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Deep Learning with Python
Introduction
Introduction (4:44)
Python Setup
Installing Anaconda Distribution and Python (4:58)
Overview of Jupyter Notebook and Google Colab (5:32)
Fundamentals of Python
Data Types in Python (12:42)
Operators in Python (10:31)
Conditionals (9:49)
Loops (13:07)
Lists Tuples Dictionaries and Sets (17:54)
Data Type Operators and Methods (11:21)
Modules in Python (5:15)
Functions in Python (8:05)
Exercise Analyse (1:46)
Exercise Solution (10:46)
Object Oriented Programming
Logic of OOP (4:58)
Constructor (6:34)
Methods (4:41)
Inheritance (6:42)
Overriding and Overloading (10:33)
Numpy Library
What is Numpy (6:49)
Why Numpy (4:23)
Array and Features (12:08)
Array Operators (4:53)
Numpy Functions (18:25)
Indexing and Slicing (10:15)
Numpy Exercises (16:03)
Using Numpy in Linear Algebra (30:14)
NumExpr Guide (9:15)
Fundamentals of Machine Learning
AI, Machine Learning and Deep Learning (4:54)
History of Machine Learning (6:52)
Turing Machine and Turing Test (12:10)
What is Deep Learning (5:53)
Learning Representetion from Data (11:15)
Workflow of Machine Learning (9:45)
Machine Learning Methods (13:34)
Supervised Machine Learning Methods - 1 (8:47)
Supervised Machine Learning Methods - 2 (13:26)
Supervised Machine Learning Methods - 3 (13:53)
Supervised Machine Learning Methods - 4 (17:04)
Unsupervised Machine Learning Methods (23:58)
Gathering Data (4:54)
Data Pre-Processing (5:33)
Choosing The Right Algorithm and Model (7:49)
Training and testing the model (5:19)
Evaluation (6:52)
Artificial Neural Network
What is ANN (7:19)
Anatomy of a neural network (9:22)
Creating a Simple ANN (17:33)
Tensor Operations - 1 (14:04)
Tensor Operations - 2 (8:20)
Keras API (6:46)
Optimizers (10:40)
What is Tensorflow (17:40)
Convolutional Neural Network
What is CNN (15:16)
Recurrent Neural Network and LSTM
Understanding RNN and LSTM Networks (13:14)
Transfer Learning
What is Transfer Learning (16:08)
Projects
Project - 1 (22:32)
Project - 2 (28:40)
Project - 3 (15:33)
Project - 4 (16:12)
Optimizers
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