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Universal Deep Learning Mastery – 2024 Edition with Updated

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Universal Deep Learning Mastery – 2024 Edition with Updated, available at $74.99, has an average rating of 4.4, with 107 lectures, 9 quizzes, based on 83 reviews, and has 654 subscribers.

You will learn about Deep learning using Keras to implement various problems like Binary Classification, Multi Class classification, & Regression Intuition on Deep Learning Neural Networks by implementing the code in Python using Keras Library Learn Python to kick start Deep Learning journey Build intuition on Various Models in Deep learning and Learning algorithms in Deep learning This course is ideal for individuals who are Anyone looking to start career in Deep learning or Anyone wants to build Deep learning – Neural networks or Anyone wants to implement Deep Learning using Keras or Anyone wants to learn to code in Python to implement Deep learning or Anyone wants to be in Latest Trend in technology – Deep learning It is particularly useful for Anyone looking to start career in Deep learning or Anyone wants to build Deep learning – Neural networks or Anyone wants to implement Deep Learning using Keras or Anyone wants to learn to code in Python to implement Deep learning or Anyone wants to be in Latest Trend in technology – Deep learning.

Enroll now: Universal Deep Learning Mastery – 2024 Edition with Updated

Summary

Title: Universal Deep Learning Mastery – 2024 Edition with Updated

Price: $74.99

Average Rating: 4.4

Number of Lectures: 107

Number of Quizzes: 9

Number of Published Lectures: 107

Number of Published Quizzes: 9

Number of Curriculum Items: 119

Number of Published Curriculum Objects: 119

Original Price: $199.99

Quality Status: approved

Status: Live

What You Will Learn

  • Deep learning using Keras to implement various problems like Binary Classification, Multi Class classification, & Regression
  • Intuition on Deep Learning Neural Networks by implementing the code in Python using Keras Library
  • Learn Python to kick start Deep Learning journey
  • Build intuition on Various Models in Deep learning and Learning algorithms in Deep learning

Who Should Attend

  • Anyone looking to start career in Deep learning
  • Anyone wants to build Deep learning – Neural networks
  • Anyone wants to implement Deep Learning using Keras
  • Anyone wants to learn to code in Python to implement Deep learning
  • Anyone wants to be in Latest Trend in technology – Deep learning

Target Audiences

  • Anyone looking to start career in Deep learning
  • Anyone wants to build Deep learning – Neural networks
  • Anyone wants to implement Deep Learning using Keras
  • Anyone wants to learn to code in Python to implement Deep learning
  • Anyone wants to be in Latest Trend in technology – Deep learning

Unlock the Future: Master Deep Learning from Scratch with Keras and TensorFlow – Your Gateway to Tomorrow’s Technology!

Have you heard the buzz about AI being the future, transforming industries, from self-driving cars to scientific discovery? The rise of deep learning, with advancements in hardware and software, has propelled AI to new heights. As the demand for deep learning experts grows, we present ‘Deep Learning from Scratch – Keras TensorFlow,’ a course designed by ManifoldAILearning to kickstart your journey into the world of deep learning.

Course Highlights:

1. Basic Nuts & Bolts of Deep Learning:

  • Lay the foundation with fundamental concepts.

  • Gain insights into the core principles of deep learning.

2. Crash Course on Python:

  • Refresh or enhance your Python skills.

  • A quick guide to Python essentials for deep learning.

3. Understanding Various Models in Deep Learning:

  • Explore diverse models crucial in deep learning.

  • Develop a comprehensive understanding of their applications.

4. Implement Deep Learning Neural Networks using Keras with TensorFlow Backend:

  • Hands-on experience with building neural networks.

  • Dive into the practical aspect of implementing deep learning models.

5. Implement Deep Learning on Common Types of Problems:

  • Tackle binary classification, multi-class classification, and regression problems.

  • Apply your deep learning knowledge to real-world scenarios.

Why Deep Learning 101?

1. Expert-Designed Course Structure:

  • A well-structured course catering to learners of all levels.

  • Exercises after each module to reinforce knowledge and boost confidence.

2. High-Quality Intuitive Tutorials:

  • Comprehensive and intuitive tutorials.

  • Theoretical concepts explained through videos, followed by practical implementations.

3. Practical Hands-On Exercise:

  • Code along with us in every practical section.

  • Build intuition on the functioning of each line of code.

  • Access downloadable codes and datasets for self-paced practice.

Embark on Your Deep Learning Journey:

Deep Learning 101 is designed to provide you with the essentials needed to kickstart your journey into the realm of deep learning. We believe that preparing for tomorrow’s technology starts today. Join us now and be part of the technological revolution shaping today and tomorrow.

“The best time to prepare for tomorrow’s technology is by learning today.”

  • Team ManifoldAILearning

Course Curriculum

Chapter 1: Welcome

Lecture 1: Introduction to Deep Learning 101

Lecture 2: Access Source Code

Chapter 2: Getting basics right

Lecture 1: Artificial Neural Networks

Lecture 2: Activation Function

Lecture 3: Bias

Lecture 4: Data

Lecture 5: Applications of Data

Lecture 6: Models

Lecture 7: Loss Functions

Lecture 8: Learning Algorithms & Model Performance

Chapter 3: Python Crash Course on Basics

Lecture 1: Getting System Ready – Jupyter Notebook

Lecture 2: Accessing Google Colab Notebook

Lecture 3: Download Materials

Lecture 4: Python Basics – Data Types

Lecture 5: Python Basics – Containers in Python

Lecture 6: Control Statements Python if..else

Lecture 7: Python Control statments – While and For

Lecture 8: Functions & Classes in Python

Chapter 4: Python for Data Science Crash Course

Lecture 1: Numpy Part 1

Lecture 2: Numpy Part 2

Lecture 3: Numpy Part 3

Lecture 4: Pandas in Python – Pandas Series

Lecture 5: Pandas Data Frame

Lecture 6: Pandas Data frame – cleaning & Examining the data

Lecture 7: Plotting with Matplotlib

Lecture 8: Contour Plots

Chapter 5: MP Neuron Model

Lecture 1: MP Neuron Introduction

Lecture 2: Intuition of data

Lecture 3: Loss & finding parameters

Lecture 4: Mathematical Intuition

Chapter 6: MP Neuron in Python

Lecture 1: Download Materials

Lecture 2: MP Neuron – Data import

Lecture 3: Train Test Split

Lecture 4: Modify Data

Lecture 5: MP Neuron in Python

Lecture 6: MP Neuron Class

Lecture 7: Assignment for MP Neuron in Python

Chapter 7: Summary of MP Neuron

Lecture 1: Summary of MP Neuron

Chapter 8: Perceptron

Lecture 1: Perceptron

Lecture 2: Perceptron Model and its representation

Lecture 3: Loss function & Parameter Update

Lecture 4: Why Update Rule Works

Lecture 5: Update Rule in Programs

Chapter 9: Perceptron in Python

Lecture 1: Download Materials

Lecture 2: Perceptron in Python

Lecture 3: Visualize the Accuracy with epochs

Lecture 4: Perceptron Assignment

Chapter 10: Sigmoid Neuron

Lecture 1: Download Materials

Lecture 2: Percepron Limitations

Lecture 3: Sigmoid Neuron Introduction

Lecture 4: Sigmoid Neuron Data

Lecture 5: Sigmoid Intuition

Lecture 6: Manual fitting of data

Lecture 7: Gradient descent

Lecture 8: Program overview

Lecture 9: Program in Python

Chapter 11: Sigmoid Neuron Implement with Python

Lecture 1: Download Materials

Lecture 2: Download Dataset

Lecture 3: Data Standardization -1

Lecture 4: Data Standardization – 2

Lecture 5: Class Sigmoid

Lecture 6: Sigmoid Assignment

Chapter 12: Basic Probability

Lecture 1: Introduction to Probability and Random Variables

Lecture 2: Why Random Variable is important

Lecture 3: Random Variable – Types

Lecture 4: Probability Distribution Table

Lecture 5: Why do we require Entropy Loss

Chapter 13: Why Deep Neural Networks – Intuition

Lecture 1: Download Materials

Lecture 2: Why Deep Neural Networks

Lecture 3: Linear Separation of Data

Chapter 14: Universal Approximation Theorem – Deep Learning Foundation

Lecture 1: Understanding Universal Approximation Theorem

Lecture 2: Confirming Universal Approximation Theorem Works

Lecture 3: Going deep into Neural Networks

Lecture 4: Challenges in Creating Deep Neural Networks from Scratch

Instructors

Rating Distribution

  • 1 stars: 1 votes
  • 2 stars: 1 votes
  • 3 stars: 3 votes
  • 4 stars: 22 votes
  • 5 stars: 56 votes

Frequently Asked Questions


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