Integer sequence learning using Python
0 likes101 views
The document discusses integer sequence learning, focusing on the development of machine learning algorithms to predict the next number in an integer sequence. It outlines problem statements, the importance of sequence learning for various applications, and introduces different algorithms including linear recurrence relations, n-grams, and RNNs. The goal is to identify patterns in sequences to solve mathematical and practical problems effectively.
Integer sequence learning using Python
- 2. Content 1.Problem Statement 2.Why Sequence Learning? 3.Road map of Phase 1 4.Sequence Learning Algorithms 4.1- Recurrence Relations 4.2- Ngram
- 3. Problem Statement To Design a machine learning algorithm capable of guessing the next number in an integer sequence 1, 2, 3, 4, 5, 7 ? Guess what's next _??? Example
- 4. If your answer is 8 (Hint: Power of primes) then you predicted the number correctly. You used your intuition and tried to find pattern in the integer sequence and then you developed algorithm which predicted the right number. That's exactly what we are trying to do the only difference is that the computer will have to predict the correct number Problem Statement (Simplified form)
- 5. Why Sequence Learning ? • Provide answers to various mathematical and physics problems such as number theory • Avoids cases of intense operations such as checking for primeness (used in cryptography) • Can be used for predicting next system call(Malware Detection)
- 6. Sequence Learning Algorithms These are the algorithms that try to find pattern in the given sequence It includes:- Linear Recurrence Relation N-gram RNN(Recurrent Neural Network) Markov chain
- 7. Linear Recurrence Relation A linear recurrence relation is an equation that defines the n’th term in a sequence in terms of the K previous terms in the sequence. The recurrence relation is in the form:
- 8. N gram An n-gram is a contiguous sequence of n items from a given sequence of text or speech. The items can be phonemes, syllables, letters, words or base pairs according to the application. The n-grams typically are collected from a text or speech corpus. When the items are words, n-grams may also be called shingles.