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This repository is a related to all about Deep Learning - an A-Z guide to the world of Deep Learning. This supplement contains the implementation of algorithms, statistical methods and techniques (in Python)

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Course 01 - 📚🧑‍🎓📺📝Neural Networks and Deep Learning

Week 1-📚Chapter1: Introduction of Deep learning

Tutorial	Video	Code
🌐1-Understanding Basic Neural Networks	1-2-3	Content 3
🌐2-Supervised Learning with Neural Networks	1	Content 6
🌐3-Exploring the Different Types of Artificial Neural Networks	---	---
🌐4- Why is Deep Learning taking off?	1	---
🌐5-Best Free Resources to Learn Deep learning (DL)	---	---

Week 2-📚Chapter1:2 Logistic Regression as a Neural Network

Tutorial	Video	Code
🌐1- Binary Classification	1	Content 3
🌐2- Logistic Regression	1-2	Content 6
🌐3- Understanding the Logistic Regression Cost Function	1	---
🌐4-Understanding the Logistic Regression Gradient Descent	1-2	---
🌐5-Intuition about Derivatives	1
🌐6-Computation Graph	1-2	---
🌐7-Derivatives with a Computation Graph	1	---
🌐8-Logistic Regression Gradient Descent	1	---
🌐9-Gradient Descent on m Examples	1

Week 3-📚Chapter 3 Python and Vectorization

Tutorial	Video	Code
🌐1-Vectorization	1
🌐2-More Vectorization Examples	1

• Assignment 1: Python Basics with Numpy

##Alogrithems - DL0101EN-3-1-Regression-with-Keras-py-v1.0.ipynb - DL0101EN-3-2-Classification-with-Keras-py-v1.0.ipynb - Keras - Tutorial - Happy House v1.ipynb - Keras_for_Beginners_Implementing_a_Convolutional_Neural_Network - Keras_for_Beginners_Building_Your_First_Neural_Network.ipynb

• Need to Implement
  • Text Classification — CNN with LSTM
  • Step-by-Step Guide — Building a Prediction Model in Python
• Important Website
  • victorzhou