Overview
Syllabus
) Introduction.
) What is Deep Learning.
) Introduction to Neural Networks.
) How do Neural Networks LEARN?.
) Core terminologies used in Deep Learning.
) Activation Functions.
) Loss Functions.
) Optimizers.
) Parameters vs Hyperparameters.
) Epochs, Batches & Iterations.
) Conclusion to Terminologies.
) Introduction to Learning.
) Supervised Learning.
) Unsupervised Learning.
) Reinforcement Learning.
) Regularization.
) Introduction to Neural Network Architectures.
) Fully-Connected Feedforward Neural Nets.
) Recurrent Neural Nets.
) Convolutional Neural Nets.
) Introduction to the 5 Steps to EVERY Deep Learning Model.
) 1. Gathering Data.
) 2. Preprocessing the Data.
) 3. Training your Model.
) 4. Evaluating your Model.
) 5. Optimizing your Model's Accuracy.
) Conclusion to the Course.
Taught by
freeCodeCamp.org