Introduction to ML and TensorFlow

Transcript

1. Introduction To Machine Learning
3. TYPES of AI Artificial Narrow Intelligence (ANI): Machine intelligence that equals or exceeds human intelligence or efficiency at a specific task. Artificial General Intelligence (AGI): A machine with the ability to apply intelligence to any problem, rather than just one specific problem (human-level intelligence). Artificial Superintelligence (ASI): An intellect that is much smarter than the best human brains in practically every field, including scientific creativity, general wisdom and social skills.
4. Machine Learning - Basics Machine Learning is a type of Artificial Intelligence that provides computers with the ability to learn without being explicitly programmed.
5. Learning Approaches
6. Problem Types
7. Problem Types
8. Problems Underfitting :- A statistical model or a machine learning algorithm is said to have underfitting when it cannot capture the underlying trend of the data. Overfitting :- A statistical model is said to be overfitted, when we train it with a lot of data (just like fitting ourselves in an oversized pants!).
9. Problems
10. Help me out here https://github.com/josephmisiti/awesome-machine-learning
11. Scikit-Learn http://scikit-learn.org/
13. Splitting
14. CheatSheet
15. TensorFlow https://www.tensorflow.org/ TensorFlow™ is an open source software library for high performance numerical computation.
16. TensorFlow • Originally developed by the Google Brain Team within Google's Machine Intelligence research organization • TensorFlow provides primitives for defining functions on tensors and automatically computing their derivatives. • An open source software library for numerical computation using data flow graphs
17. Tensor? Simply put: Tensors can be viewed as a multidimensional array of numbers. This means that: • A scalar is a tensor, • A vector is a tensor, • A matrix is a tensor
19. Data Flow Graph? tensorboard Computations are represented as graphs: • Nodes are the operations (ops) • Edges are the Tensors (multidimensional arrays) https://playground.tensorflow.org/ https://js.tensorflow.org/
24. Google Codelab https://colab.research.google.com/ Google Colab is a free cloud service and now it supports free GPU! Since Colab is working on your own Google Drive, we first need to specify the folder we’ll work. Create folder name “app” on my Google Drive. Of course, you can use a different name or choose the default Colab Notebooks folder instead of app folder.
25. GPU!! It is so simple to alter default hardware (CPU to GPU or vice versa); just follow Edit > Notebook settings or Runtime>Change runtime type and select GPU as Hardware accelerator.
26. Thank you for tolerating me https://github.com/kashyap32 https://twitter.com/kashyapraval32 https://www.facebook.com/kashyapbraval https://medium.com/@kashyapraval