Artificial intelligence is profoundly changing our lives. Fueled by recent advances in deep learning, fields such as computer vision, speech recognition, and pattern recognition are being transformed. Advances in technology are redefining applications in human computer interaction, advanced manufacturing, social networking, autonomous systems, security, and entertainment. This program aims to provide hands on experience to the faculty, research scholars and students in the field of Artificial Intelligence/ Machine Learning & Deep Learning. The programme will help the participants to learn the principles of Machine Learning & Deep learning and understand how to apply deep learning & Machine Learning for solving problems.
Target Audience: Faculty, Research Scholars, UG/PG Students
Course Outline :
- Overview of machine & deep learning
- Python libraries -introduction & installation
- Machine learning:
- Supervised & unsupervised algorithms
- Bias/variance, over-fitting / under-fitting
- Regularization, optimization, Gradient descent
- ML applications hands-on
- Deep learning :
- Deep neural networks (DNN)
- Convolutional neural networks (CNN)
- Recurrent neural networks (RNN)
- Long short term memory networks (LSTM)
- Auto encoders (AE)
- Generative adversarial networks (GAN)
- DL applications handsonusing Anaconda,Tensor flow & Keras
Smt. Indira Gandhi College of Engineering, Navi Mumbai
Department of Electronics and Telecommunication Engineering Organized Two days’ Workshop on AI and Deep Learning
16th and 17th March 2019
Pre-requisite: Workshop Lab Material with Azure Notebooks
Our workshop notebooks based on Tensorflow and Keras can be practiced using free online platform Azure Notebooks.
- Account creation: To get started with Azure notebooks you have to log-in to https://notebooks.azure.com/ using the Microsoft account. If you have an existing Microsoft account you can use it for logging in to Azure Notebooks. Otherwise you can create a Microsoft account in https://account.microsoft.com/account
- Getting started with Azure Notebook: After logging-in, you can click on “Library” tab to go into your library where you can “Add a new Library”. After creating a library, you can add new file which can be Notebooks of Python, R or F#.
- Using Existing Repos: If you have the URL of some existing repositories, you can clone them into your library. For example the below library can be cloned by visiting the URL. https://notebooks.azure.com/jakevdp/libraries/pythondatasciencehandbook After visiting the URL, you can press “Clone” to copy the entire folder/repository into your library. Then any modification can be done by opening, editing and running those .ipynb notebooks.
- Our Notebooks: During the time of workshop, we will share links to our notebook repository for your practice.
Pre-requisite: Workshop Lab Material with Anaconda Python in your Laptop/PC (Optional), If you cannot use Azure Notebook services during the workshop, our same lab material can be used by installing Anaconda Python and necessary packages.
Anaconda 5.1 – Python Version 3.6 You can Download it from the link freely from the given link.
Python Libraries: numpy, pandas, sklearn, nltk, tensorflow, keras and for plotting matplotlib
Installation of Packages:
- For installing the above mentioned python packages please follow the procedure mentioned below.
- For installing new packages, system must be connected through internet.
- Press start in windows and search for “Anaconda Prompt”. When you will click on the application, a prompt window will be open,wait for few seconds.
- You can check/ install any package in Anaconda Python by.. pip install package_name
Ex: pip install numpyand press Enter key after typing these commands.
Note. If you are installing numpy like this, it will give requirement already satisfied message on the prompt. That means package is already installed in the anaconda python.
Smt. Indira Gandhi College of Engineering, Navi Mumbai
Department of Electronics and Telecommunication Engineering Organized Two days’ Workshop on Artificial Intelligence/ Machine Learning and Deep Learning 16th and 17th March 2019
Workshop Schedule:
| Day 1 (16th March 2019, Saturday) | |
|---|---|
| 8.00-9.00 | Registration |
| Session 1 9.00-11.00 | Motivation Foundations and Terminology of Deep Learning |
| Session 2 11.30-1.30 | Machine Learning Basics Linear Regression, Logistic Regression Gradient Descent |
| Session 3 2.15-4.00 | Designing & Optimizing Neural Network Model Building Deep Models and Hyper parameter Tuning |
| Session 4 4.30-6.30 | Deep Learning Hacks System/project level tricks and regularization strategies |
| Day 2 (17th March 2019, Sunday) | |
| Session 1 9.00-11.00 | Convolutional Neural Network Computer Vision with CNNs |
| Session 2 11.30-1.30 | Recurrent Neural Network Sequence Modelling with RNNs, generative modelling in Deep Learning |
| Session 3 2.15-4.00 | Advanced Learning Topics Hands-On Labs |
| Session 4 4.30-6.30 | Assessment |