Career in AI

How the early college students start with AI, and what AI actually is. Different roles wherein AI is used, and what tools and technology they need to learn to succeed.

Transcript

1. Career in AI Tanisha Bhayani
2. Dive into AI
3. Start Learning AI
4. • Good Algorithmic background • Good Mathematical background • Good coding skills • Great amount of patience • Knowledge of OS
5. • Data Gathering ◦ Python Parsing libraries - depends on the type of dataset. • Data Cleaning ◦ Numpy, Pandas • Data Visualization ◦ Jupyter Notebooks, Matplotlib, Seaborn, Plotly • Feature Engineering ◦ Jupyter Notebook, Domain Knowledge, Some database knowledge, may also require additional data gathering. • Training and Testing ◦ tensorflow, keras, scikit-learn • Deploying ◦ Tensor flow serving API, Any cloud provider, Firebase, integration
6. AI Engineer, really?
7. Use Cases • Sentiment Analysis • Facial Recognition • Fake News Detection • Loan Default Prediction • Recommendations
9. Research Trends • Generative Models • Reinforcement Learning • Capsule Networks • Markov Models • Probabilistic Models • Combining Symbolic AI with Connectionist Models • Information Theory for Deep Neural Networks
10. Certificates / Projects?
11. Whom to Follow 1. Andrej Karpathy 2. Geoffrey Hinton 3. Yann LeCun 4. Youshua Bengio 5. Ian Goodfellow 6. Demis Hassabis 7. Richard Sutton
12. QnA