Basically 1_notmnist is to learn how to display data in Jupyter Notebook. Besides, it also let us know on sklearn - a python machine library - so that we can then compare with TensorFlow. This is the exact ipynb file at Tensorflow Github Repo.

Basically 1_notmnist is to learn how to display data in Jupyter Notebook. Besides, it also let us know on sklearn - a python machine library - so that we can then compare with TensorFlow. This is the exact ipynb file at Tensorflow Github Repo.

Basically 1_notmnist is to learn how to display data in Jupyter Notebook. Besides, it also let us know on sklearn - a python machine library - so that we can then compare with TensorFlow. This is the exact ipynb file at Tensorflow Github Repo.

Basically 1_notmnist is to learn how to display data in Jupyter Notebook. Besides, it also let us know on sklearn - a python machine library - so that we can then compare with TensorFlow. This is the exact ipynb file at Tensorflow Github Repo.

Basically 1_notmnist is to learn how to display data in Jupyter Notebook. Besides, it also let us know on sklearn - a python machine library - so that we can then compare with TensorFlow. This is the exact ipynb file at Tensorflow Github Repo.

Basically 1_notmnist is to learn how to display data in Jupyter Notebook. Besides, it also let us know on sklearn - a python machine library - so that we can then compare with TensorFlow. This is the exact ipynb file at Tensorflow Github Repo.

FYI, the course link is https://www.udacity.com/course/deep-learning--ud730. This course takes approximately 3 months with assumption 6hrs/wk (work at your own pace).

On 10th November, I saw the news that Google open sourced Tensorflow. As a programmer that is passionate towards AI, this is a thing that I must try out.

Recently Julia is on the trend, due to its purpose of becoming an easy-to-use scripting language, while giving near to C performance speed. I always see it as combination of Python + R + C, while some might think it as Python + Matlab + C

CUDA® is a parallel computing platform and programming model invented by NVIDIA. It enables dramatic increases in computing performance by harnessing the power of the graphics processing unit.

This course provides a broad introduction to machine learning, datamining, and statistical pattern recognition. Topics include:

• Supervised learning (parametric/non-parametric algorithms, support vector machines, kernels, neural networks).
• Unsupervised learning (clustering, dimensionality reduction, recommender systems, deep learning).
• Best practices in machine learning (bias/variance theory; innovation process in machine learning and AI).

Kaggle titanic challenge is a famous knowledge competition which many new Kaggler will try their first Kaggle competition. Since there are currently no tutorial to solve this challenge with artificial neural network, I decided to use torch7 to compete in this competition. FYI, click here to get the data.

Bike sharing systems are a means of renting bicycles where the process of obtaining membership, rental, and bike return is automated via a network of kiosk locations throughout a city. Using these systems, people are able rent a bike from a one location and return it to a different place on an as-needed basis. Currently, there are over 500 bike-sharing programs around the world.

Backpropagation, is a well known learning algorithm in neural network. In the algorithm, the weight calculated is based on the out put of the result. To prevent overfitting and introduce more uncertainty, its often comes with L1 and L2 regularization.

Weights with greater uncertainty introduce more variability into the decisions made by the network, leading naturally to exploration