Deep Learning syllabus

DL Syllabus

DL Syllabus

What is TensorFlow keras

Introduction to neural networks

Multi-layer perceptron: first example of a network

Activation functions

Backpropagation

Regularization methods

Simple linear regression using TensorFlow Keras

Logistic regression on the MNIST dataset using TensorFlow Keras

ConvNets in TensorFlow: The math behind CNN

Other CNN architectures

Recognizing CIFAR-10 images with deep learning

Word embedding fundamentals (Word2Vec and GloVe)

Word2Vec models: CBOW and Skip-gram arthitectures

Creating custom Azerbaijani embeddings using Gensim

Language model-based embeddings: BERT, ULMFiT, GPT

How Recurrent Neural Networks works?

Long short-term memory (LSTM)

Gated recurrent unit (GRU)

RNN variants

Text cleaning

Use case: Azerbaijani novel generation using Recurrent Neural Networks

Encoder-decoder architecture – seq2seq

Attention mechanism

Use case: Building seq2seq network with/without attention mechanism for machine translation (Az - Eng)

Transformers: Understanding architecture

Language models: BERT, Reformer, GPT-2,3, RoBERTa, ELECTRA and etc.

Using Hugging Face models

Introduction to Generative Adversarial Networks

DCGAN for MNIST digits

Flow-based models for data generation

Diffusion models for data generation

Introduction to Docker, Flask, FastAPI

Deploying Deep Learning Model in Heroku using flask

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