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.

Improved Content & Math View Correctio Added A View Full Screen To Read The Article With Full Screen Added Correctly Work On Dark And Light Mode Available from iOS 17 Chang the SubTitle name 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