Dive into the world of Deep Learning with this comprehensive crash course designed for absolute beginners. Explore fundamental concepts and terminology without any prior programming experience required. Gain a solid understanding of Neural Networks, Machine Learning constructs like Supervised, Unsupervised, and Reinforcement Learning, and various Neural Network architectures. Learn key ideas behind deep learning, including activation functions, loss functions, optimizers, and hyperparameters. Discover the five essential steps in every Deep Learning model, from data gathering to model optimization. By the end of this 1-2 hour course, developed by Jason Dsouza, acquire a strong foundation in Deep Learning concepts, preparing you for further exploration in this exciting field of Artificial Intelligence.
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
