Write a programme to conduct uninformed and informed search. Write a programme to conduct game search. Write a programme to construct a Bayesian network from given data. Write a programme to infer from the Bayesian network. Write a programme to run value and policy iteration in a grid world. Write a programme to do reinforcement learning in a grid world. Mini Project work.
Pandas is a Data Analysis library for Python. The tutorials contains the fundamentals and advanced concepts of the pandas library like Series, DataFrame, Groupby functions, Aggregate functions, other commonly used functions, Reading CSV files, Sorting, combining multiple conditions, plotting of the data and working with three different CSV datasets. Code for all the videos can be downloaded from below link https://github.com/tejalal/pandas-tutorial Installing Python using Anaconda on Windows https://tejalal.wordpress.com/2018/04/06/install-open-cv-on-windows-using-anaconda/ Installing Python using Anaconda on Linux (Ubuntu) https://tejalal.wordpress.com/2018/04/06/install-open-cv-on-windows-using-anaconda/
Pandas is a Data Analysis library for Python. The tutorials contains the fundamentals and advanced concepts of the pandas library like Series, DataFrame, Groupby functions, Aggregate functions, other commonly used functions, Reading CSV files, Sorting, combining multiple conditions, plotting of the data and working with three different CSV datasets. Code for all the videos can be downloaded from below link https://github.com/tejalal/pandas-tutorial Installing Python using Anaconda on Windows https://tejalal.wordpress.com/2018/04/06/install-open-cv-on-windows-using-anaconda/ Installing Python using Anaconda on Linux (Ubuntu) https://tejalal.wordpress.com/2018/04/06/install-open-cv-on-windows-using-anaconda/
Pandas is a Data Analysis library for Python. The tutorials contains the fundamentals and advanced concepts of the pandas library like Series, DataFrame, Groupby functions, Aggregate functions, other commonly used functions, Reading CSV files, Sorting, combining multiple conditions, plotting of the data and working with three different CSV datasets. Code for all the videos can be downloaded from below link https://github.com/tejalal/pandas-tutorial Installing Python using Anaconda on Windows https://tejalal.wordpress.com/2018/04/06/install-open-cv-on-windows-using-anaconda/ Installing Python using Anaconda on Linux (Ubuntu) https://tejalal.wordpress.com/2018/04/06/install-open-cv-on-windows-using-anaconda/
Zero-shot learning aims to recognize objects whose instances may not have been seen during training. Zero-Shot Learning is a very new area of research, but it is an unquestionable fact that it has a very high potential and it is one of the leading research topics in Computer Vision.
Defining cloud TPU and running MNIST dataset on Cloud Shell and dispay result on TensorBoard.
Auto Encoders are used for reconstruction of input data that can be image, vectors etc. It is a very trending topic and has many applications like In Removing Watermarks, Constructing blurred Image into Clear Image etc.
Gym is a toolkit for developing and comparing reinforcement learning algorithms. It supports teaching agents everything from walking to playing games like Pong or Pinball.
This video discusses various methods to train deep learning/machine learning models on cloud at free of cost
Gensim is a robust open-source vector space modeling and topic modeling toolkit implemented in Python. It uses NumPy, SciPy and optionally Cython for performance. Gensim is specifically designed to handle large text collections, using data streaming and efficient incremental algorithms, which differentiates it from most other scientific software packages that only target batch and in-memory processing.
Gensim is a robust open-source vector space modeling and topic modeling toolkit implemented in Python. It uses NumPy, SciPy and optionally Cython for performance. Gensim is specifically designed to handle large text collections, using data streaming and efficient incremental algorithms, which differentiates it from most other scientific software packages that only target batch and in-memory processing.
Gensim is a robust open-source vector space modeling and topic modeling toolkit implemented in Python. It uses NumPy, SciPy and optionally Cython for performance. Gensim is specifically designed to handle large text collections, using data streaming and efficient incremental algorithms, which differentiates it from most other scientific software packages that only target batch and in-memory processing.
Gensim is a robust open-source vector space modeling and topic modeling toolkit implemented in Python. It uses NumPy, SciPy and optionally Cython for performance. Gensim is specifically designed to handle large text collections, using data streaming and efficient incremental algorithms, which differentiates it from most other scientific software packages that only target batch and in-memory processing.
nolearn contains a number of wrappers and abstractions around existing neural network libraries, most notably Lasagne, along with a few machine learning utility modules.
Deepy is a deep learning framework for designing models with complex architectures. Many important components such as LSTM and Batch Normalization are implemented inside. Although highly flexible, deepy maintains a clean high-level interface.
Deepy is a deep learning framework for designing models with complex architectures. Many important components such as LSTM and Batch Normalization are implemented inside. Although highly flexible, deepy maintains a clean high-level interface.
Deepy is a deep learning framework for designing models with complex architectures. Many important components such as LSTM and Batch Normalization are implemented inside. Although highly flexible, deepy maintains a clean high-level interface.
Deepy is a deep learning framework for designing models with complex architectures. Many important components such as LSTM and Batch Normalization are implemented inside. Although highly flexible, deepy maintains a clean high-level interface.
This video is an tutorial of TFLearn,tensorflow. It contain an example of deep learning based network classifier.
Keras is an open source neural network library written in Python. It is capable of running on top of TensorFlow, Microsoft Cognitive Toolkit, or Theano. Designed to enable fast experimentation with deep neural networks, it focuses on being user-friendly, modular, and extensible. It was developed as part of the research effort of project ONEIROS (Open-ended Neuro-Electronic Intelligent Robot Operating System), and its primary author and maintainer is François Chollet, a Google engineer.