Deep Learning

In this course, we will talk about the basics of neural networks and deep learning, how to use deep learning ConvNets, transfer learning, transformers - a deep learning architecture that has revolutionized the traditional NLP.

Deep Learning syllabus

📊 In this course, you’ll learn:

Neural Network Foundations with the TensorFlow

Using the TensorFlow to build simple, multiple, and multivariate regression models

Convolutional Neural Networks, Transfer Learning

Understanding origins of and theory behind distributed representations and word embeddings

Recurrent Neural Networks

Understanding the seq2seq architecture and Autoencoders

Reviewing the most popular transformers, working with implementations both based on the vanilla architecture and on popular libraries: (Hugging Face and TensorFlow Hub)

Generative Adversarial Networks (GANs), discussing the various GAN architectures

Deploying Deep Learning Model in Heroku using flask

Introduction to Docker, Flask, FastAPI

📍This course focuses on balancing important theory concepts with practical hands-on exercises and projects that let you learn how to apply the concepts in the course to your own data sets.

Students will work on state-of-the-art projects in NLP and Computer Vision tasks such as Azerbaijani novel generation, object detection, image segmentation, and generating photo-realistic images.

Projects that you will add to your portfolio:

Recognizing CIFAR-10 images with deep learning

Creating custom Azerbaijani embeddings using Gensim

Azerbaijani novel generation using Recurrent Neural Networks

Human Face Detection

Fake News Detection with BERT

Text Summarizer in Azerbaijani

Generate Human Faces with DCGAN

Multi-Class Azerbaijani news classification

Building Restaurant Recommendation System

Topic Modeling with BERT

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