Envision a future in which deep learning models can learn with little or no help from humans, are flexible to changes in their environment, and can solve a wide range of reflexive and cognitive problems. The Deep Learning course is our foundational program that will help you understand the capabilities, challenges, and consequences of deep learning and prepare you to participate in the development of leading-edge AI technology. It provides a pathway for you to gain the knowledge and skills to apply machine learning to your work, level up your technical career, and take the definitive step in the world of AI.

Who should attend? 

• Software Engineers
• Enthusiasts about AI

Knowledge and Benefits:

After completing the program, participants will be able to master the following:

• Build and train deep neural networks
• Identify key architecture parameters, implement vectorized neural networks and deep learning to applications
• Train test sets, analyze variance for DL applications, use standard techniques and optimization algorithms, and build neural networks in TensorFlow
• Build a CNN and apply it to detection and recognition tasks, use neural style transfer to generate art, and apply algorithms to image and video data
• Build and train RNNs, work with NLP and Word Embeddings, and use HuggingFace tokenizers and transformer models to perform NER and Question Answering

• Introduction to Deep Learning
• What is a Neural Network?
• Supervised Learning with Neural Networks
• Why is Deep Learning taking of ?

• Neural Networks Basics
• Binary Classification
• Logistic Regression
• Logistic Regression Cost Function
• Gradient Descent
• Derivatives
• More Derivative Examples
• Computation Graph
• Derivatives with a Computation Graph
• Logistic Regression Gradient Descent
• Gradient Descent on m Examples
• Vectorization
• More Vectorization Examples
• Vectorizing Logistic Regression
• Vectorizing Logistic Regression's Gradient Output
• Broadcasting in Python
• A Note on Python/Numpy Vectors
• Quick tour of Jupyter/iPython Notebooks
• Explanation of Logistic Regression Cost Function

• Shallow Neural Networks
• Neural Networks Overview
• Neural Network Representation
• Computing a Neural Network's Output
• Vectorizing Across Multiple Examples
• Explanation for Vectorized Implementation
• Activation Functions
• Why do you need Non-Linear Activation Functions?
• Derivatives of Activation Functions
• Gradient Descent for Neural Networks
• Backpropagation Intuition
• Random Initialization

• Deep Neural Networks
• Deep L-layer Neural Network
• Forward Propagation in a Deep Network
• Getting your Matrix Dimensions Right
• Why Deep Representations?
• Building Blocks of Deep Neural Networks
• Forward and Backward Propagation
• Parameters vs Hyperparameters
• What does this have to do with the brain?