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

You can book One to one consultancy session with me on Mentoga: https://mentoga.com/muhammadaammartufail #codanics #dataanalytics #pythonkachilla #pkc24 ✅✅✅✅✅✅✅✅✅✅✅✅✅✅✅✅✅✅✅✅✅ Python ka chilla 2024 You can now register for Python ka chilla 2024 This is a paid course which you can register and find more information at the following link: https://forms.gle/kUU3eZJsFRb7Cn6r8 ✅✅✅✅✅✅✅✅✅✅✅✅✅✅✅✅✅✅✅✅✅ Here you can access all the codes and datasets from Python ka chilla 2024: https://github.com/AammarTufail/pytho... ✅✅✅✅✅✅✅✅✅✅✅✅✅✅✅✅✅✅✅✅✅ --------------------------------------------------------------------------------------------------------------------------------------- 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 --------------------------------------------------------------------------------------------------------------------------------------- ✅✅✅RoadMap to Data Analytics✅✅✅ Complete RoadMap to Become a Data Analyst in 2024. Learn and Practice these skills and courses from our youtube Channel Codanics or our website www.codanics.com ✅Our Free Books: https://codanics.com/books/abc-of-sta... ✅Our website: https://www.codanics.com ✅Our Courses: https://www.codanics.com/courses ✅Our YouTube Channel:    / @codanics   ✅ Our whatsapp channel: https://whatsapp.com/channel/0029Va7n... ✅Our Facebook Group:   / codanics   ✅Our Discord group for community Discussion:   / discord   ✉️For more Details contact us at [email protected] 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