Fundamentals of Deep Learning
- Created By raju2006
- Last Updated December 20th, 2023
- Overview
- Prerequisite
- Audience
- Audience
- Curriculum
Description:
Master the world of Deep Learning with our "Deep Dive into Deep Learning" training. This course provides a strong foundation in neural networks and their pivotal role in deep learning for Artificial Intelligence perception. With hands-on experience in TensorFlow, you'll create Convolutional Neural Networks (CNN) for image recognition and Recurrent Neural Networks (RNN) for natural language processing.
Long Description:
This Fundamentals of Deep Learning class will provide you with a solid understanding of the technology that is the foundation of artificial intelligence. We will explore deep neural networks and discuss why and how they learn so well.
We will begin with understanding the context of deep learning and its relationship to machine learning and artificial intelligence. We’ll then examine shallow neural networks in order to better understand the advantages of deep neural networks, which enable deep learning. Specifically, we’ll delve into Convolutional Neural Networks and Recurrent Neural Networks, and explore uses cases for each. We will use Keras and TensorFlow as our tools to apply deep learning on real-world datasets. We’ll look at some of the amazing things that deep learning can do and how to spot opportunities for deep learning.
Course Code/Duration:
BDT5 / 1 Day
Learning Objectives:
After this course, you will be able to:
- Install Anaconda on a personal computer.
- Install TensorFlow
- Understand deep learning in the context of machine learning and AI
Understand neural networks - Understand the architectural differences between shallow and deep neural
networks - Understand deep Convolutional and Recurrent Neural Networks
- Use Pandas to work with unstructured data
- Use Scikit-learn to evaluate model performance
- Understand use cases for Convolutional Neural Networks
- Understand use cases for Recurrent Neural Networks
- Understand the relationship between TensorFlow and Keras for applying deep
learning
- Basic Python Programming
- Anyone willing to develop a solid foundation for data science and learn exploratory data analysis.Anyone who is not that comfortable with coding but who is interested in Deep Learning and wants to apply it easily on datasets.Any data analysts who want to level up in Deep Learning.
- Anyone willing to develop a solid foundation for data science and learn exploratory data analysis.Anyone who is not that comfortable with coding but who is interested in Deep Learning and wants to apply it easily on datasets.Any data analysts who want to level up in Deep Learning.
Course Outline:
- Course Introduction
- Install Anaconda
- Milestone 1: Learn how to use Jupyter Notebooks
- Essential libraries
- TensorFlow
- Keras
- Pandas
- Scikit-learn
- Neural networks
- Deep learning
- Convolutional Neural Networks
- Architecture
- Use cases
- Recurrent Neural Networks
- Architecture
- Use cases
- Milestone 2: Work with unstructured data using Pandas
- Milestone 3: Apply deep learning using Keras/TensorFlow
- Conclusion: Deep Learning opportunities, next steps
Structured Activity/Exercises/Case Studies:
- Milestone Project 1: Install and setup Anaconda/Jupyter Notebooks
- Milestone Project 2: Work with unstructured data using Pandas
- Milestone Project 3: Apply deep learning using Keras/TensorFlow
Training material provided:
Yes (Digital format)
