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Gate Smashers via YouTube Video playlist

Deep Learning (ANN, RNN, Tranformers, RNN)

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Overview

Artificial Neural Networks (ANNs) are at the core of Deep Learning & Artificial Intelligence (AI), transforming industries from healthcare to finance. Whether you're a beginner, student, or preparing for competitive exams, certifications, or job interviews, this complete ANN playlist covers everything you need to become an AI & Deep Learning expert!

What You’ll Learn in This Playlist?

✅ Introduction to Neural Networks – How ANNs mimic the human brain

✅ Perceptron Model & Activation Functions – Sigmoid, ReLU, Softmax explained

✅ Feedforward Neural Networks (FNN) – How data moves in a neural network

✅ Backpropagation & Gradient Descent – Training ANNs efficiently

✅ Deep Neural Networks (DNNs) – Layers, architectures, and optimizations

✅ Convolutional Neural Networks (CNNs) – Image recognition & computer vision

✅ Recurrent Neural Networks (RNNs) & LSTMs – Time series & NLP applications

✅ Loss Functions & Optimization Techniques – Adam, RMSprop, and more

✅ AI & ANN in Real-Life Applications – Healthcare, self-driving cars, finance, and more

✅ Career Opportunities in AI & Deep Learning – Job roles, certifications, and roadmap

Who Is This Playlist For?

✔️ Engineering, BCA, MCA, and IT Students preparing for university exams

✔️ Beginners who want to start a career in AI & Deep Learning

✔️ Job seekers preparing for AI, Data Science, and Machine Learning interviews

✔️ IT Professionals & Developers looking to upskill in Neural Networks & AI

✔️ Entrepreneurs & Startups exploring AI-powered business solutions

🔥 Why Watch This Playlist?

🔹 40+ well-structured videos covering university syllabus & real-world applications

🔹 Hands-on demonstrations & coding examples using Python, TensorFlow, & PyTorch

🔹 Beginner-friendly yet industry-level depth

🔹 Explains top interview & AI certification topics in an easy-to-understand way

🔔 Subscribe now & start your journey in Artificial Neural Networks & Deep Learning! 🚀

#ArtificialNeuralNetworks #DeepLearning #MachineLearning #AI #NeuralNetworks #Backpropagation #GradientDescent #Perceptron #ANN #CNN #RNN #LSTMs #ActivationFunctions #ReinforcementLearning #AIinBusiness #AIinHealthcare #AIinFinance #DataScience #PredictiveAnalytics #AIModelTraining #SupervisedLearning #UnsupervisedLearning #TensorFlow #PyTorch #AIInterviewQuestions #AIResearch #NeuralNetworkArchitecture

Videos in this playlist 35

  1. Deep Learning | Complete Syllabus Discussion
  2. Understand Artificial 🤖Neural Networks🦾 from Basics with Examples | Components | Working
  3. Why Activation Function is Must in ANN | Artificial Neural Network
  4. Sigmoid Activation function | Artificial Neural Network
  5. Hyperbolic Tangent (Tanh) activation function
  6. ReLU & Leaky ReLU Activation Function
  7. Loss Function In Neural Network & Various Types of Loss Functions
  8. Mean Squared Error vs Mean Absolute Error | Loss Functions
  9. Huber Loss Function in Neural Network | Artificial Neural Networks
  10. Hinge Loss Function in Neural Network | Artificial Neural Networks
  11. Binary Cross Entropy (Log Loss) | Artificial Neural Networks
  12. Categorical Cross Entropy | Artificial Neural Networks
  13. Artificial vs. Convolutional Neural Network with Real Life Examples | Beginners Friendly
  14. Various Activation Functions with Real life Use-cases
  15. Convolutional Padding in CNN
  16. What is Convolution in CNN | Artificial vs Convolutional Neural Network
  17. Stride Convolution | Stride-1, Stride-2, Stride-3
  18. Introduction to Pooling Layer in CNN | Max Pooling, Average Pooling
  19. What is Sequence Data, Types & Models with Real life examples
  20. Introduction to Gradient Descent | How Models Minimize Loss
  21. Architecture of Recurrent Neural Networks (RNN)
  22. Introduction to Transfer Learning With Execution
  23. Comparison of All ANN Models | Real life Examples
  24. Introduction to Word Embedding | Static vs Dynamic Embedding | Deep Learning
  25. Introduction to LSTM (Long Short-Term Memory) | Deep Learning
  26. Forget Gate in LSTM | Deep Learning
  27. Input Gate in LSTM | Deep Learning
  28. Output Gate in LSTM | Deep Learning
  29. Introduction to Encoder-Decoder Architecture | Sequence to Sequence Model
  30. Introduction to RAG (Retrieval Augmented Generation) | Deep Learning
  31. How LLM Works? Simplified Self Attention | Deep Learning
  32. Softmax Activation Function | Deep Learning
  33. How GPT Predicts the Next Word? Masked Attention Explained Simply
  34. How GPT Thinks? Multi-Head Attention Explained Simply | Transformers & LLMs
  35. The AI Breakthrough That Changed Everything | Self-Attention Explained

Advantages and disadvantages

Advantages

  • Free and complete: every lecture is in the playlist, in order.
  • Watch at 1.5x, skip what you know, rewatch what you don't.
  • Taught in Hindi (or Hinglish), which many students find easier to follow.
  • Made for Indian students, with placement and exam context.
  • 567,592 views, so help and notes are easy to find.
  • Completely free.
  • Self-paced: start any time.
  • A clear syllabus (35 videos) you can see before you start.

Disadvantages

  • No certificate, deadlines or graded work.
  • Quality and depth vary: check that the playlist is finished before you start.
  • Less depth than a university course on some topics.
  • No certificate.
  • No graded assignments or feedback.

Some points apply to every course of this kind; see how we rank.

Free

  • Free: Watch on YouTube, no account needed.
  • Certificate: None. Code or take notes along to make it stick.

Gate Smashers is in Tier 3: good universities, respected companies, nonprofits and well-known teachers of our institution ranking (76/100). GATE and university CS subjects in Hindi.

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