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30-Day Money-Back Guarantee
Development Data Science Neural Networks

Deep Learning and Neural Networks - Complete BootCamp [2020]

Master Deep Learning and Neural Networks from Scratch
Rating: 4.0 out of 54.0 (10 ratings)
3,290 students
Created by Abhishek Kumar
Last updated 5/2020
English
English [Auto]
30-Day Money-Back Guarantee

What you'll learn

  • Fundamentals of Neural Networks used in Deep Learning.

Course content

6 sections • 32 lectures • 5h 37m total length

  • Preview08:11

  • Supervised Learning
    07:05
  • Why is deep learning getting popular now?
    08:08

  • Preview07:58
  • 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
    08:29
  • More Vectorization examples using NumPy
    12:09
  • Vectorizing Logistic regression Forward Propagation
    12:20
  • Vectorizing Logistic regression Backpropagation
    13:44

  • 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

  • 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

Instructor

Abhishek Kumar
Computer Scientist at Adobe
Abhishek Kumar
  • 4.1 Instructor Rating
  • 294 Reviews
  • 9,348 Students
  • 16 Courses

I am working as Computer Scientist at Adobe. I have 7 years of extensive experience in Programming. I am a Machine Learning enthusiast and have 4+ years of experience in Machine Learning.

I am very passionate about learning the latest developments in Computer Vision and Machine Learning and like sharing my knowledge with others.


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