⚪️ Basics of Neural Network Programming

✒️ by prof. Andrew Ng
🔹Source: Coursera

🔖 Lecture 17 Broadcasting in Python

Neural Networks and Deep Learning
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#free #python #supervised_learning
#broadcasting #python

⚪️ Basics of Neural Network Programming

✒️ by prof. Andrew Ng
🔹Source: Coursera

🔖 Lecture 18 A Note on Python Numpy Vectors

Neural Networks and Deep Learning
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⚪️ Basics of Neural Network Programming

✒️ by prof. Andrew Ng
🔹Source: Coursera

🔖 Lecture 19 Quick Tour of Jupyter iPython Notebooks

Neural Networks and Deep Learning
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🔹The 5 Basic Statistics Concepts Data Scientists Need to Know

Statistics can be a powerful tool when performing the art of Data Science (DS). From a high-level view, statistics is the use of mathematics to perform technical analysis of data. A basic visualisation such as a bar chart might give you some high-level information, but with statistics we get to operate on the data in a much more information-driven and targeted way. The math involved helps us form concrete conclusions about our data rather than just guesstimating.
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link: https://towardsdatascience.com/the-5-basic-statistics-concepts-data-scientists-need-to-know-2c96740377ae

#statistics #datascience
#machinelearning
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#deeplearning

⚪️ Basics of Neural Network Programming

✒️ by prof. Andrew Ng
🔹Source: Coursera

🔖 Lecture 20 Explanation of Logistic Regression's Cost Function

Neural Networks and Deep Learning
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🔹 Reinforcement Learning

Acme: A research framework for reinforcement learning

Github: https://github.com/deepmind/acme

Paper: https://arxiv.org/abs/2006.00979
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#neuralnetworks #python #math
#statistics #reinforcement #Acme

⚪️ Basics of Neural Network Programming

✒️ by prof. Andrew Ng
🔹Source: Coursera

🔖 Lecture 21 Neural Network Overview

Neural Networks and Deep Learning
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⚪️ Basics of Neural Network Programming

✒️ by prof. Andrew Ng
🔹Source: Coursera

🔖 Lecture 22 Neural Network Representations

Neural Networks and Deep Learning
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⚪️ Basics of Neural Network Programming

✒️ by prof. Andrew Ng
🔹Source: Coursera

🔖 Lecture 23 Computing Neural Network Output

Neural Networks and Deep Learning
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⚪️ Basics of Neural Network Programming

✒️ by prof. Andrew Ng
🔹Source: Coursera

🔖 Lecture 24 Vectorizing Across Multiple Examples

Neural Networks and Deep Learning
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⚪️ Basics of Neural Network Programming

✒️ by prof. Andrew Ng
🔹Source: Coursera

🔖 Lecture 25 Explanation For Vectorized Implementation

Neural Networks and Deep Learning
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⚪️ Basics of Neural Network Programming

✒️ by prof. Andrew Ng
🔹Source: Coursera

🔖 Lecture 26 Activation Functions

Neural Networks and Deep Learning
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⚪️ Basics of Neural Network Programming

✒️ by prof. Andrew Ng
🔹Source: Coursera

🔖 Lecture 27 Why Non-linear Activation Functions

Neural Networks and Deep Learning
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