描述
Master artificial intelligence and machine learning at your own pace with Day-by-Day Deep Learning—a dynamic and interactive learning app designed to simplify complex topics like neural networks, CNNs, RNNs, Transformers, and more. Whether you’re just starting your AI journey or advancing your deep learning expertise, this app offers everything you need to learn effectively.
Key Features:
• Interactive Flashcards: Learn with bite-sized flashcards designed for clarity and retention, and mark your progress as you go.
• Personalized Notes for Each Topic: Add and save your own notes directly within each topic, making it easy to personalize your learning journey.
• Bookmark and Track Progress: Bookmark key topics for quick reference and monitor your overall progress with a detailed dashboard that keeps you motivated.
• Mark as Read: Stay organized by marking lessons as read and tracking what’s left to explore.
• Offline Access: Learn anytime, anywhere, without needing an internet connection.
• Powerful Search Functionality: Quickly find topics or lessons using the robust search tool.
• Beginner to Advanced Topics: Cover foundational concepts like supervised learning and neural networks, then dive into advanced topics like NLP, Transformers, and Reinforcement Learning.
• Math & Code Support: View beautifully rendered equations and syntax-highlighted code examples for a seamless and professional learning experience.
Take your deep learning skills to the next level. Whether you’re learning for work, school, or personal growth, Day-by-Day Deep Learning gives you the tools to succeed. Download now and start your journey to becoming an AI expert today!
新功能
- 版本: 2.2
- 發佈日期:
- Fixed the material of the content, the material are more up to date for 2025 and all information got better and more up to date :
Now is 80 articles fixed and updated for 2025 to learn ai - deep learning step by step in 80 days : topics covered are :
80 days topics include in this app are :
Intro to ML – ML types, models, train-test split.
ML in iOS – Using ML in apps.
Model Types – Supervised vs. unsupervised.
Regression & Classification – Basics with MNIST.
SGD Math – How it works.
Normal Equation – Prediction without iteration.
Gradient Descent – Concept and application.
Types of GD – Batch, Stochastic, Mini-Batch.
Perceptrons – Deep learning basics.
MLPs – Regression vs. classification.
Activation Functions – ReLU, Sigmoid, etc.
Non-Linearity – Hidden layers & activation.
Intro to Keras – Building models.
Keras APIs – Sequential, Functional, Subclassing.
Keras API Comparison – Sequential vs. Functional.
TensorFlow Tools – TensorBoard, callbacks, saving models.
Hyperparameter Tuning – With Keras Tuner.
Manual vs. Auto Optimization – Tuning models.
Bayesian Optimization – Neural network tuning.
Vanishing Gradient – Explanation.
Weight Initialization – Strategies.
Monetizing AI APIs – Creating paid APIs.
Weight Init: Part 2 – Advanced methods.
Advanced Activation – GELU, Mish, etc.
Batch Norm – How it works.
Batch Norm: Part 2 – Further details.
Batch Norm Parameters – Trainable/non-trainable.
Gradient Clipping – Avoid exploding gradients.
Transfer Learning – Basics.
Transfer Learning Example – Implementing it.
Labeled vs. Unlabeled Data – Differences.
Speeding Up Training – Optimization tricks.
Momentum Optimization – Deep dive.
Momentum vs. Normalization – Comparisons.
Momentum: Part 3 – Advanced details.
NAG Optimizer – Nesterov Accelerated Gradient.
AdaGrad – Origins & proof.
Optimizer Comparison – AdaGrad, RMSProp, Adam.
Adam vs. Other Optimizers – Pros & cons.
Adam & Local Minima – Understanding behavior.
More Optimizers – NAdam, AdaMax, AdamW.
Learning Rate Schedules – Why they matter.
1Cycle Schedule – Explanation.
Gradient Clipping & Weight Init – Combined effect.
LR Scheduling Methods – 1-Cycle, CED, Exponential.
TensorFlow vs. PyTorch vs. MLX – Frameworks compared.
Regularization – Preventing overfitting.
Dropout – Including MC Dropout.
Max-Norm Regularization – Explanation.
Deep vs. Dense Networks – Differences.
Deep Learning Use Cases – Overview.
ML in iOS Apps – Integration.
CNNs – Basics & use cases.
CNN Math – How it works.
RNNs: Part 1 – Sequence modeling.
RNNs: Part 2 – More details.
RNNs & Time Series – Forecasting.
RNN vs. Feedforward – Mathematical comparison.
ARIMA & SARIMA – Before diving into RNNs.
RNN Step-by-Step – Time series forecasting.
Seq2Seq Forecasting – Iterative vs. direct.
LSTMs & Layer Norm – RNN enhancements.
RNNs for NLP – Language modeling.
Why Transformers Win in NLP – Key insights.
Transformers Overview – GPT to DeepSeek.
Transformer Breakthroughs – ChatGPT to DeepSeek.
BERT Explained – Key insights.
How ChatGPT Works – Basics.
ChatGPT vs. BERT – Understanding comparison.
ChatGPT: Step-by-Step – Breakdown.
NLP Mathematics – Behind modern AI models.
Transformers in Vision & Multimodal AI – Expansion.
Autoencoders, GANs, & Diffusion – Overview.
Stacked Autoencoders – Unsupervised pretraining.
Diffusion Models – Breaking them down.
GANs – Deep learning for image generation.
How DALL·E Works – Image synthesis.
Reinforcement Learning – Applications & impact.
DeepNet – Scaling Transformers to 1,000 layers.
DeepSeek-R1 – Advancing LLM reasoning.
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- 最大值: $9.99
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