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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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Topic List AI ACADEMY : DEEP LEARNING
Machine Learning (ML) Overview - Day 1
Integrate ML Into iOS Apps - Day 2
Models-Based, Instance Models, Train-Test Splits: The Building Blocks of Machine Learning - Day 3
Regression & Classification With MNIST - Day 4
Mathematical Explanation Behind SGD Algorithm - Day 5
Can We Make Predictions Without Iteration? - Day 6
What Is Gradient Descent? - Day 7
Three Types of Gradient Descent: Batch, Stochastic & Mini-Batch - Day 8
Deep Learning: Perceptrons - Day 9
Regression vs. Classification in MLPs - Day 10
Activation Function - Day 11
Activation Function, Hidden Layer, and Non-Linearity - Day 12
What Is Keras? - Day 13
Sequential, Functional, and Subclassing API in Keras - Day 14
Sequential vs. Functional Keras API: Part 2 - Day 15
TensorFlow: Using TensorBoard and Callbacks - Day 16
Hyperparameter Tuning With Keras Tuner - Day 17
Automatic vs. Manual Optimization in Keras - Day 18
Exploring Bayesian Optimization - Day 19
Vanishing Gradient Explained in Detail - Day 20
Weight Initialization in Deep Learning - Day 21
How to Create an API With Deep Learning - Day 22
Weight Initialization: Part 2 - Day 23
Activation Function Progress: Relu, Elu, Mish, etc. - Day 24
Batch Normalization - Day 25
Batch Normalization: Part 2 - Day 26
Batch Normalization: Trainable vs. Non-Trainable - Day 27
Understanding Gradient Clipping - Day 28
Transfer Learning - Day 29
How to Perform Transfer Learning: Examples - Day 30
Labeled vs. Unlabeled Data in ML - Day 31
Deep Neural Network Optimization Techniques - Day 32
Momentum Optimization Explained - Day 33
Momentum vs. Normalization - Day 34
Momentum: Part 3 - Day 35
NAG as an Optimizer - Day 36
AdaGrad: Origins and Mathematical Proof - Day 37
AdaGrad vs. RMSProp vs. Adam - Day 38
Adam vs. SGD vs. AdaGrad - Day 39
Adam Optimizer: Understanding Local Minimum - Day 40
Optimizers: NAdam, AdaMax, AdamW, etc. - Day 41
Learning Rates and Schedules Explained - Day 42
1-Cycle Scheduling and Learning Rates - Day 43
Gradient Clipping and Weight Initialization - Day 44
Learning Rate: CED and Exponential Decay - Day 45
Comparing TensorFlow, PyTorch, and MLX - Day 46
Understanding Regularization in Deep Learning - Day 47
Dropout and Monte Carlo Dropout - Day 48
Max-Norm Regularization in Deep Learning - Day 49
Deep Neural Networks vs. Dense Networks - Day 50
Deep Learning Examples: Overview - Day 51
Integrating DL Models Into iOS Apps - Day 52
CNN: Convolutional Neural Networks Explained - Day 53
Mathematics Behind CNNs - Day 54
RNN Deep Learning: Part 1 - Day 55
Understanding RNNs: Part 2 - Day 56
Time Series Forecasting With RNNs - Day 57
RNNs vs. FNNs: Understanding the Math - Day 58
Learn RNNs by Understanding ARIMA - Day 59
Step-by-Step RNN Forecasting - Day 60
Iterative and Seq2Seq Models for Forecasting - Day 61
RNN, Layer Normalization, and LSTMs - Day 62
Natural Language Processing and RNNs - Day 63
Why Transformers Are Better for NLP - Day 64
The Transformer Model Revolution - Day 65
Transformers in Deep Learning - Day 66
BERT Explained in 2 Minutes - Day 67
ChatGPT for Clinical and Medical Applications - Day 68
ChatGPT vs. BERT: Comparison - Day 69
How ChatGPT Works Step by Step - Day 70
Mastering NLP With a Scientific Paper Study - Day 71
Transformers in Vision and Multimodal Models - Day 72
Secrets of Autoencoders, GANs, and Diffusion Models - Day 73
Unsupervised Pretraining With Autoencoders - Day 74
Breaking Down Diffusion Models - Day 75
GANs in Deep Learning - Day 75
How DALL·E Image Generator Works - Day 77