Buying has: Invest Without any problems – Feature first

Buying stocks: Invest Safely - Part 1

Moneylife Foundation conducted its first exclusive workshop on investing in stocks. Debashis Basu, editor, Moneylife, took the participants through how and why many investors lose money in stocks and addressed how to pick stocks safely

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Buying in the Investment | Vijay Kedia | TEDxDSBInternationalSchool

Mr Vijay Kedia discusses the life failures that led him to his success. He discusses the benefits of investing in the stock market through his own life examples and the way he became financially independent. Vijay Kedia, MD of Kedia securities, is a private investor in the stock market having experience of more than three decades. Kedia, a commerce graduate, born and brought up in Kolkata joined stock market with zero capital.Some of his investments have appreciated 100 times in the last 10-12 years. He has been a key note speaker in S.P. Jain, Welingkar Institute of Management, HR College, St. Xaviers, Kolkata, Bombay Stock Exchange, The Institute of Company Secretaries of India and The Institute of Chartered Accountants of India, Rajkot, CFA Institute, Kolkata and also in IIM Ahmedabad, IIM Bangalore, London Business School and TEDxIIMAmritsar. He is also on the board of Atul Auto Ltd. a Gujarat based Automobile Company, having a market cap of around Rs.1000 Crs. He is the largest shareholder in more than 10 listed companies. In 2017, Kedia was conferred with a doctorate degree. Kedia is very passionate about singing and yoga as well. Recently he has started investing in startups also.“ This talk was given at a TEDx event using the TED conference format but independently organized by a local community. Learn more at
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LSTM Signals – The mathematics of Information (Long weekend 8)

Recurrent Networks can be improved to remember long range dependencies by using whats called a Long-Short Term Memory (LSTM) Cell. Let’s build one using just numpy! I’ll go over the cell components as well as the forward and backward pass logic.

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Recurrent Neural Networks Tutorial, Part 1 – Introduction to RNNs

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