We discussed in the talk: 1. Python ML Libraries 2. Is Python better for ML 3. Future Bottlenecks in ML Python ML Libraries 1. Tensor Flow 2. Scikit-Learn 3. Theano 4. Pylearn2 5. PyEvolve

2. Top languages for ML Implementation

3. Sequence
1. Python ML Libraries
2. Is Python better for ML
3. Future Bottlenecks in ML

4. Python ML Libraries
1. Tensor Flow
2. Scikit-Learn
3. Theano
4. Pylearn2
5. PyEvolve

5. Tensor Flow
1. High-level neural network library
2. Written in C++ - High Performance
3. Developed for Google Brain project

6. Scikit-Learn
1. Data mining and data analysis
2. Built on top of NumPy, SciPy, and Matplotlib libraries
3. Low level, has most of the ML implementations

7. Theano
1. Define, optimize, and evaluate maths on multi-
dimensional arrays
2. Integrates closely with NumPy
3. Uses GPU internally – Extremely Fast

8. Pylearn2
Like Scikit-Learn but more flexible
in terms of writing custom code

9. Pyevolve
Neural network research in space of genetic algorithms

10. Is Python better for ML
1. Concise
2. Huge number of ML libraries
2. Easily extensible
3. Multiparadigm
4. Interactive
5. Easy to read

11. Future Bottlenecks in ML
1. Computation: Even with really fast processor and distributed
computing, the growth of size of data remains an issue
2. Algorithms: Going hand in hand with computation, better algorithms
need to be developed
3. Datasets: With a large dataset, the ML models show great results but
procuring a large dataset for each model is a bottleneck

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