How to Use Deep Learning by Mu Sigma Product Manager
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In this presentation, Ankit Raheja, helps you understand whether it makes sense to build AI Products and how to showcase the value you can get out of your AI Products. He also discusses what you should focus on during Designing Products. And finally, talks about how Developing and Deploying AI Products are two very different beasts and how to deal with them differently.
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How to Use Deep Learning by Mu Sigma Product Manager
- 1. www.productschool.com How to Use Deep Learning by Mu Sigma Product Manager
- 2. FREE INVITE Join 25,000+ Product Managers on
- 3. COURSES Product Management Learn the skills you need to land a product manager job
- 4. COURSES Coding for Managers Build a website and gain the technical knowledge to lead software engineers
- 5. COURSES Data Analytics for Managers Learn the skills to understand web analytics, SQL and machine learning concepts
- 6. COURSES Digital Marketing for Managers Learn how to acquire more users and convert them into clients
- 7. COURSES Blockchain for Managers Learn how to trade cryptocurrencies and build products using the blockchain
- 9. Product Manager's Practical Guide for Building AI Products that customers would love Ankit Raheja Engagement Manager @ Mu Sigma
- 10. My Journey so far
- 11. AI & ML HPC Big Data
- 12. AI ≠ ML ≠ DL
- 13. What is not an AI first Company? Traditional Technology Company with A/D(Neural Networks is not an AI Company
- 14. Characteristics of an AI First Company
- 15. Economics of AI 1.Downward Sloping Curve 2.Cross Price Elasticity 3.Complements 4.Substitutes
- 16. What should an AI Product Manager Do 1. Come up with a Solid Business Model for Path to Profit 2. Understand whether it an AI solvable problem a. Too much Data - Google Search b. Computer Vision - Amazon X Ray Feature c. NLP - Siri/Alexa/Google Assistant d. Anomaly Detection - Payment Providers e. Recommendations f. Reinforcement learning g. Predictions and Forecasting h. Others 3. Strategic Data Acquisition - Training/Feedback Data is the new wireframe 4. Understand ML concepts and define & track success metrics* 5. PM 101 Skills 6. Agile* Data Science
- 17. Meteoric Rise
- 18. Why is DL picking up and why should you care? 1. Data Explosion 4. Robust,Generalizable and Scalable Algorithms 2. Computation Power - CUDA 5. Neural Networks are cool again 3. Less time feature engineering 6. Activation Functions* 3584!
- 20. Gradient Descent ● Learning Rate ● Momentum ● Mini Batching ● Adaptive ● Decay Rate
- 22. Metrics ● Bias/Variance Tradeoff ● Overfitting ● Accuracy ● Precision vs Recall vs F Score ● Training Time ● Querying Time ● Business Metrics
- 23. Andrew Ng’s Flow Chart for Applying Deep Learning
- 24. Dropout Rate You drop off neurons to generalize better and to avoid overfitting and memorizing https://codelabs.developers.google.com/codelabs/cloud-tensorflow-mnist/#9
- 25. Data Science Life Cycle by Microsoft
- 26. My Product Management Mantra
- 27. MLOps
- 28. Machine Learning in Production ● Choose Right models for Offline or Online Processing ● Response Time, Implementation Cost, Runtime Cost ● Choose Containers for Productionizing - Packaging, Inheritance,Microservices ● Track Metrics in Real Time ● Put Fail Safe Mechanism in place ● Get Evangelists ● Training Data/Test Data is indicative of Real Data ● Reproducible Research Tools/Algo Data Banks ● Run Offline Models first before putting them in production ● Run Targeted A/B Tests ensuring proper Statistical Significance, Sample Size etc. ● Model Development Lifecycle ≠ Software Development Life Cycle
- 29. Next Steps Great Resources 1. Prediction Machines - Simple Economics of AI 2. Follow/Youtube - Geoffrey Hinton , Andrew Ng, Yann LeCun,Andrej Karpathy,Ian Goodfellow,Fei-Fei Li etc. 3. DeepLearning.AI 4. Fast.AI 5. Hands-On Machine Learning 6. Deep Learning Fundamental: An Introduction for Beginners 7. Siraj Raval’s videos 8. https://keras.io/
- 30. Thank You
- 31. www.productschool.com Part-time Product Management, Coding, Data, Digital Marketing and Blockchain courses in San Francisco, Silicon Valley, New York, Santa Monica, Los Angeles, Austin, Boston, Boulder, Chicago, Denver, Orange County, Seattle, Bellevue, Toronto, London and Online
Editor's Notes
- Training Time from Weeks to Days. Can Iterate Quickly. Data is exploding. So much data is getting generated and easier to ingest now. Parallel Computing Architecture. 3584 cores. Throw Kitchen sink at it. Tune hyper parameters
- Input layer or Input Features , Hidden Layers - Pick 4-5-6-7-8,
- https://codelabs.developers.google.com/codelabs/cloud-tensorflow-mnist/#9