across layers, and learn how kernel size, stride, and depth shape feature visibility.

Explore image classification in computer vision, detailing CNN-based deep neural networks that extract features, assign labels, and distinguish single versus multi-object images, with key datasets and metrics.

LeNet five, an early cnn architecture by Yann LeCun (1998) designed for handwritten digit recognition on MNIST, featuring seven layers (three conv, two pooling, two dense) to classify digits 0–9.

Implement LeNet in Keras to classify MNIST digits with a sequential convnet using max pooling and ReLU activations, culminating in high accuracy and a model summary.

Learn to implement the LeNet-5 model in PyTorch on MNIST, using torchvision transforms, data loaders, and an Adam optimizer with cross-entropy loss, plus training, evaluation, and inference.

Explore AlexNet, a deep convolutional neural network that demonstrated deep learning for ImageNet. See eight-layer architecture with five conv and three dense layers, ReLU, normalization, pooling, and dropout.

Implement AlexNet in Keras using a custom two-class flower dataset (dandelion vs daisy) on Google Colab, with data augmentation, batch normalization, and save the model to perform predictions.

learn to implement alexnet in pytorch with image augmentation, transforms, and a custom image folder dataset, train with loss and accuracy tracking, evaluate, and perform inference.

Explore the VGG architecture, a deep CNN built from stacked 3x3 convolutions and max pooling for ImageNet classification, covering 16 and 19 variants and transfer learning options.

Master transfer learning and pre-trained models in computer vision, using ImageNet as a starting point, freezing and training layers on small datasets for faster training and better accuracy.

Explore VGG pre-trained models in Keras applications, load ImageNet weights, and perform inference with VGG16 or VGG19, including image preprocessing and top-5 predictions.

Explore a simple PyTorch implementation of a VGG-16 pre-trained model with torchvision, eval mode for inference, and ImageNet preprocessing with softmax top-5 predictions mapped to class names.

Apply transfer learning with a pre-trained VGG16 model in PyTorch to a two-class flowers dataset (daisy and dandelion), freezing all layers except the final classifier.

Explore the GoogLeNet inception architecture, tracing inception v1 to v3 with parallel convolution blocks, dimension reduction via pointwise convolutions, auxiliary classifiers, and global average pooling.

Use the inception v3 pretrained model in keras with imagenet weights to preprocess, predict, and decode top results for a sample image, illustrating transfer learning.

Learn to load a pre-trained inception v3 model from Torchvision in PyTorch, apply image transforms, and visualize top-5 ImageNet predictions.

Apply inception transfer learning with keras on the CIFAR ten dataset, using inception v3 ImageNet weights, customizing input shape, freezing layers, and training a 10-epoch classifier.

Explore the ResNet architecture and how residual blocks, skip connections, and identity mapping tackle vanishing gradients, enabling deep networks through bottleneck designs across variants.

Explore ResNet 50 pretrained with Keras, including loading images, preprocessing, decoding predictions, and visualizing top five ImageNet classes with a zebra example.

Explore loading a pretrained ResNet-50 in PyTorch, perform image preprocessing and normalization with ImageNet stats, and run inference to obtain top-3 predictions with embedded class labels.

Explore transfer learning with a pre-trained ResNet-50 in PyTorch, adapting the classifier for five fruit classes, training with ImageNet normalization, and evaluating predictions on a large dataset.

Learn how data augmentation artificially expands small image datasets through geometric and color-space transformations, noise, and occlusion, boosting generalization, while using OpenCV, Keras, torchvision, Albumentations, and imgaug.

Apply albumentations to augment images with horizontal flips, shifts, scales, rotates, and multiple combined transforms; generate augmented images and save the results.

learn to perform data augmentation with imgaug, applying flips, rotations, brightness, noise, and grayscale. visualize results, configure the pipeline, and save augmented images.

Discover how object detection differs from image classification by locating multiple objects with bounding boxes, class labels, and confidence scores in images or video frames.

Understand object detection evaluation using intersection over union, true/false positives, precision and recall, and PR curves; compare 11-point and all-point interpolation for average precision with Pascal VOC and COCO benchmarks.

Explore bounding boxes, rectangular regions that enclose objects detected by model, and compare corner format with center format using x_min, y_min, x_max, y_max, or x_center, y_center with width and height.

Explore real-time object detection with YOLO v11 on CPU, testing nano, small, medium, large, and x models with pre-trained weights. Install Ultralytics, run inference, visualize results, and note performance.

Explore Detectron2, a state-of-the-art detection and segmentation library, through a Colab notebook, install models from the model zoo, and run the default predictor for bounding boxes and labels.

Explore object detection architectures across two-stage, one-stage, and transformer-based designs, emphasizing classification and localization, region proposals, and advances from R-CNN to DETR.

Introduce the R-CNN architecture, its region-proposal pipeline with selective search, CNN feature extraction, SVM classification, and bounding-box regression.

Explore how fast R-CNN speeds up object detection by sharing feature maps, replacing external selective search with an RPN, and performing simultaneous classification and bounding-box regression.

Explore the faster R-CNN architecture, a region proposal network built on a convolution backbone, using anchor boxes and ROI pooling for end-to-end object detection.

Implement faster r-cnn with PyTorch using a pre-trained COCO model to perform inference, preprocess images, and visualize bounding boxes with class labels and confidence scores.

Train a custom object detector with YOLOv11 using Ultralytics in Google Colab, from dataset download to training, validation, and inferencing. Export the model to ONNX or TensorRT for faster inference.

Train a custom object detector with Detectron2 on a dataset (person, head, helmet), register the data in coco format, train faster r-cnn, and save the model and config for predictions.