Deep Learning Crash Course Part-1 | Master Neural Networks & AI Fundamentals

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Deep Learning Crash Course Part-1 | Master Neural Networks & AI Fundamentals

Welcome to the first installment of our Deep Learning Crash Course! In this comprehensive video, we dive into the basics of deep learning, neural networks, and artificial intelligence to kickstart your journey in one of today’s most revolutionary technologies.

What You'll Learn:
Introduction to Deep Learning: Understand what deep learning is and how it differs from traditional machine learning.
Neural Network Fundamentals: Get a clear explanation of neurons, layers, activation functions, and how they all work together.
Key Concepts & Terminology: Learn essential terms like backpropagation, gradient descent, and overfitting.
Real-World Applications: Discover how deep learning is applied in fields like computer vision, natural language processing, and more.
Why Watch This Video?
Whether you’re a beginner eager to explore AI or a seasoned developer looking to refresh your knowledge, this crash course is designed to provide a strong foundation in deep learning. We break down complex concepts into easy-to-understand segments, ensuring that you gain practical insights to kickstart your projects.

Key Features:
Step-by-Step Explanations: Follow along with detailed visuals and clear narration.
Practical Examples: See real-life applications and demos that bring theory into practice.
Expert Tips: Learn from industry best practices and common pitfalls in deep learning.
Who Is This For?
Beginners in AI & Machine Learning
Software Developers & Data Scientists
Students & Academics
Don't Forget to:
👍 Like this video if you found it helpful
🔔 Subscribe for more in-depth tutorials and crash courses on deep learning, machine learning, and AI
💬 Comment below with your questions and topics you’d like us to cover in future videos

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Chapters:
00:00:00 Part 1
00:00:03 What will you learn?
00:02:29 What is Deep Learning?
00:06:49 AI vs ML vs DL
00:20:42 Small vs Big Data
00:23:11 What is a Neural Network?
00:44:26 Types of Neural Networks
00:51:32 Architecture of Neural Network
00:56:05 Single Layer vs Multi Layer Neural Network
00:59:20 Multilayer Perceptron
01:15:43 Types of Multilayer Perceptron
01:25:32 Applications of Multilayer Perceptron
01:30:01 Python Libraries and Installations for DL
01:46:58 Ten Step guide to create an ANN
01:57:49 Creating ANN with TensorFlow in Python
01:58:06 Simple Neural Network in TensorFlow
02:21:14 Using GPU for DL in TensorFlow
02:24:33 MLP in TensorFlow with Python
02:37:57 Call Back Function and Early Stopping
02:45:39 How many number of Neurons?
02:54:08 Activation Function
03:27:16 Linear Activation Function
03:30:48 Non-linear Activation Functions
03:33:06 Binary Step Activation Function
03:37:25 Sigmoid or Logistic Activation Function
03:48:04 tanH Activation Function
03:52:30 ReLu Activation Function
04:04:43 Leaky ReLu Activation Function
04:09:50 Parametric ReLu Activation Function
04:13:47 Softmax activation function
04:23:26 How to choose an Activation Function?
04:38:59 Computer Vision Basics
05:09:17 Computer Vision in Python
05:31:21 Convolutional Neural Network (CNN) Intro
05:45:24 CNN Advancement
06:20:30 CNN Coding in Python TF
06:57:22 CNN Key Concepts
07:24:16 CNN Image Classification Case Study
08:56:13 CNN Key Terms
09:09:23 CNN Project Fasion MNIST
09:41:22 CNN Project Rice Disease Detection
10:35:56 Summary
10:36:30 Crash Course Part2 Coming Soon