Introduction to Deep Learning

Welcome to the introduction to deep learning course! ​ This course is designed to provide you with a solid foundation in the fundamentals of deep learning. Throughout this course, you will learn about the basic building blocks of deep learning, including basics of machine learning, convolutional neural networks, and natural language processing. You will also gain an understanding of how deep learning algorithms are used to solve a variety of real-world problems, such as image classification, natural language processing and a few advance approaches such as GANs. ​

By the end of the course, you will have a solid understanding of the core concepts and techniques used in deep learning, as well as hands-on experience building and training your own deep learning models using popular frameworks such as PyTorch and Catalyst ​

Dr. Sergey Plis is the instructor for this course, bringing his expertise of an active researcher in the fields of neuroscience and computer science. He has extensive experience applying machine learning algorithms to the analysis of brain imaging data. He is also an experienced educator, having taught numerous courses in data science, machine learning, and deep learning at the graduate and undergraduate levels. ​

The hands-on part of the course has been developed by Mrinal Mathur, a seasoned machine learning engineer with experience building and deploying machine learning models for a variety of industries. Mrinal has a deep understanding of the underlying mathematical and statistical concepts that power deep learning algorithms, and he has a passion for teaching others about the exciting possibilities of this field. ​

Together, we have designed a comprehensive and engaging course that will provide you with the knowledge and skills you need to succeed in the exciting field of deep learning. ​

Introduction to Deep Learning

1. Introduction

Lecture Slides

1. Introduction to Collab
2. Pandas (optional)

Lecture Slides

3. Numpy

Machine Learning

2. Foundations of Machine Learning

1. Calculus and Optimization

   Lecture Slides

2. Linear Regression/Classification

   Lecture Slides

3. Perceptron

   Lecture Slides

3. Automatic Differentitation

Lecture Slides

Colab Notebooks

• Automatic Differentitation programming

4. Practice for Automatic Differentiation

Lecture Slides

5. Pytorch

Colab Notebooks

• Introduction to Pytorch and Catalyst

6. Model Comparision

Lecture slides

Colab Notebook

• Model Comparision

Computer Vision

7. Computer Vision and Image Processing

Lecture Slides

Colab Notebook

• Introduction to Computer Vision

8. Convolution Neural Network

Lecture Slides

Colab Notebook:

• Introduction to CNNs

9. Image Classification

Lecture Slides

Colab Notebook

• Classification in Computer vision

10. Skip Connections and ResNets

Colab Notebook

• Intro to Skip Connections and ResNets

11. Segmentation

Lecture Slides

Colab Notebook

• Image Segmentation

12. Auto-Encoders

Lecture Slides

Colab Notebooks:

• Auto-Encoders
• Variational Autoencoders

13. Generative Adversarial Nets

Lecture Slides

Colab Notebook:

• Generative Adversarial Nets

14. Regularization

Lecture Slides

Natural Language Processing

15. Introduction to NLP

Lecture Slides

Colab Notebooks:

• Introduction to NLP

16. Recurrent Neural Networks

Colab Notebooks:

• RNNs

17. LSTM and GRU

Lecture Slides

Colab Notebook:

• LSTM and GRU in pytorch

18. Seq2Seq2

Lecture Slides

Colab Notebooks:

• seq2seq models

19. Attention is all you need!

Lecture Slides

Colab Notebooks:

• Attention Mechanisms

20. Transformers

Lecture Slides

Colab Notebooks:

• Transformers

Advance Topics (To be added)

1. [Graph Neural Networks]
2. [Reinforcement Learning]
3. [Meta Learning]
4. [Adversarial Learning]
5. [Transfer Learning]
6. [Self Supervised Learning]
7. [Few Shot Learning]
8. [Active Learning]
9. [Multi Task Learning]
10. [Multi Modal Learning]
11. [Domain Adaptation]
12. [Continual Learning]
13. [Causal Learning]