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Neural Networks
Deep Learning and Neural Networks - Complete BootCamp [2020]
3.9 (10 ratings)
Course Ratings are calculated from individual students’ ratings and a variety of other signals, like age of rating and reliability, to ensure that they reflect course quality fairly and accurately.
3,287 students enrolled

Deep Learning and Neural Networks - Complete BootCamp [2020]

Master Deep Learning and Neural Networks from Scratch
3.9 (10 ratings)
Course Ratings are calculated from individual students’ ratings and a variety of other signals, like age of rating and reliability, to ensure that they reflect course quality fairly and accurately.
3,287 students enrolled
Created by Abhishek Kumar
Last updated 5/2020
English
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Preview this course
Deep Learning and Neural Networks - Complete BootCamp [2020]
Current price: $139.99 Original price: $199.99 Discount: 30% off
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This course includes
  • 5.5 hours on-demand video
  • 32 downloadable resources
  • Full lifetime access
  • Access on mobile and TV
  • Certificate of Completion
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What you'll learn
  • Fundamentals of Neural Networks used in Deep Learning.
Course content
Expand all 32 lectures 05:37:51
+ – Types and Applications of Neural Networks
2 lectures 15:13
Supervised Learning
07:05
Why is deep learning getting popular now?
08:08
+ – Neural Networks Basics
14 lectures 02:35:02
Binary Classification
Preview 07:58
Logistic Regression
06:20
Logistic Regression Cost Function
11:02
Gradient Descent Algorithm
14:04
Derivatives - part 1
12:05
Derivatives - part 2
22:54
Derivatives - part 3 (Sum Rule)
06:06
Derivatives - part 4 (Derivative of exponential function)
08:11
Derivatives - part 5 (Product Rule)
10:33
Derivatives - part 6 (Quotient Rule)
11:24
Computation Graph
04:15
Derivatives with Computation Graph - Backpropagation
14:46
Logistic Regression Gradient Descent
13:10
Gradient Descent on m Examples
12:14
+ – Vectorization
4 lectures 46:42
Vectorization
08:29
More Vectorization examples using NumPy
12:09
Vectorizing Logistic regression Forward Propagation
12:20
Vectorizing Logistic regression Backpropagation
13:44
+ – NumPy Quickstart Guide
6 lectures 01:05:36
NumPy - Overview
08:39
Creating NumPy arrays
09:59
linspace(), zeros() and ones()
08:56
Indexing Arrays
14:33
Slicing NumPy Arrays
12:57
Broadcasting NumPy Arrays
10:32
+ – Shallow Neural Networks
5 lectures 47:07
One Hidden Layer Neural Network
05:58
Neural Network Representation
06:35
Neural Network Computation
10:15
Vectorizing computations across training examples
08:26
Activation Functions
15:53
Requirements
  • No
Description

A complete course on Deep Learning and Neural Networks concepts in a simplified and easy to understand manner. In this course, you will learn the foundations of deep learning. The course covers following:

  • Introduction to Neural networks and its Applications

  • Basics of Neural Networks

  • NumPy crash Course and Vectorization

  • Shallow Neural Networks

  • Deep Neural networks

  • Key parameters in neural network architecture

Let's dive into the course.

Who this course is for:
  • Data scientists, Research Engineers, Software Developers, Software Engineers, Engineering Students