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…
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 at theis our foundational program that will help you understand the capabilities, challenges, and consequences of deep learning. This course prepares you to participate in the development of leading-edge AI technology. It provides a pathway for you to gain the knowledge and skills necessary to apply machine learning to your work, advance your technical career, and take a definitive step into the world of AI.
Course Objectives
By the end of the course, participants will be able to:
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
Course Outline
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?
Schedule & Fees
2026 Schedule & Fees
There are no public sessions remaining for the course this year. Please contact us if you wish to have a customized in-house session, or to find out how an additional public session can be scheduled.