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Deep Learning part -1
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Deep Learning part -1
Published 11/2025
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Language: English | Duration: 1h 7m | Size: 470 MB
Start your deep learning journey with clear lessons on ANN, feed forward networks, and backpropagation
What you'll learn
Introduction to deep learning
Functions of Deep learning
Definition of Deep Learning
Few Insights about AI,ML,DL
Definition of Supervised Machine Learning with diagram
Definition of Unsupervised Machine Learning with diagram
Definition of Reinforcement Machine Learning with diagram
Difference between Machine Learning and Deep Learning
Historical Trends in Deep Learning
Why Deep Learning is growing
Deep learning block diagram
Types of neural networks
Definition of Feed forward neural networks with diagram
Definition of Convolutional Neural Networks with diagram
Definition of Recurrent neural networks with diagram
Applications of Deep Learning
Deep learning based applications
Computer vision with its applications
Natural Language Processing and its application
Reinforcement learning and its application
Popular Specific applications of Deep Learning
5 Most common types of Deep learning vision models
Advantages and disadvantages of Deep Learning
Definition of Artificial neural networks with diagram
Artificial neurons vs Biological neurons
How do ARTIFICIAL NEURAL NETWROKS learn
Types of Artificial neural networks
Applications of artificial neural networks
Neural Network,Non-linear classification example using Neural Networks: XOR/XNOR
XOR problem with neural networks
The linear separability of points
Need for linear separability in neural networks
How to solve the XOR problem with neural networks
For X1=0 and X2=0 we should get an input of 0.Let us solve it.
How does it works
Types of Architecture :single &Multi layer perceptron
Feed forward neural network with example
key components of a feedforward neural network
Structure of a Feedforward Neural Network
Activation Functions
Training a Feedforward Neural Network
Gradient Descent
Evaluation of Feedforward neural network
Implementation of Feedforward Neural Network
Back propagation in Neural Network
Working of Back Propagation Algorithm
Forward Pass Work and Backward Pass
Example of Back Propagation in Machine Learning with forward and backward propagation
Solution for Assume the neurons use the sigmoid activation function for the forward and backward pass. The target output is 0.5 and the learning rate is 1.
Advantages and challenges of Back propagation in neural network
Requirements
You don't need any prior experience in deep learning or AI - I'll guide you step by step.
Basic Python knowledge can be helpful, but don't worry if you're new. We'll learn everything together.
Some very simple math (like basic algebra and matrix ideas) will make things easier, but it's totally okay if you just have the interest to learn.
Most importantly, come with curiosity, patience, and excitement to explore deep learning. I'll support you throughout your learning journey.
Description
Welcome to this beginner-friendly Deep Learning Fundamentals course!If you've always wanted to understand how machines learn, how neural networks work, and how modern AI systems are built, this course will guide you step by step in a simple, clear, and supportive way.In this course, you will start by learning the core foundations of Deep Learning-perfect for students, beginners, career-switchers, and anyone curious about AI.We begin with the basics such as types of deep learning, move into understanding artificial neural networks, and then explore how information flows through a feed forward networkYou will then learn the essential concept that allows all neural networks to learn: backpropagationEach lesson is designed in a smooth, easy-to-follow manner with simple explanations, real-world examples, and clear visuals. You will not feel lost at any stage-this course gently takes you from zero knowledge to a strong conceptual understanding.By the end of the course, you will have a solid foundation in how deep learning models are built, how they learn, and why they are used in today's AI systems . These concepts will prepare you for advanced topics like CNNs, RNNs, optimization, regularization, and more.What You Will LearnTypes of Deep Learning and where they are usedThe structure and working of Artificial Neural Networks (ANN)How data flows through Feed Forward Neural NetworksThe complete intuition behind BackpropagationHow neural networks update weights and learn from dataEssential foundations needed for advanced deep learningWhy This Course is Perfect for YouNo prior deep learning experience neededFriendly explanations-no complicationConcepts taught step by stepBeginner-supportive teaching styleBuilds strong fundamentals for advanced AI topics
Who this course is for
Complete beginners who are curious about AI and want to start from the basics
Students in computer science, engineering, or related fields looking to strengthen their understanding
Working professionals who want to switch to AI/ML roles or upgrade their skills
Researchers who want a strong foundation in neural networks and model building
Teachers and educators looking for structured content to introduce deep learning to learners
Anyone who loves technology and wants to understand how modern AI systems work
Freelancers who want to add deep learning skills to their portfolio
Aspiring data scientists / ML engineers who want job-ready deep learning knowledge
Kod:
https://rapidgator.net/file/841240f45523c0728cda305f7ec8b25f/Deep_Learning_Part_-1.rar.html
Kod:
https://ddownload.com/htb38df4acg9/Deep_Learning_Part_-1.rar
