Tensorflowv5.0
1 like332 views
Sanjib Basak works as the director of data science at Digital River and has over 15 years of experience in analytics. He is fascinated by machine learning and deep learning, and has been experimenting with TensorFlow for 6-7 months. In his presentation, he introduces TensorFlow and builds basic models using it, including language models and RNNs. He discusses advantages of TensorFlow over Python/NumPy and takes questions at the end.
Tensorflowv5.0
- 1. Sanjib Basak Digital River Inc.
- 2. Working as a director of data science at Digital River Working in analytics field for 15 plus years Built analytical applications in Retail and Healthcare Fascinated by Machine Learning and Deep Learning Playing with Tensor Flow for 6-7 months Host of another meet up Twin cities Big Data Analytics
- 3. Introduction to Tensor Flow A basic model with Tensor Flow Language Model RNN with Tensor Flow Advantages of Tenor Flow over Python Numpy Question and Answer
- 4. TensorFlow is an open source software library for numerical computation using data flow graphs. Open sourced by Google in Nov,2015 The flexible architecture allows to deploy computation to one or more CPUs or GPUs in a desktop, server, or mobile device with a single API. One of the most popular project in open source community with 30,000+ Stars and 13,000+ Forks in Git Hub
- 5. Tensors provide a natural and concise mathematical framework for formulating and solving problems in areas of physics such as elasticity, fluid mechanics, and general relativity Wolfram Mathworld Tensors are n dimensional arrays Scalar is 0 D tensor Vector is 1 D tensor Matrix is 2 D tensor 1.5 1.1 1.3 1.5, 1.6,1.7 1.1,1.2,1.3 1.3,1.6,1.7 Vector Matrix 1.5 Scalar Tensor
- 6. Node: Represents Operations like Addition, Multiplications etc. Edges: Carriers of the operations Kernels: Implementation of the operations in device - CPU , GPU Session: A session is created when client program establish communication with TF run time code Tensor Flow data structure: Rank : Rank as defined above Shape : Number of rows and columns Type Int,Float32
- 7. A linear regression model using ipython notebook
- 8. Language Models computes the probability of occurrences of words in a sequence Widely used in speech and translation system Model chooses best word ordering from various possibilities The world is small,small is the world
- 9. Bi-Gram and Tri-Gram Model Drawbacks: In some cases window of past consecutive words may not be sufficient Fails to capture context Jane and John met in the market. They started fighting with each other. Who started fighting? Memory intensive
- 10. In 2001, Bengio et al. introduces the first large-scale deep learning for natural language processing model This model capture context via learning a distributed representation of words A Feed Forward Neural Network Model
- 11. word vectors Loss Function: Cross Entropy Measurement of the Model In 2010 Mokolov et. all presented multi layered Recurrent Neural Network (in NLP)
- 12. Load Data Add Placeholder Create Feed Dictionary Validate Model Test Model Add Embedding (embed_size = 50) Create and update Hidden Layer (Hidden layer of size =100) Create and update Projection Layer Calculate Loss Optimizer applied to all trainable variables S Optimization achieved E Repeatuntil required optimization Train Model
- 13. Problem Definition Test 1 Trained on 20 sections of WSJ data of Penn Tree Bank Test 2 Trained on Harry Potter and Deathly Hallows
- 16. On Deep Learning/NLP http://cs.stanford.edu/people/karpathy/ http://www.wildml.com/ Mikolovs thesis: http://www.fit.vutbr.cz/~imikolov/rnnlm/thesis.pdf Richard Socherrs Thesis http://nlp.stanford.edu/~socherr/thesis.pdf On TensorFlow https://www.tensorflow.org/ Comparison between TensorFlow and other frameworks http://deeplearning4j.org/compare-dl4j-torch7-pylearn.html
- 17. Q&温馨沿;粥