Introduction to Deep Learning (and Neural Networks)

Colleagues, the Introduction to Deep Learning program will equip you in all the important concepts relating to deep learning models and how they give rise to the recent results in AI. We use guided examples and discuss a variety of practical applications, all accompanied by animations and visualizations. We also cover recent breakthroughs in deep learning research. This course will demystify the models that underpin the recent AI revolution and provide a solid foundation for further learning. Skill-based training modules include: 1) Fundamentals, 2) Perceptron: Weights, Biases, Activation Functions, 3) Multi-neuron Networks : XOR and nonlinearity, and 4)  Learning: Gradient Descent. After taking this course you will understand What deep learning is and how it  differs from other types of machine learning and artificial intelligence, How deep learning models use neural networks to make computations, What types of problems deep learning models can be used to solve, Types of data needed to train deep learning models, Variety of inputs deep  learning models receive and solutions they produce, Advantages that deep learning can offer over traditional machine learning, Why multi-neuron networks are able to solve complex problems, How neural networks use gradient descent and back-propagation to learn to make predictions.

Enroll today (individuals & teams welcome): https://tinyurl.com/phdve8xj 

Much career success, Lawrence E. Wilson - Online Learning Central