Generative AI Training In Hyderabad

• Intro – Programing Cycle of Python, Python IDE, Variables, Data type.
• Operator, Decision making & Control flow statements.
• Mathematical functions, Random function, Trigonometric function.
• Strings, Number type conversion.
• List, Tuples, Sets, Dictionary – Functions & Methods.
• Date & time, Calendar module and Time module.
• Functions, Modules, Packages.
• Files in Python, Directories and Exception Handling
• OOPS concepts, Class, Objects, Inheritance, Overriding methods like _init_, Overloading operators, Data hiding.
• Regular Expressions – match function, search function, matching vs searching
  Regular exp modifiers and patterns.
• Introduction to Flask framework, overview, environment, Apps life cycle, creating views Application.
• Python Libraries – NumPy, Pandas, Matplotlib.

Introduction to Machine Learning / Deep Learning / Artificial Intelligence (AI)

• Understanding Machine Learning Fundamentals, Limitations of ML,
• Deep Learning, AI vs ML vs Generative AI.

• Python revision, Introduction to TensorFlow and Keras.

• Optimization, Derivatives, Function, Probability,
• Scalar-Vector-Matrix, Vector Operation, Vector spaces.

• Principles of Regression and Classification Model,
• Assumptions, Model Evaluation Matrices.

 Machine Learning:(Segmentation)

• K-Means & Hierarchical Clustering

 Machine Learning:(Bagging)

• Understanding Bagging Concepts, Decision Tree, Random Forest

• Understanding Boosting Concepts, XGBM.

• Understanding Human Brain’s functionality,
• Gradient Descent Optimization.

• Neural Network Overview

• Neural Network Processing, Backward Propagation

• Train, Test & Validation Set, Vanishing & Exploding Gradient,
• Dropout, Regularization, Bias correction, learning rate, Tuning, SoftMax etc.

• Adam, Ada, AdaBoost, RMS Prop etc.

• What is Computer Vision?, Read in Images in Python,
• Introduction to OpenCV, Basic Image Conversions, Image Resizing,
• Image Transformations, Contrast Stretching.

• Introduction to CNN architecture, Convolution, Feature detector, Padding, Stride,
• Activation function, Pooling, Training CNN, Loss function.

• Object detection concepts, Bounding box, object detection models,
• Pre-trained models, Transfer learning, Segmentation concepts.

• Advanced CNN models applications, Face detection,
• RCNN, Fast RCNN, Faster RCNN, Mask RCNN, YOLO.

• Text pre-processing, Binary weight, BoW, TF-IDF,
• Spam detection / Sentiments Analysis, Named Entity Recognition.

• Working with word vectors, Word Embedding, Word2Vec, CBOW/Skip-gram.

• Idea behind RNN architecture,
• Vanishing/Exploding gradient problem.

• LSTM, GRU, RNN vs LSTM vs GRU, Training of RNN model.

• Sequential data analysis, seq2seq model, Encoder-Decoder Architecture,
• Application of seq2seq models.

• Idea behind Transformer, Architecture and Analysis, Pre-trainer models,
• Attention (Elmo, BERT, T5), Self-Attention, Transformer Block, Multi-Head Attention.

• Generative AI using LLM,
• Introduction to GPT, VAE, GANs.

• DCGAN – Intuition, MNIST dataset, Building the generator, Building the discriminator,
• Loss (error) calculation, WGAN – Intuition.

• Pretrained Models (GPT, BERT, BART, T5), Models with applications,
• Intro to Different types of Transformer encoder models- Basic BERT, RoBERTa, DistilBERT etc.

• Basics of Autoencoders, Different types of autoencoders,
• applications with examples of autoencoders, variational autoencoders.

• ChatGPT, Bing, Bard

• Introduction, Automated Speech Recognition,
• text to speech conversion, voice assistant devices.

• Intro to RL, Q-Learning concept examples, Q-learning applications, Exploration & Exploitation.

• Policy gradient concepts, Actor-Critic methods, Proximal policy Optimization (PPO),
• Work with deep RL libraries.

LLM

• Real-world applications and case studies of LLMs,
• Chatbot Creation.

Finetuning LLM using Transformers for different tasks

• Fine-tuning a model process,
• LSTM vs GRU,
• Fine tuning Transformers model.

Evolution of GenAI

• History Of Generative AI,
• Generative AI (GenAI): The evolution of creativity through technology.

 GenAI Models and Basic: GPT Models with Prompt Engineering

• ChatGPT- GPT-2, GPT-3, GPT-3.5,
• LLAMA-2,
• Open Chat Model,
• Prompt Engineering Hallucination in LLM.

 Finetuning LLM LLAMA-2

• Simple finetuning of LLM: LLAMA-2.