Python – Data Processing

Python – Data Processing

What you’ll learn

Understand the intuition behind Artificial Neural Networks
Build Deep Learning Models
Convolution Neural Networks
Sequence Models

Course content
Expand all 36 lectures02:29:53
–Introduction
09:34
Applications of Deep Learning
Preview
05:12
What is Deep Learning?
Preview
01:36
Why Deep Learning?
Preview
01:08
Why now?
Preview
01:38
–Fundamentals
04:13
Hello World of Deep learning
Preview
01:14
Dataset and Features
01:09
Classification
01:50
–Neural Networks
36:59

Perceptron
12:13
Sigmoid Function
06:39
Softmax Function
05:27
One Hot Encoding
03:56
Activation Functions
04:50
Logic Gates and XOR Problem
03:54
–Training Neural Networks
51:02
Cross Entropy
06:19
Loss Optimization
03:24
Gradient Descent
12:10
Non Linear Models
03:20
Feed Forward
03:00
Backward Propagation
02:18
Overfitting problem
04:42
Early Stopping
05:57
Regularization
04:26
Drop out
01:23
Vanishing Gradient Problem
04:03
–Convolution Neural Networks
32:32
Need for feature extraction
05:18
Preprocessing
01:39
Convolution Operation
21:10
Pooling Layer
04:25
Flattening
Processing..
–Sequence Models
15:33
Recurrent Neural Networks
05:05
LSTMs
03:08
Architecture of LSTMs
01:20
Forget Gate
02:41
Learn Gate
01:47
Remember Gate
00:38
Use Gate
00:54
Requirements

Just some high school mathematics
Basic linear algebra and calculus

Description

Interested in the field of Machine Learning and Deep Learning? Then this course is for you!

This course is designed in a very simple and easily understandable content.

You might have seen lots of  buzz on deep learning and you want to figure out where to start and explore.

This course is designed exactly for people like you!

If basics are strong, we can do bigger things with ease.

My focus in this course is to build complicated things starting from very basics

In this course, I will cover the following things

Session 1 – Introductory material on Deep learning, its applications and significance.

Session 2 –  Introduces the fundamental building block of deep learning

Session 3 – Logistic Regression, Activation Functions, Perceptron, One Hot Encoding, XOR problem and Multi-Layer Perceptron models

Session 4 – Training of Neural Networks: Cross Entropy, Loss Function, Gradient descent Algorithm, Non-Linear Models, Feed Forward, Backward propagation, Overfitting problem, Early stopping, Regularization, drop out and Vanishing Gradient problem.

Session 5 – Convolution Neural Networks:  Feature Extraction, Convolution Layer, Pooling Layer, Relu, Flattening and Deep Convolution Neural Networks.

Session 6 – Sequence Models: Recurrent Neural Networks, LSTMs

Are there any course requirements or prerequisites?

Just some high school mathematics level.

Who this course is for:

Anyone interested in Machine Learning and Deep Learning

Students who have high school knowledge in mathematics and who want to start learning Deep Learning

Any intermediate level people who know the basics of machine learning, who want to learn more advanced topics like deep learning

Any students in college who want to start a career in Data Science

Any data analysts who want to level up in Machine Learning and Deep Learning

Any people who are not satisfied with their job and who want to become a Data Scientist

Any people who want to create added value to their business by using powerful Learning tools

Build a foundation on the principles of Deep Learning to understand the latest trends

Who this course is for:

Anyone interested in Machine Learning and Deep Learning
Students who have high school knowledge in mathematics and who want to start learning Deep Learning
Any intermediate level people who know the basics of machine learning, who want to learn more advanced topics like deep learning
Any students in college who want to start a career in Data Science
Any data analysts who want to level up in Machine Learning and Deep Learning
Any people who are not satisfied with their job and who want to become a Data Scientist.
Any people who want to create added value to their business by using powerful Learning tools.
Build a foundation on the principles of Deep Learning to understand the latest trends

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