TensorFlow: Google's open-source deep learning framework
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The document discusses several deep learning frameworks including TensorFlow, Keras, PyTorch, Theano, Deep Learning 4 Java, Caffe, Chainer, and Microsoft CNTK. TensorFlow was developed by Google Brain Team and uses dataflow graphs to process data. Keras is a high-level neural network API that runs on top of TensorFlow, Theano, and CNTK. PyTorch was designed for flexibility and speed using CUDA and C++ libraries. Theano defines and evaluates mathematical expressions involving multi-dimensional arrays efficiently in Python. Deep Learning 4 Java integrates with Hadoop and Apache Spark to bring AI to business environments. Caffe focuses on image detection and classification using C++ and Python. Chainer was developed in collaboration with several companies
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TensorFlow: Google's open-source deep learning framework
- 1. How Big Data evolved? Deep Learning Frameworks
- 3. How Big Data evolved? Microsoft CNTKTensorFlow
- 5. TensorFlow Developed by Google Brain Team Supports languages
- 6. TensorFlow Developed by Google Brain Team Supports languages Uses dataflow graphs to process data
- 7. TensorFlow Developed by Google Brain Team Supports languages Uses dataflow graphs to process data Easy to build Machine Learning models
- 8. TensorFlow Developed by Google Brain Team Supports languages Uses dataflow graphs to process data Easy to build Machine Learning models Robust Machine Learning production
- 9. TensorFlow Developed by Google Brain Team Supports languages Uses dataflow graphs to process data Easy to build Machine Learning models Powerful experimentation for research Robust Machine Learning production
- 10. TensorFlow Developed by Google Brain Team Supports languages Uses dataflow graphs to process data TensorBoard for data visualization Easy to build Machine Learning models Powerful experimentation for research Robust Machine Learning production
- 11. How Big Data evolved? Microsoft CNTKKeras
- 12. Keras Francois Chollet is the author of keras, with over 350,000 users and 700+ open-source contributors
- 13. Keras High-level neural network API, written in Python Francois Chollet is the author of keras, with over 350,000 users and 700+ open-source contributors
- 14. Keras Runs on top of TF, Theano, CNTK CNTK Francois Chollet is the author of keras, with over 350,000 users and 700+ open-source contributors High-level neural network API, written in Python
- 15. Keras CNTK Used in several startups, research labs, and companies High-level neural network API, written in Python Runs on top of TF, Theano, CNTK Francois Chollet is the author of keras, with over 350,000 users and 700+ open-source contributors
- 16. Keras CNTK Used in several startups, research labs, and companies User-friendly as it offers simple APIs and provides clear and actionable feedback upon user error Provides modularity as a sequence or a graph of standalone, fully configurable modules that can be plugged together with as few restrictions as possible Easily extensible as new modules are simple to add. This feature makes Keras suitable for advanced research 1 2 3 Runs on top of TF, Theano, CNTK Francois Chollet is the author of keras, with over 350,000 users and 700+ open-source contributors High-level neural network API, written in Python
- 17. How Big Data evolved? Microsoft CNTKPyTorch
- 18. PyTorch Authored by Adam Paszke, Sam Gross, Soumith Chintala and Gregory Chanan
- 19. PyTorch Authored by Adam Paszke, Sam Gross, Soumith Chintala and Gregory Chanan Lua based scientific computing framework for Machine Learning and Deep Learning algorithms
- 20. PyTorch Authored by Adam Paszke, Sam Gross, Soumith Chintala and Gregory Chanan Lua based scientific computing framework for Machine Learning and Deep Learning algorithms It employed CUDA along with C/C++ libraries for processing and was designed to scale the production of building models and overall flexibility
- 21. PyTorch Authored by Adam Paszke, Sam Gross, Soumith Chintala and Gregory Chanan Lua based scientific computing framework for Machine Learning and Deep Learning algorithms Widely used in companies like It employed CUDA along with C/C++ libraries for processing and was designed to scale the production of building models and overall flexibility
- 22. PyTorch Authored by Adam Paszke, Sam Gross, Soumith Chintala and Gregory Chanan Lua based scientific computing framework for Machine Learning and Deep Learning algorithms Widely used in companies like Provides flexibility and speed due to its hybrid front-end Enables scalable distributed training and performance optimization in research and production using torch.distributed backend Deep integration into Python allows popular libraries and packages to be used for quickly writing neural network layers in Python. 1 2 3 It employed CUDA along with C/C++ libraries for processing and was designed to scale the production of building models and overall flexibility
- 23. How Big Data evolved? Microsoft CNTKTheano
- 25. Theano Developed by Written in
- 26. Theano Python library that allows to define, optimize, and evaluate mathematical expressions involving multi- dimensional arrays efficiently Developed by Written in
- 27. Theano Python library that allows to define, optimize, and evaluate mathematical expressions involving multi- dimensional arrays efficiently • Theano has tight integration with NumPy for data computations • Uses GPU’s to perform data-intensive computations which are much faster than on a CPU • Has extensive unit-testing and self-verification that can detect and diagnose many types of errors Developed by Written in
- 28. How Big Data evolved? Microsoft CNTKDeep Learning 4 Java
- 29. Deep Learning 4 Java Developed by a Machine Learning group led by Adam Gibson
- 30. Deep Learning 4 Java Written in Java and Scala, DL4J supports different neural networks like CNN, RNN, and LSTM Developed by a Machine Learning group led by Adam Gibson
- 31. Deep Learning 4 Java DL4J was contributed to Eclipse Foundation. Integrated with Hadoop and Apache Spark, DL4J brings AI to business environments for use on distributed CPUs and GPUs Written in Java and Scala, DL4J supports different neural networks like CNN, RNN, and LSTM Developed by a Machine Learning group led by Adam Gibson
- 32. Deep Learning 4 Java DL4J was contributed to Eclipse Foundation. Integrated with Hadoop and Apache Spark, DL4J brings AI to business environments for use on distributed CPUs and GPUs • Provides a distributed computing framework as training with DL4J occurs in a cluster • Includes an n-dimensional array class using ND4J that allows scientific computing in Java and Scala • Offers a vector space modeling and topic modeling toolkit that is designed to handle large text sets and perform NLP Written in Java and Scala, DL4J supports different neural networks like CNN, RNN, and LSTM Developed by a Machine Learning group led by Adam Gibson
- 33. How Big Data evolved? Microsoft CNTKCaffe
- 35. Caffe Caffe stands for Convolutional Architecture for Fast Feature EmbeddingDeveloped at
- 36. Caffe Caffe stands for Convolutional Architecture for Fast Feature Embedding Written in C++, with a Python interface Developed at
- 37. Caffe Caffe stands for Convolutional Architecture for Fast Feature Embedding Written in C++, with a Python interface Generally used for image detection and classification Developed at
- 38. Caffe Caffe stands for Convolutional Architecture for Fast Feature Embedding Written in C++, with a Python interface Generally used for image detection and classification • Used in academic research projects, startup prototypes, and large-scale industrial applications in vision, speech, and multimedia • Caffe supports GPU and CPU-based acceleration computational kernel libraries such as NVIDIA, cuDNN and, IntelMLK • Offers high speed can process over 60M images per day with a single NVIDIA K40 GPU Developed at
- 39. How Big Data evolved? Microsoft CNTKChainer
- 40. Chainer Preferred Networks in collaboration with IBM, Intel, Microsoft, and Nvidia Developed by
- 41. Chainer Preferred Networks in collaboration with IBM, Intel, Microsoft, and Nvidia Written purely in Python, it runs on top of Numpy and CuPy Python libraries Developed by
- 42. Chainer Preferred Networks in collaboration with IBM, Intel, Microsoft, and Nvidia Written purely in Python, it runs on top of Numpy and CuPy Python libraries Provides a number of extended libraries Developed by
- 43. Chainer Developed by Preferred Networks in collaboration with IBM, Intel, Microsoft, and Nvidia Written purely in Python, it runs on top of Numpy and CuPy Python libraries Provides a number of extended libraries • Supports CUDA computation. It only requires a few lines of code to leverage a GPU. It also runs on multiple GPUs with little effort • Provides various network architectures, including feed-forward nets, convnets, recurrent nets, and recursive nets
- 44. How Big Data evolved? Microsoft CNTKMicrosoft CNTK
- 46. Microsoft Cognitive Toolkit CNTK CNTK is a Deep Learning framework that builds a neural network as a series of computational steps via a directed graph Developed by
- 47. Microsoft Cognitive Toolkit CNTK Supports interfaces such as Python, C++ CNTK is a Deep Learning framework that builds a neural network as a series of computational steps via a directed graph Developed by
- 48. Microsoft Cognitive Toolkit CNTK Supports interfaces such as Python, C++ CNTK is a Deep Learning framework that builds a neural network as a series of computational steps via a directed graph Used mainly for Developed by
- 49. Microsoft Cognitive Toolkit CNTK Developed by Supports interfaces such as Python, C++ CNTK is a Deep Learning framework that builds a neural network as a series of computational steps via a directed graph Used mainly for • Designed for speed and efficiency, CNTK scales well in production using GPUs but has limited support from the community • Supports both RNN and CNN type of neural models capable of handling image, handwriting and speech recognition problems