ARTIFICIAL INTELLIGENCE
RESEARCH & DEVELOPMENT
துர்கா AI
Empower your sales with smart, seamless customer engagement.
Durga AI is an innovative chatbot API that seamlessly integrates with your webpage to transform your marketing and sales strategies.
Specializing in marketing through regional languages, it breaks down communication barriers between companies and customers, delivering personalized, intelligent responses.
Whether nurturing leads, answering queries, or guiding purchasing decisions, Durga AI drives conversions and accelerates growth by simplifying customer engagement.
For info visit www.durgaai.com
ஆசான் AI
Empowering Learning, Simplifying Education with Aasan AI
Aasan AI is an innovative Tamil language generative AI tailored specifically for educational purposes.
Designed to enhance learning experiences, it leverages advanced algorithms to provide interactive and personalized education solutions.
Optimized for clarity and ease of understanding, Aasan AI supports students and educators by offering detailed explanations, interactive quizzes, and comprehensive learning modules.
With its user-friendly interface, it aims to make learning accessible and engaging for Tamil-speaking communities.
For info reach out to evolverobotlab@gmail.com
DEEP LEARNING
ARTIFICIAL NEURAL NETWORK
- Introduction to neural networks
- Neuron model and activation functions
- Feedforward neural networks and architecture
- Backpropagation algorithm
- Gradient descent optimization
- Regularization techniques (L1/L2 regularization, dropout)
- Common activation functions (sigmoid, tanh, ReLU)
- Convolutional neural networks (CNNs)
- Recurrent neural networks (RNNs)
- Long Short-Term Memory (LSTM) networks
- Transfer learning and pre-trained models
- Model evaluation and metrics
- Practical implementation in TensorFlow or Keras
2. Generative Pretrained Transformer (GPT)
- Introduction to Sequence Modeling and Transformer
- Transformer Architecture and Components (Encoder, Decoder)
- Self-Attention Mechanism and Multi-Head Attention
- Loss Functions for Transformers (Cross-Entropy Loss)
- Training Techniques for Transformers
- Variations of Transformers (BERT, GPT, Transformer-XL, etc.)
- Evaluating Transformers (Perplexity, BLEU Score, ROUGE Metrics)
- Transformer Applications (Machine Translation, Text Summarization)
- Practical Implementation of Transformers in TensorFlow or PyTorch
3. DIFFUSION MODEL FOR IMAGE GENERATION
- Introduction to Diffusion Models
- Mathematical Foundations of Diffusion Processes
- Diffusion Model Architecture and Components
- Loss Functions in Diffusion Models
- Training Techniques for Diffusion Models
- Diffusion Models for Image & Audio Generation
- Text-to-image synthesis with stable diffusion model
- Practical Implementation of Diffusion Models in TensorFlow or PyTorch
4. Large Language Models
- Transformer Architecture in LLMs
- Pre-training and Fine-tuning Paradigm
- GPT Series (GPT-2, GPT-3, GPT-4)
- BERT
- Fine-tuning and Prompt Engineering
- Multimodal Models
- Combining text with images, audio, and other data types (e.g., CLIP, DALL·E)
- Practical Implementation of LLMs in TensorFlow or PyTorch
- Building a Text Generation Application
5. Optimizers:
- Introduction to optimization algorithms in deep learning
- Stochastic Gradient Descent (SGD) and its variants
- Adaptive optimization algorithms (Adam, RMSprop, Adagrad)
- Learning rate scheduling and decay
- Momentum-based optimizers
- Second-order optimization algorithms (Newton’s method, conjugate gradient)
- Regularization techniques in optimization (weight decay, dropout)
- Understanding and tuning optimizer hyperparameters
- Comparison and trade-offs of different optimizers
- Practical implementation and experimentation with optimizers
6. TensorFlow & Pytorch
- Introduction to TensorFlow and PyTorch Ecosystems
- Basics of Tensors and Operations in TensorFlow & PyTorch
- Building Computational Graphs & Dynamic Computation with PyTorch
- Defining and Training Models using High-Level APIs
- Saving and Loading Models
- TensorFlow for production and deployment
- Distributed Training on Multiple GPUs/TPUs
- Practical exercises and projects using TensorFlow &Pytorch
7. Data Science
- Data preprocessing and cleaning techniques
- Handling missing data and outliers
- Exploratory Data Analysis (EDA)
- Feature Engineering and Selection
- Model Evaluation and Optimization
- Data Science for Deployment
Quick Links
Contact Us
- Plot No.40-B1, Door No.2, Second Floor, Ram Nagar First Main Road, Nanganallur, Chennai - 600 061,
- +91 73580 06064
- contact@evolverobot.in