Loading repository data…
donnemartin / repository
Data science Python notebooks: Deep learning (TensorFlow, Theano, Caffe, Keras), scikit-learn, Kaggle, big data (Spark, Hadoop MapReduce, HDFS), matplotlib, pandas, NumPy, SciPy, Python essentials, AWS, and various command lines.
A transparent discovery signal based on current public GitHub metadata.
This score does not audit code, security, maintainers, documentation quality, or suitability. Verify the repository and its current documentation before adoption.
IPython Notebook(s) demonstrating deep learning functionality.
Additional TensorFlow tutorials:
Selected from shared topics, language and repository description—not editorial ratings.
tuanx18 /
This is a repository to demonstrate my details, skills, projects and to keep track of my progression in Data Analytics and Data Science topics.
73/100 healthLqNoob /
Data science Python notebooks: Deep learning (TensorFlow, Theano, Caffe, Keras), scikit-learn, Kaggle, big data (Spark, Hadoop MapReduce, HDFS), matplotlib, pandas, NumPy, SciPy, Python essentials, AWS, and various command lines.
31/100 healthEESH20 /
| Notebook | Description |
|---|---|
| tsf-basics | Learn basic operations in TensorFlow, a library for various kinds of perceptual and language understanding tasks from Google. |
| tsf-linear | Implement linear regression in TensorFlow. |
| tsf-logistic | Implement logistic regression in TensorFlow. |
| tsf-nn | Implement nearest neighboars in TensorFlow. |
| tsf-alex | Implement AlexNet in TensorFlow. |
| tsf-cnn | Implement convolutional neural networks in TensorFlow. |
| tsf-mlp | Implement multilayer perceptrons in TensorFlow. |
| tsf-rnn | Implement recurrent neural networks in TensorFlow. |
| tsf-gpu | Learn about basic multi-GPU computation in TensorFlow. |
| tsf-gviz | Learn about graph visualization in TensorFlow. |
| tsf-lviz | Learn about loss visualization in TensorFlow. |
| Notebook | Description |
|---|---|
| tsf-not-mnist | Learn simple data curation by creating a pickle with formatted datasets for training, development and testing in TensorFlow. |
| tsf-fully-connected | Progressively train deeper and more accurate models using logistic regression and neural networks in TensorFlow. |
| tsf-regularization | Explore regularization techniques by training fully connected networks to classify notMNIST characters in TensorFlow. |
| tsf-convolutions | Create convolutional neural networks in TensorFlow. |
| tsf-word2vec | Train a skip-gram model over Text8 data in TensorFlow. |
| tsf-lstm | Train a LSTM character model over Text8 data in TensorFlow. |
| Notebook | Description |
|---|---|
| theano-intro | Intro to Theano, which allows you to define, optimize, and evaluate mathematical expressions involving multi-dimensional arrays efficiently. It can use GPUs and perform efficient symbolic differentiation. |
| theano-scan | Learn scans, a mechanism to perform loops in a Theano graph. |
| theano-logistic | Implement logistic regression in Theano. |
| theano-rnn | Implement recurrent neural networks in Theano. |
| theano-mlp | Implement multilayer perceptrons in Theano. |
| Notebook | Description |
|---|---|
| keras | Keras is an open source neural network library written in Python. It is capable of running on top of either Tensorflow or Theano. |
| setup | Learn about the tutorial goals and how to set up your Keras environment. |
| intro-deep-learning-ann | Get an intro to deep learning with Keras and Artificial Neural Networks (ANN). |
| theano | Learn about Theano by working with weights matrices and gradients. |
| keras-otto | Learn about Keras by looking at the Kaggle Otto challenge. |
| ann-mnist | Review a simple implementation of ANN for MNIST using Keras. |
| conv-nets | Learn about Convolutional Neural Networks (CNNs) with Keras. |
| conv-net-1 | Recognize handwritten digits from MNIST using Keras - Part 1. |
| conv-net-2 | Recognize handwritten digits from MNIST using Keras - Part 2. |
| keras-models | Use pre-trained models such as VGG16, VGG19, ResNet50, and Inception v3 with Keras. |
| auto-encoders | Learn about Autoencoders with Keras. |
| rnn-lstm | Learn about Recurrent Neural Networks (RNNs) with Keras. |
| [lstm-sentence-gen](https://nbviewer.jupyter.org/github/donnemartin/data-science-ipython-notebooks/blob/master/deep-learning/keras-tutorial/3.3%20%28Extra%29%20LSTM%20for%20Sentence%20Generation |
🔬 Explore data science workflows with Science Flake, a powerful tool for managing and sharing reproducible research environments effortlessly.
Garavirod /
Projects and notes related with big data, data science and data engineering
39/100 healthAlainDaccache /
Modular infrastructure for the Data Engineering, Data Science, and ML Engineering lifecycle. Serves as a generic baseline to kickstart Artificial and Business Intelligence pipelines with industry practices.
42/100 healtheric-erki /
Data science Python notebooks: Deep learning (TensorFlow, Theano, Caffe, Keras), scikit-learn, Kaggle, big data (Spark, Hadoop MapReduce, HDFS), matplotlib, pandas, NumPy, SciPy, Python essentials, AWS, and various command lines.
27/100 health