Deep Learning Notes
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O aplikaciji
š Deep Learning Notes (2025ā2026 Edition)
š The Deep Learning Notes (2025ā2026) Edition is a complete academic and practical resource tailored for university students, college learners, software engineering majors, and aspiring developers. Covering the entire deep learning syllabus in a structured and student-friendly way, this edition combines a complete syllabus with practice MCQs and quizzes to make learning both effective and engaging.
This app provides a step-by-step guide to mastering deep learning concepts, starting from the basics of programming and progressing to advanced topics such as convolutional networks, recurrent neural networks, and structured probabilistic models. Each unit is carefully designed with explanations, examples, and practice questions to strengthen understanding and prepare students for academic exams and professional development.
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šÆ Learning Outcomes:
- Understand deep learning concepts from fundamentals to advanced programming.
- Reinforce knowledge with unit-wise MCQs and quizzes.
- Gain hands-on coding experience.
- Prepare effectively for university exams and technical interviews.
---
š Units & Topics
š¹ Unit 1: Introduction to Deep Learning
- What is Deep Learning?
- Historical Trends
- Deep Learning Success Stories
š¹ Unit 2: Linear Algebra
- Scalars, Vectors, Matrices, and Tensors
- Matrix Multiplication
- Eigendecomposition
- Principal Components Analysis
š¹ Unit 3: Probability and Information Theory
- Probability Distributions
- Marginal and Conditional Probability
- Bayes' Rule
- Entropy and KL Divergence
š¹ Unit 4: Numerical Computation
- Overflow and Underflow
- Gradient-Based Optimization
- Constrained Optimization
- Automatic Differentiation
š¹ Unit 5: Machine Learning Basics
- Learning Algorithms
- Capacity and Overfitting and Underfitting
š¹ Unit 6: Deep Feedforward Networks
- Architecture of Neural Networks
- Activation Functions
- Universal Approximation
- Depth vs. Width
š¹ Unit 7: Regularization for Deep Learning
- L1 and L2 Regularization
- Dropout
- Early Stopping
- Data Augmentation
š¹ Unit 8: Optimization for Training Deep Models
- Gradient Descent Variants
- Momentum
- Adaptive Learning Rates
- Challenges in Optimization
š¹ Unit 9: Convolutional Networks
- Convolution Operation
- Pooling Layers
- CNN Architectures
- Applications in Vision
š¹ Unit 10: Sequence Modeling: Recurrent and Recursive Nets
- Recurrent Neural Networks
- Long Short-Term Memory
- GRU
- Recursive Neural Networks
š¹ Unit 11: Practical Methodology
- Evaluating Performance
- Debugging Strategies
- Hyperparameter Optimization
- Transfer Learning
š¹ Unit 12: Applications
- Computer Vision
- Speech Recognition
- Natural Language Processing
- Game Playing
š¹ Unit 13: Deep Generative Models
- Autoencoders
- Variational Autoencoders
- Restricted Boltzmann Machines
- Generative Adversarial Networks
š¹ Unit 14: Linear Factor Models
- PCA and Factor Analysis
- ICA
- Sparse Coding
- Matrix Factorization
š¹ Unit 15: Autoencoders
- Basic Autoencoders
- Denoising Autoencoders
- Contractive Autoencoders
- Variational Autoencoders
š¹ Unit 16: Representation Learning
- Distributed Representations
- Manifold Learning
- Deep Belief Networks
- Pretraining Techniques
š¹ Unit 17: Structured Probabilistic Models for Deep Learning
- Directed and Undirected Graphical Models
- Approximate Inference
- Learning with Latent Variables
---
š Why Choose This App?
- Covers the complete deep learning syllabus in a structured format with MCQs, & quizzes for practice.
- Suitable for BS/CS, BS/IT, software engineering students, and developers.
- Builds strong foundations in problem solving and professional programming.
---
ā This app is inspired by the authors:
Ian Goodfellow, Yoshua Bengio, Aaron Courville
š„ Download Now!
Get your Deep Learning Notes (2025ā2026) Edition today! Learn, practice, and master deep learning concepts in a structured, exam-oriented, and professionalĀ way.
š The Deep Learning Notes (2025ā2026) Edition is a complete academic and practical resource tailored for university students, college learners, software engineering majors, and aspiring developers. Covering the entire deep learning syllabus in a structured and student-friendly way, this edition combines a complete syllabus with practice MCQs and quizzes to make learning both effective and engaging.
This app provides a step-by-step guide to mastering deep learning concepts, starting from the basics of programming and progressing to advanced topics such as convolutional networks, recurrent neural networks, and structured probabilistic models. Each unit is carefully designed with explanations, examples, and practice questions to strengthen understanding and prepare students for academic exams and professional development.
---
šÆ Learning Outcomes:
- Understand deep learning concepts from fundamentals to advanced programming.
- Reinforce knowledge with unit-wise MCQs and quizzes.
- Gain hands-on coding experience.
- Prepare effectively for university exams and technical interviews.
---
š Units & Topics
š¹ Unit 1: Introduction to Deep Learning
- What is Deep Learning?
- Historical Trends
- Deep Learning Success Stories
š¹ Unit 2: Linear Algebra
- Scalars, Vectors, Matrices, and Tensors
- Matrix Multiplication
- Eigendecomposition
- Principal Components Analysis
š¹ Unit 3: Probability and Information Theory
- Probability Distributions
- Marginal and Conditional Probability
- Bayes' Rule
- Entropy and KL Divergence
š¹ Unit 4: Numerical Computation
- Overflow and Underflow
- Gradient-Based Optimization
- Constrained Optimization
- Automatic Differentiation
š¹ Unit 5: Machine Learning Basics
- Learning Algorithms
- Capacity and Overfitting and Underfitting
š¹ Unit 6: Deep Feedforward Networks
- Architecture of Neural Networks
- Activation Functions
- Universal Approximation
- Depth vs. Width
š¹ Unit 7: Regularization for Deep Learning
- L1 and L2 Regularization
- Dropout
- Early Stopping
- Data Augmentation
š¹ Unit 8: Optimization for Training Deep Models
- Gradient Descent Variants
- Momentum
- Adaptive Learning Rates
- Challenges in Optimization
š¹ Unit 9: Convolutional Networks
- Convolution Operation
- Pooling Layers
- CNN Architectures
- Applications in Vision
š¹ Unit 10: Sequence Modeling: Recurrent and Recursive Nets
- Recurrent Neural Networks
- Long Short-Term Memory
- GRU
- Recursive Neural Networks
š¹ Unit 11: Practical Methodology
- Evaluating Performance
- Debugging Strategies
- Hyperparameter Optimization
- Transfer Learning
š¹ Unit 12: Applications
- Computer Vision
- Speech Recognition
- Natural Language Processing
- Game Playing
š¹ Unit 13: Deep Generative Models
- Autoencoders
- Variational Autoencoders
- Restricted Boltzmann Machines
- Generative Adversarial Networks
š¹ Unit 14: Linear Factor Models
- PCA and Factor Analysis
- ICA
- Sparse Coding
- Matrix Factorization
š¹ Unit 15: Autoencoders
- Basic Autoencoders
- Denoising Autoencoders
- Contractive Autoencoders
- Variational Autoencoders
š¹ Unit 16: Representation Learning
- Distributed Representations
- Manifold Learning
- Deep Belief Networks
- Pretraining Techniques
š¹ Unit 17: Structured Probabilistic Models for Deep Learning
- Directed and Undirected Graphical Models
- Approximate Inference
- Learning with Latent Variables
---
š Why Choose This App?
- Covers the complete deep learning syllabus in a structured format with MCQs, & quizzes for practice.
- Suitable for BS/CS, BS/IT, software engineering students, and developers.
- Builds strong foundations in problem solving and professional programming.
---
ā This app is inspired by the authors:
Ian Goodfellow, Yoshua Bengio, Aaron Courville
š„ Download Now!
Get your Deep Learning Notes (2025ā2026) Edition today! Learn, practice, and master deep learning concepts in a structured, exam-oriented, and professionalĀ way.
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Å to je novo
š Initial Launch of Deep Learning Notes
āØ Whatās Inside:
ā Complete syllabus covering deep learning fundamentals
ā Interactive MCQs & quizzes for self-assessment
ā Perfect for students & developers who want to master the subject
šÆ Suitable For:
š©āš Students of BSCS, BSIT, Software Engineering & ICS
š University & college exams (CS/IT related subjects)
š Test prep for certifications & technical assessments
š» Beginners aiming for freelancing & entry-level developer jobs
āØ Whatās Inside:
ā Complete syllabus covering deep learning fundamentals
ā Interactive MCQs & quizzes for self-assessment
ā Perfect for students & developers who want to master the subject
šÆ Suitable For:
š©āš Students of BSCS, BSIT, Software Engineering & ICS
š University & college exams (CS/IT related subjects)
š Test prep for certifications & technical assessments
š» Beginners aiming for freelancing & entry-level developer jobs
PodrŔka za aplikaciju
Informacije o programeru
kamran Ahmed
kamahm707@gmail.com
Sheer Orah Post Office, Sheer Hafizabad, Pallandri, District Sudhnoti
Pallandri AJK, 12010
Pakistan
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