Deep Learning

Explore course objectives, prerequisites, and your instructor as you prepare to build, train, and evaluate neural networks from scratch in deep learning.

Discover deep learning: its place in AI, how neural networks learn patterns from data, why it's so powerful, and when to use it for complex, large-scale, unstructured data tasks.

Explore how simple artificial neurons combine into layers to form neural networks, enabling machines to learn complex patterns for tasks like image recognition and decision-making.

Build and understand a single-neuron network using PyTorch; explore how weights and bias create decision boundaries; solve real and logic problems with perceptrons.

Discover how activation functions add non-linearity to neural networks, enabling them to learn complex patterns beyond linear relationships for real-world tasks.

Explore how activation functions empower neural networks to learn non-linear patterns, compare Sigmoid, Tanh, and ReLU, and understand their limitations in solving complex problems.

Explore how neural networks use layers, depth, and width to learn complex patterns. Learn to design and balance network architectures for effective deep learning solutions.

Learn to build, analyze, and compare multi-layer neural networks (MLPs) in PyTorch, exploring model architecture, hidden layers, and parameter counts with real and classic datasets.

Discover how data moves through neural networks using computational graphs and tensor shapes, enabling you to trace, debug, and refine model predictions with confidence.

Learn to control data flow through PyTorch neural networks, implement custom forward passes, and debug architectural errors using shape tracking for robust model building.

Learn how neural networks use loss functions to measure errors, guide learning, and choose the right function for regression, binary, and multiclass tasks.

Learn how and why to select and implement appropriate loss functions for regression (MSE) and binary classification (BCE) tasks to ensure effective neural network training.

Explore how neural networks learn: gradient descent and backpropagation optimize weights to reduce error, with optimizers like Adam accelerating and stabilizing the training process.

Learn to build a five-step PyTorch training loop, prepare data, train neural networks, and compare optimizers (SGD vs. Adam) for effective model learning.

Learn essential steps to prepare data for machine learning: split datasets, preprocess for quality and optimization, and efficiently load data for reliable model training.

Learn to build efficient data pipelines: clean, encode, scale, batch, and load tabular data for PyTorch models, optimizing preprocessing and DataLoader settings for robust training.

Learn to diagnose machine learning model performance by identifying underfitting, overfitting, and training instability using the bias-variance tradeoff and loss curves.

Learn to plot and interpret loss curves to diagnose underfitting, overfitting, good fit, and unstable training by comparing training and validation losses during model training.

Learn to evaluate machine learning models beyond loss and accuracy by choosing metrics like precision, recall, MAE, and RMSE that align with real-world needs and error costs.

Learn to assess model performance using metrics beyond accuracy, using precision, recall, F1, PR and ROC curves to evaluate and optimize models, especially with imbalanced data.

Learn a systematic framework to improve deep learning models by diagnosing issues and applying techniques in Data, Model, Optimization, and Inference to boost performance and stability.

Learn to diagnose and address overfitting, underfitting, and instability by applying techniques like dropout, learning rate decay, and systematic model improvement strategies.

Discover why fully-connected networks struggle with structured data and how specialized architectures like CNNs, RNNs, and Transformers leverage inductive bias for better AI solutions.

Explore the course goals, meet your instructor, and discover how computer vision and CNNs enable computers to interpret images and solve real-world visual tasks.

Explore how Convolutional Neural Networks (CNNs) revolutionize computer vision by preserving spatial structure and enabling translation-invariant image recognition.

Discover how convolutional neural networks extract image features by applying learnable filters, enabling efficient visual recognition through spatial awareness and translation invariance.

Learn to implement 2D convolutional layers in PyTorch, use custom filters to extract features from images, and visualize feature maps for foundational computer vision skills.

Learn how padding and stride control feature map size and detail in CNNs, preserve spatial information, and enable efficient, deeper models for robust computer vision tasks.

Learn how pooling layers in PyTorch downsample feature maps, boost efficiency, and build robust CNNs using max and average pooling techniques for image recognition tasks.

Learn how CNN architectures stack convolution, activation, and pooling layers to extract features and classify images, powering modern computer vision applications.

Learn to build, train, and visualize a CNN for image classification, interpret feature maps and filters, and refine models using data, architecture, and regularization techniques.

Learn how data augmentation expands training data using transformations, helping models generalize better and prevent overfitting by teaching invariance to real-world variations.

Learn to build and apply data augmentation pipelines in PyTorch, creating robust vision models by chaining transforms for training and evaluation using torchvision and DataLoader.

Master advanced CNN training by tackling imbalanced data, optimizing learning rates, and using regularization techniques for robust, reliable, and generalizable deep models.

Master advanced CNN training with PyTorch: tackle overfitting using data augmentation, dropout, batch normalization, learning rate scheduling, and early stopping for robust models.

Explore key vision architectures like LeNet, AlexNet, VGG, ResNet, ConvNeXt, and Vision Transformers, learning their innovations and how they shaped modern computer vision.

Learn to leverage pretrained models with transfer learning through feature extraction and fine-tuning to quickly build accurate computer vision models with limited data.

Learn transfer learning in PyTorch: fine-tune pretrained models, adapt classifiers, handle real-world data challenges, and build custom image classifiers efficiently.

Learn how visualization and attribution methods like feature maximization and Grad-CAM make CNNs explainable, interpretable, and more trustworthy in critical applications.

Learn to blend the content of one image with the style of another using neural style transfer and a pre-trained VGG19 network, creating unique, artistic images with deep learning.

Explore encoder-decoder networks (autoencoders) that compress and reconstruct data, enabling image denoising, anomaly detection, and unsupervised feature learning.

Learn to build and train autoencoders with PyTorch for image reconstruction and denoising, using fully-connected and convolutional architectures on the MNIST dataset.

Discover object detection by learning how models find and classify objects in images using bounding boxes, multi-head networks, and metrics like IoU and mean Average Precision (mAP).

Learn to use and fine-tune PyTorch pretrained models for object detection. Build YOLO from scratch, apply Faster R-CNN, process outputs, and visualize results on real-world images.

Learn how semantic segmentation assigns a class to every pixel using encoder-decoder networks like U-Net, combining context and spatial detail via skip connections for precise image mapping.

Learn semantic segmentation with PyTorch by building and training U-Net models to classify pixels, using theory, demos, and hands-on exercises for real-world image analysis tasks.

Get introduced to Creating Sequence Models and Transformers, meet your instructor, and discover the course objectives.

Discover why the order of data matters, real-world applications of sequence models, key challenges, and foundational techniques for processing and modeling sequential information.

Learn how text is broken into tokens, assigned numerical IDs, and prepared for AI models. Explore tokenization strategies and their impact on model performance and costs.

Learn how to build a tokenizer in PyTorch, transforming text into numerical data for NLP models, and compare simple word tokenization with advanced subword techniques used in modern AI.

Learn how word embeddings transform words into vectors, letting machines grasp meaning, relationships, and analogies for smarter NLP applications like search, recommendations, and translation.

Learn to use pre-trained word embeddings in PyTorch, explore GloVe vectors, visualize relationships, solve analogies, and understand static versus contextual embeddings in NLP.

Learn how RNNs, LSTMs, and GRUs model sequences, overcome memory limitations, and enable applications like text prediction, translation, and time series forecasting.

Discover how RNN, LSTM, and GRU networks in PyTorch excel at predicting sequences, comparing their memory, accuracy, and efficiency in character-level text tasks.

Explore how Seq2Seq models use encoder-decoder architectures to transform sequences, enabling tasks like translation, summarization, and chatbot responses.

Build a Seq2Seq model in PyTorch for tasks like translation and Q&A, learning about encoders, decoders, teacher forcing, and the context vector bottleneck.

Learn how attention mechanisms let AI models focus on relevant input parts, overcoming Seq2Seq bottlenecks and improving tasks like translation and summarization.

Learn to enhance Seq2Seq models in PyTorch using attention mechanisms, robust QA evaluation with EM and F1, and data-centric strategies to address complex language tasks.

Explore Transformer architecture, self-attention, multi-head attention, and positional encoding to understand how modern AI models process language efficiently and contextually.

Discover how Transformers process language by exploring tokenization, contextual embeddings, and attention visualization using HuggingFace and BERT for model interpretability.

Learn to rigorously evaluate Transformer models using metrics like EM, F1, BLEU, and ROUGE, and qualitative error analysis for reliable, real-world language tasks.

Learn to implement and interpret evaluation metrics like EM, F1, Precision@k, and MRR for QA models using Hugging Face, and perform error analysis to guide model improvement.

Learn to fine-tune pretrained transformers for extractive Q&A: prepare data, train with Hugging Face, evaluate using SQuAD, EM, and F1, and apply techniques in real-world scenarios.

Compare RNNs (sequential processing) and Transformers (parallel self-attention) for sequence tasks in AI, focusing on strengths, limits, and real-world applications.

Get an introduction to building Generative Adversarial Networks, meet your instructor, and preview key topics covered in the course.

Explore the shift from data recognition to creation in AI through generative models, their applications, differences from classifiers, and crucial ethical considerations.

Learn how generative AI uses latent spaces and data distributions to transform random noise into structured, creative outputs by mapping simple vectors to complex data like images.

Learn to build a simple generator network in PyTorch that transforms random noise into images, which is a core foundation for Generative Adversarial Networks (GANs) and creative AI models.

Discover how Generative Adversarial Networks use competition between a Generator and Discriminator to create realistic synthetic data through adversarial training.

Learn how to build, train, and evaluate a discriminator in PyTorch as part of a GAN, mastering adversarial training, data preparation, and model evaluation techniques.

Explore GANs: two neural networks, a generator and a discriminator, compete to create realistic data, driving advances in creative AI like image, art, and text generation.

Learn step-by-step GAN training with PyTorch by building, training, and analyzing generator and discriminator models to create realistic images and understand adversarial processes.

Learn to overcome GAN training challenges like mode collapse and instability using techniques such as label smoothing, batch normalization, and LeakyReLU for stable, diverse outputs.

Learn how label smoothing and feature matching stabilize GAN training in PyTorch, alleviating common issues like instability and mode collapse for more reliable, diverse outputs.

Learn to build Deep Convolutional GANs (DCGANs) using convolutional layers, transposed/strided convolutions, and stable training to generate high-quality, realistic images.

Build and train a DCGAN in PyTorch to create realistic images from random noise, mastering adversarial training, architecture, and techniques for creative image generation.

Learn how Conditional GANs generate specific images by combining labels with noise, enabling control for tasks like data augmentation, text-to-image, and image-to-image translation.

Learn to build, train, and use a conditional GAN to generate images of specific classes, gaining control over generative models by conditioning on class labels.

Learn how diffusion models generate images by adding and removing noise in steps, using neural networks, and why they're stable and effective for generative AI.

Discover how the U-Net architecture enables diffusion models to create images with both structure and fine detail, using encoder-decoder paths, skip connections, and time embedding conditioning.

Learn to build, train, and implement a basic diffusion model from scratch, mastering forward noising, U-Net architecture, and image generation through denoising steps.

Learn how diffusion models generate images by reverse diffusion, denoising random noise step-by-step, and explore the trade-off between generation speed and image quality.

Explore the key differences between GANs and diffusion models, speed versus image quality, and learn to choose the right AI tool for any creative image generation task.