What will you learn in A deep understanding of deep learning (with Python intro) Course
Grasp the theory and math behind deep learning: from gradient descent to regularization, weight initialization, transfer learning, and autoencoders.
Build and analyze models like feedforward neural networks, CNNs, RNNs, and GANs using PyTorch.
Learn Python from scratch if needed, with an extensive appendix (8+ hours) covering basics for beginners.
Use Google Colab (cloud-based notebooks with free GPU) for all coding and experimentation.
Improve models via hyperparameter tuning, dropout, batch normalization, and understanding why neural networks work or fail. ([turn0search0])
Program Overview
Module 1: Deep Learning Fundamentals & Math Theory ⏳ ~10–12 hours
Topics: Core calculus and optimization (gradient descent, loss functions), layer activations, network architectures, regularization, weight initialization.
Hands‑on: Python and math walkthroughs in Colab, code-based visualization of training curves and parameter effects.
Module 2: Building Neural Architectures in PyTorch
⏳ ~8–10 hours
Topics: Construct neural networks using PyTorch; build CNNs, RNNs, and generative models including autoencoders and basic GANs.
Hands‑on: Implement models from scratch, visualize filters, generate sample outputs, and experiment with transfer learning.
Module 3: Advanced Optimization, Regularization & Practical Performance
⏳ ~5 hours
Topics: Learning rate schedules, batch norm, dropout, optimizer choices, parameter tuning, and overfitting avoidance strategies.
Hands‑on: Tune and retrain models with different settings; evaluate model behavior and runtime efficiency.
Module 4: Python Refresher & Supporting Tools
⏳ ~8 hours
Topics: Python essentials for beginners: data structures, functions, NumPy, plotting, Colab environment setup.
Hands‑on: Guided coding exercises to prep for deep learning modules.
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Job Outlook
Equips learners for ML engineer roles, deep learning practitioner roles, or researcher-adjacent jobs demanding strong model intuition.
Applicable industries include AI startups, autonomous systems, medical imaging, fintech predictive modeling, and research labs.
Knowledge of model internals and tuning makes you adept at roles beyond just implementation—ideal for driving new service ideas or interpreting model behavior.
Salary potential: ML/AI engineers with deep learning specialization often earn ₹15–30 LPA in India and $110K–$160K+ in the U.S.
Specification: A deep understanding of deep learning (with Python intro)
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