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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.
📊 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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