IISC Deep Learning Course: What You Need to Know

IISC Deep Learning Course: What You Need to Know

If you’re interested in taking a deep learning course, the Indian Institute of Science (IISC) offers a great option. Here’s what you need to know about the course, including what you’ll learn and what you’ll need to get started.

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Introduction

Deep learning is a subset of machine learning that deals with algorithms inspired by the structure and function of the brain. Deep learning is one of the hottest fields in artificial intelligence right now, and is behind some of the most impressive achievements in AI, such as facial recognition and self-driving cars.

If you’re interested in getting started in deep learning, the Indraprastha Institute of Information Technology (IISC) in Delhi offers a Deep Learning Course that covers all the basics. In this article, we’ll give you an overview of what the course entails and what you can expect to learn.

What is Deep Learning?

Deep learning is a subset of machine learning that uses algorithms to model high-level abstractions in data. By doing so, deep learning enables computers to automatically improve their ability to perform tasks through experience, without being explicitly programmed.

Deep learning is a neural network-based approach that learns from data in an unsupervised or semi-supervised manner. Neural networks are composed of a large number of interconnected processing nodes, or neurons, that can learn to recognize patterns of input data. The interconnected nodes form layers of representation, with each successive layer extracting higher-level features from the data.

Deep learning algorithms have been used for many years, but recent advances in computational power and training data have made them much more powerful and effective. Deep learning is currently being used for a variety of tasks, including image recognition, speech recognition, natural language processing, and driving autonomous vehicles.

What You Need to Know

The IISC Deep Learning Course is designed to provide you with a comprehensive overview of deep learning, from the basics of neural networks to more advanced topics such as transfer learning and reinforcement learning. The course covers all the major deep learning frameworks, including TensorFlow, PyTorch, and Keras. You will also learn how to apply deep learning to a variety of real-world tasks, such as image classification, object detection, and text generation.

The Course Outline

The Indian Institute of Science (IISC) is offering a course on deep learning from Dec. 9-11, 2020. The course will cover the basics of deep learning, including neural networks and convolutional neural networks. It will also cover more advanced topics such as sequence models, reinforcement learning, and unsupervised learning.

The Instructors

All of the instructors for the IISC Deep Learning course are experienced professionals in the field. You can expect to learn from some of the leading experts in the field, including:

-Andrej Karpathy: Director of Artificial Intelligence at Tesla, and a research scientist at Stanford University. He is also the creator of the popular blog, “Deep Learning 101.”
– Geoffrey Hinton: A cognitive psychologist and computer scientist who is known as one of the “godfathers” of artificial neural networks. He is currently a Distinguished Emeritus Professor at the University of Toronto.
-Yann LeCun: Director of AI Research at Facebook, and a Silver Professor of Computer Science, Data Science, Neural Science, and Electrical Engineering at New York University.

The Course Format

This guide covers the format of the IISC Deep Learning course. The course is designed to be taken over a period of four weeks, with each week consisting of three one-hour lectures. In addition, there will be two one-hour practical sessions each week.

The Course Material

The IISC Deep Learning Course covers a variety of topics related to deep learning. The course is designed to be accessible to a wide range of students, from those with no prior experience in machine learning to those who are already familiar with the basics of deep learning.

The course material is divided into four main sections:

1. Introduction to Deep Learning: This section covers the basics of deep learning, including an overview of the different types of neural networks and how they are used.

2. Fundamentals of Deep Learning: This section covers the fundamental concepts and methods used in deep learning, such as gradient descent and backpropagation.

3. Advanced Deep Learning Methods: This section covers more advanced topics in deep learning, such as convolutional neural networks and recurrent neural networks.

4. Applications of Deep Learning: This section covers some applications of deep learning, such as computer vision and natural language processing.

The Course Schedule

This 12-week course meets twice a week for three hours each session. The first half of each class will be a lecture by the instructor, and the second half will be a laboratory session.

The course schedule is as follows:

Week 1: Introduction to Machine Learning and Deep Learning
Week 2: Deep Learning Basics
Week 3: Neural Networks I
Week 4: Neural Networks II
Week 5: Convolutional Neural Networks I
Week 6: Convolutional Neural Networks II
Week 7: Recurrent Neural Networks I
Week 8: Recurrent Neural Networks II
Week 9: Unsupervised Learning and Generative Models I
Week 10: Unsupervised Learning and Generative Models II
Week 11: Reinforcement Learning I
Week 12: Reinforcement Learning II

The Course Requirements

In order to successfully complete the IISC Deep Learning Course, there are a few requirements that you will need to fulfill. First, you must have a strong working knowledge of linear algebra and probability. You should also be familiar with basic optimization methods, and be able to code in either Python or MATLAB. Finally, you will need to complete all of the assignments and projects for the course.

The Course Grading

The IISC Deep Learning Course is graded pass/fail. A passing grade requires a score of 60% or higher on the final exam.

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