Deep Learning Professional — Study Guide

A visual, example-driven study guide for the Deep Learning Professional certification. Build a working mental model of neural networks: the forward pass, backpropagation and the chain rule, activations and losses, optimizers and schedules, initialization and the gradient pathologies, the full regularization and normalization toolkit, and the two great architecture families — CNNs and RNNs/LSTMs — plus embeddings, transfer learning, and the practicalities of GPUs and batch size. Diagrams, comparison tables, and a fully worked tiny forward-and-backward pass.

Key facts

• Course price: $195 (one-time payment)
• Field: Artificial Intelligence & LLMs
• Prepares for: Deep Learning Professional
• 5 modules · 15 lessons · about 4 hours of lessons
• Self-paced, online, illustrated lessons

Course outline

1. Neurons, Networks & the Forward Pass — From the perceptron to a multilayer network: how a forward pass turns inputs into predictions, and why non-linearity is essential.
   • The Perceptron and the MLP
   • The Forward Pass, Step by Step
   • Softmax and Making Predictions
2. Backpropagation & the Chain Rule — How a network learns: losses, the chain rule, the backward gradient flow, and a fully worked tiny forward-and-backward pass.
   • Loss Functions: MSE and Cross-Entropy
   • The Chain Rule and the Backward Pass
   • Worked Example: A Tiny Forward + Backward Pass
3. Activations, Optimizers & Learning Rates — The levers that make training work: activation functions, the optimizer family from SGD to Adam, and learning-rate schedules — with the training loop.
   • Activation Functions Compared
   • Optimizers: SGD, Momentum, RMSprop, Adam
   • Learning-Rate Schedules and Warmup
4. Training Deep Nets that Actually Work — Initialization, the gradient pathologies, regularization, and normalization — plus the full training loop that ties it together.
   • Initialization and the Gradient Pathologies
   • Regularization: Dropout, Weight Decay, Early Stopping
   • Normalization and the Training Loop
5. CNNs, RNNs & Transfer Learning — The two great architecture families — convolutional and recurrent — plus embeddings, transfer learning, augmentation, and the GPU/batch-size realities.
   • Convolutional Neural Networks
   • RNNs, LSTMs and GRUs for Sequences
   • Embeddings, Transfer Learning and the Hardware Reality