CBSE Class 9: Structure of The Atom. Thomson proposed the model of an atom be similar to that of a Christmas pudding. His model included the explanation of positive and negative charges.
CBSE Class 9: Structure of The Atom. Ernest Rutherford was interested in knowing how the electrons are arranged within an atom. He conducted experiments to explain the model of an atom.
CBSE Class 9: The Fundamental Unit of Life. The movement of water molecules through such a selectively permeable membrane is called Osmosis. This is demonstrated with an activity.
Atal Innovation Mission, NITI Aayog has opened up once again applications from secondary schools ( having Grade VI upto Grade XII ) for the setup of Atal Tinkering Labs in the schools. This is aligned with our endeavour to ‘Cultivate 1 Million+ children in India as Neoteric Innovators’. Interested schools can apply by logging into the following link : http://aimapp2.aim.gov.in/atlopenapplication/ Schools are instructed to fill the application form, and submit desired documentation at the earliest. Schools are instructed to read the manual at the below link carefully before applying. Link: User Manual
Ministry of Human Resource Development (MHRD), Govt. of India has established ‘MHRD’s Innovation Cell (MIC)’ to systematically foster the culture of Innovation amongst all Higher Education Institutions (HEIs). The primary mandate of MIC is to encourage, inspire and nurture young students by supporting them to work with new ideas and transform them into prototypes while they are informative years. MHRD’s Innovation Cell (MIC) has envisioned encouraging creation of ‘Institution’s Innovation Council (IICs)’ across selected Higher Education Institutions (HEIs). A network of these IICs will be established to promote innovation in the Institution through multitudinous modes leading to an innovation promotion…
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/
This Video has a few examples of Machine Learning in Civil Engineering specific Sub- subjects with examples of applications already done.
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.
