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Deep Learning part -1 - mitsumi - 11-20-2025 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 ingle &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 [To see links please register or login] [To see links please register or login]