Deep Learning Foundations for AI Research
Master the theoretical and practical aspects of deep neural networks, from backpropagation to modern architectures.
Dr. Sarah Chen
Program Director & Lead Instructor
$45K
Full program tuition · Payment plans available
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What You'll Learn
Prerequisites
- •Strong Python programming skills
- •Linear algebra and calculus fundamentals
- •Basic machine learning knowledge
- •Familiarity with probability theory
Curriculum
6 modules · 0 minutes total
Foundations of Neural Networks
Perceptrons, activation functions, forward and backward propagation from first principles.
Convolutional Neural Networks
Convolution operations, pooling, ResNet, EfficientNet, and vision architectures.
Recurrent Networks & Sequence Models
LSTMs, GRUs, and sequence-to-sequence architectures for temporal data.
Attention Mechanisms & Transformers
Self-attention, multi-head attention, positional encoding, and the transformer architecture.
Generative Models: GANs & Diffusion
Generative adversarial networks, variational autoencoders, and diffusion models.
Research Methods & Paper Writing
Experimental design, ablation studies, reproducibility, and academic writing for top venues.