Overview

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Build practical deep learning skills and learn to build, train, evaluate, and optimize neural networks with PyTorch. You’ll begin by distinguishing machine learning from deep learning and exploring perceptrons, neural networks, and their role in real-world AI systems. You’ll then set up Jupyter Notebooks, Google Colab, and PyTorch before working with tensors, gradients, hidden layers, and transfer learning. Through guided coding exercises and case studies, you’ll prepare image datasets and develop classification models using MNIST, CIFAR-10, and CIFAR datasets. You’ll also apply CNNs to text classification, compare loss functions, and generate context-aware text with transformers. Advanced lessons cover language models, sequence generation, encoder-decoder architectures, attention-based translation, training pipelines, and performance evaluation. You’ll extend these techniques to tabular prediction through preprocessing and feature engineering, then compare collaborative and content-based filtering for recommender systems. Designed for learners seeking practical AI, deep learning, or data science skills, this course uniquely progresses from beginner-friendly concepts to advanced vision, NLP, attention, and recommendation projects. Enroll to turn deep learning theory into working models across image, text, structured data, and recommendation tasks.

Syllabus

Foundations of Deep Learning
- This module introduces learners to the core principles of machine learning and deep learning, exploring their methods, applications, and the evolution from perceptrons to deep neural networks.
Getting Started with Tools
- This module provides hands-on exposure to essential coding platforms, tools, and frameworks like Jupyter, Google Colab, and PyTorch, while building foundational skills with tensors, gradients, and basic networks.
Image Classification in Action
- This module explores image classification through practical case studies, guiding learners to preprocess, transform, and visualize datasets, then build, train, and test deep neural networks on benchmarks like MNIST and CIFAR-10.
Deep Learning for Text
- This module introduces natural language processing (NLP) tasks, including text classification with CNNs and text generation with transformers, focusing on preparing textual data, building models, and evaluating results.
Advanced NLP with Attention
- This module dives deeper into NLP using attention-based architectures, covering sequence-to-sequence models for text translation, encoder-decoder frameworks, and best practices for training and evaluation.
Beyond Vision & Text
- This module extends deep learning applications to structured tabular data and recommender systems, demonstrating predictive modeling and approaches like collaborative and content-based filtering.