About this course
About this Course
This course is targeted at individuals with some programming or data science background who wants to get started in the AI domain. We will would cover deep learning based AI techniques from first principles to help you get a grasp of how it works from the ground up. I will also cover various aspects of implementation, high level AI programming frameworks like Keras and Tensorflow as well as the basics how to tune an AI and improve the learning outcomes. I will also broadly cover the problems that deep learning is suited and also cover the topic of handwriting and specific image recognition using in-depth.
This course will be taught in Python 3.0 + other high level AI programming framework Attendees are expected to be able to program in Python and have a fair understanding of math and stats.
Overview
There is a lot of FOMO driving AI in every sector now, and a lot of hype surrounding AI. Many startups have rebranded themselves into one that is “AI-driven”, and Chinese and American investors are pouring in huge amounts of money into the space. On top of that, FANG and BAT are extremely avid in seeking acquisitions (e.g. DeepMind, DNNResearch), as well as going on technology demonstration sprees. Most notably, we have: Google’s AlphaGo beating Ke Jie, the world’s top Go player, and Baidu’s Xiaodu AI trumping humans in facial and voice recognition.
Everyone wants in on a piece of the action and a slice of the AI pie.
AI is definitely something you should at least have a sensing about because its impacts on each individual are definitely real. It will change our ways of life very drastically and reshape industries. It could be the best and worst of times. From self-driving cars to personal assistants, data analysis, and the creation of art, it will redefine the ways we live and learn, and it will bring with it many opportunities we
Start getting your hands dirty with code and data today!
Expectations & Goals
- Keras
- Tensorflow
- Forward propagation
- Backpropagation
- Pandas
- Matplotlib
- Num.py
- Activation functions: Sigmoid, Linear, ReLU
- Hyper parameters tuning
- Loss function
- Confusion matrix
- Stochastic gradient descent
- Data augmentation
- Overfitting / Underfitting
- Testing and Validation
What am I going to get from this course?
- Develop using iPython notebooks
- Understand AI use cases, development in the space & open questions in AI
- Implement a simple neural network from first principles
- Understand the problem domain of image recognition
- Work with networks likes CNN / ResNet
- Appreciate the intricacies and complexity that goes into deep learning algorithm
- Hands on in testing & validation
- Hands in tuning AI algorithms
- Manipulate data with num.py and pandas
- Visualize data with Matplotlib
- Set up a GPU environment
Required Materials
Laptop. The course will walk you through installing the necessary free software & there would be about an additional $250 spend on GPU compute environments.
Basic Programming Knowledge (Preferably Python 3.0).
At least high school level Math & Stats
Prerequisites
None