Deep Learning Using Python
Development Academy of the Philippines
Enrollment is Closed
Course Overview
Deep Learning is a more advanced implementation of machine learning. By having knowledge in deep learning, data scientists may accomplish complex machine learning tasks including artificial intelligence that may provide deep levels of perceptual recognition. This course equips participants with practical knowledge in Deep Learning using Python. The course covers widely used Python libraries useful in efficiently performing Deep Learning, together with various scenarios and algorithms that go along with it.
What You Will Learn
Upon completion of this course, the learners are expected to:
- analyze the different concepts of Deep Learning and its use-cases; and
- perform Python algorithms and accomplish Deep Learning tasks, applying widely used libraries on realistic examples.
Requirements
You will need a computer or laptop with Python installed. Computer or laptop requirements are:
- 8GB RAM and Core i5, or get a production type, download and install Anaconda package tool.
Note to SPARTA scholars: Upon enrollment, you will have 6 months to finish a SPARTA course. Failure to complete the course in 6 months and/or inactivity for 3 months will result in course access revocation.
Course Instructor
Ronrick Da-ano
Subject Matter Expert
Course Content
Week 1: Introduction to Deep Learning using Python
4 Videos | 3 Activities
3 Videos
- Welcome to the course!
- What is Deep Learning?
- Mathematical Building Blocks of Neural Networks
- Getting Started with Neural Networks
3 Activities
- Try it out: Padlet Activity
- Let's Recall: Anagram
- Peer-Graded Assignment: Implementing Deep Learning
Week 2: Deep Learning for Computer Vision
4 Videos | 3 Activities
4 Videos
- Training a Convnet from Scratch on a Small Dataset
- Using a Pre-Trained Convnet
- Visualizing What a Convnet Learn (2 Videos)
3 Activities
- Try it Out: Whack-a-Mole/li>
- Let's Recall: Cloze Activity
- Peer-Graded assignment: Pre-Trained Network
Week 3: Deep Learning For Texts and Sequences
4 Videos | 3 Activities
4 Videos
- Working with Text Data
- Understanding Recurrent Neural Networks (RNNs)
- Advance Use of RNNs
- Sequence Processing with Convnets
3 Activities
- Try it out: Padlet Activity
- Let's Recall: Match-Up
- Peer-Graded Assignment: Reuters Dataset
Week 4: Advanced Deep Learning Practices
2 Videos | 3 Activities
2 Videos
- Introduction to Functional API
- Inspecting and Monitoring Deep-Learning Models Using Keras Callbacks, and Tensorboard
3 Activities
- Ponder and Prove: Padlet Activity
- Let’s Recall: Fact or Bluff
- Peer-Graded Assignment: Advanced Deep Learning Practices
Week 5: Generative Deep Learning
5 Videos | 3 Activities
5 Videos
- Text Generation with LSTM
- Neural Style Transfer
- Generating Images with Variational Autoencoders
- Introduction to Generative Adversarial Networks (GAN)
- Key Takeaways
3 Activities
- Try it out: Word Cloud
- Let’s Recall: Hangman
- Capstone Project: CNN Architecture