Resume

Resume

Education / Certifications

Building AI Agents and Agentic Workflows

  • •Gained practical expertise in designing agentic AI systems using modern frameworks and workflow patterns.
  • •Built LangGraph agents supporting memory, iteration, conditional logic, and Agentic RAG; developed self-improving AI agents; designed multi-agent systems with orchestration; and structured modular workflows using CrewAI.
  • •Applied frameworks like IBM BeeAI and AG2 (AutoGen) to conversation-driven applications and scalable workflows, configuring agents, tasks, memory, and tools to enable reasoning, feedback loops, and autonomous behavior.

Improving Deep Neural Networks: Hyperparameter Tuning, Regularization and Optimization

  • •Understand industry best-practices for building deep learning applications.
  • •Be able to effectively use the common neural network tricks, including initialization, L2 and dropout regularization, Batch normalization, gradient checking.
  • •Be able to implement and apply a variety of optimization algorithms, such as mini-batch gradient descent, Momentum, RMSprop and Adam, and check for their convergence.
  • •Understand new best-practices for the deep learning era of how to set up train/dev/test sets and analyze bias/variance.
  • •Be able to implement a neural network in TensorFlow.

AWS Fundamentals: Addressing Security Risk

  • •Detective controls, including Amazon CloudTrail, AWS Security Hub, Amazon GuardDuty, and AWS Config.
  • •Encryption of data at rest, in motion, and best practices for how to store data within and between various AWS services.
  • •The AWS Well-Architected Framework and protecting compute resources such as Amazon EC2 and AWS Lambda.

Neural Networks and Deep Learning

  • •Understand the major technology trends driving Deep Learning
  • •Be able to build, train and apply fully connected deep neural networks
  • •Know how to implement efficient (vectorized) neural networks
  • •Understand the key parameters in a neural network's architecture

AWS Fundamentals: Migrating to the Cloud

AWS Fundamentals: Building Serverless Applications

  • •AWS serverless framework and architecture in the context of a real business problem
  • •Provide AWS Lambda, Amazon API Gateway, Amazon DynamoDB, Amazon Lex, and other services needed to deploy serverless solutions.

Sequence Models

Recurrent Neural Networks

Recurrent Neural Networks and commonly-used variants such as GRUs and LSTMs; apply RNNs to Character-level Language Modeling; gain experience with natural language processing and Word Embeddings; and use HuggingFace tokenizers and transformer models to solve different NLP tasks such as NER and Question Answering.

Natural Language Processing & Word Embeddings

Natural language processing with deep learning is an important combination. Using word vector representations and embedding layers you can train recurrent neural networks with outstanding performances in a wide variety of industries. Examples of applications are sentiment analysis, named entity recognition and machine translation.

Sequence Models

Sequence models can be augmented using an attention mechanism. This algorithm will help your model understand where it should focus its attention given a sequence of inputs. This week, you will also learn about speech recognition and how to deal with audio data.