• Home
  • Deep Learning 2024 – 2025
    • Announcement : ai chat by api
    • ONNX vs. Core ML: Choosing the Best Approach for Model Conversion in 2024
    • Core ML vs (PyTorch – TensorFlow)for deep learning integration on iOS app on Xcode using SWIFT
    • Announcement : New iOS app coming for Christmas
    • How to Monetize Your Deep Learning Skills and Models as a Solo Developer in 2024 – 2025
    • Understanding Tokens in Deep Learning: Types, Examples, and Use Cases -LLM
    • Understanding Computation Graphs in Pytorch vs Tensorflow
    • Compare iPad M4 chip, Mac M4 Chip , Cloud & GoogleColab for building deep learning model – day 10
    • Solo Developer’s Guide to Building Competitive Language Model Application – day 9
    • Where to Get Data for Machine Learning and Deep Learning Model Creation – day 8
    • GPU and Computing Technology Comparison 2024 – day 7
    • Fine-Tuning vs. Transfer Learning in Voice Synthesis
    • Fine-Tuning in Deep Learning with a practical example – day 6
    • DeepNet – Scaling Transformers to 1,000 Layers new for 2024 – day 5
    • Transformers seems most famous for deep learning on 2024 so lets learn it more – day 4
    • New on 2024 – 2025 are MLX and Transofermers so lets compare Custom Deep Learning Models for iOS with MLX on Apple Silicon vs. PyTorch – day 2
    • Deep Learning in 2024: Continued Insights and Strategies – day 1
  • Application
    • Ai Academy : Deep Learning
    • German Grammar B1
    • ai chat by api Application
    • Flash Card
    • Video Voice Change
    • Todo
    • background img remove INGOAMPT
    • Video Voice Edit INGOAMPT
  • Machine learning _ Deep learning ( day by day )
    • Reinforcement Learning: An Evolution from Games to Real-World Impact – day 77
    • How Dalle Image Generator works ? – day 76
    • Breaking Down Diffusion Models in Deep Learning – Day 75
    • Understanding Unsupervised Pretraining Using Stacked Autoencoders – day 74
    • Unlock the Secrets of Autoencoders, GANs, and Diffusion Models – Why You Must Know Them? -Day 73
    • The Rise of Transformers in Vision and Multimodal Models – Hugging Face – day 72
    • Mastering NLP: Unlocking the Math Behind It for Breakthrough Insights with a scientific paper study – day 71
    • How ChatGPT Work Step by Step – day 70
    • Can ChatGPT Truly Understand What We’re Saying? A Powerful Comparison with BERT” – day 69
    • Leveraging Scientific Research to Uncover How ChatGPT Supports Clinical and Medical Applications – day 68
    • Do you want to read a summery of what is BERT in 2 min read? (Bidirectional Encoder Representations from Transformers) – day 67
    • Transformers Deep Learning – day 66
    • The Revolution of Transformer Models – day 65
    • why transformers are better for NLP ? Let’s see the math behind it – Day 64
    • Natural Language Processing (NLP) and RNN – day 63
    • RNN, Layer Normalization, and LSTMs – Part 8 of RNN Deep Learning- day 62
    • Iterative Forecasting which is Predicting One Step at a Time 2- Direct Multi-Step Forecasting with RNN 3- Seq2Seq Models for Time Series Forecasting – day 61
    • Step-by-Step Explanation of RNN for Time Series Forecasting – part 6 – day 60
    • To learn what is RNN (Recurrent Neural Networks ) why not understand ARIMA, SARIMA first ? – RNN Learning – Part 5 – day 59
    • Understanding RNNs: why compare it with Feedforward Neural Networks with simple Example to show the Math Behind it ? – DAY 58 INGOAMPT
    • Time Series Forecasting with Recurrent Neural Networks (RNNs) – part 3 – day 57
    • Understanding Recurrent Neural Networks (RNNs) – part 2 – day 56
    • RNN Deep Learning – Part 1 – Day 55
    • Mathematics Behind CNN or Convolutional Neural Network in Deep Learning – Day 54
    • CNN- Convolutional Neural Networks – DAY 53
    • Deep Learning Models integration for iOS Apps – briefly explained – Day 52
    • How to Configure and Optimize Deep Neural Networks for Performance and Efficiency – Day 50
    • Learn Max-Norm Regularization to avoid overfitting : Theory and Importance in Deep Learning and proof – day 49
    • DropOut and Monte Carlo Dropout (MC Dropout)- Day 48
    • Understanding Regularization in Deep Learning – day 47
    • Comparing TensorFlow (Keras), PyTorch, & MLX – Day 46
    • Learning rate – part 4- 1-Cycle Scheduling, exponential decay and Cyclic Exponential Decay (CED) – day 45
    • Exploring Gradient Clipping and Weight Initialization in Deep Learning – day 44
    • Theory Behind 1Cycle Learning Rate Scheduling and Learning Rate Schedules – Day 43
    • The Power of Learning Rates in Deep Learning and Why Schedules Matter – Day 42
    • Deep Learning Optimizers: NAdam, AdaMax, AdamW, and NAG Comparison – day 41
    • Adam Optimizer deeply explained – day 40
    • Adam vs. SGD: Selecting the Right Optimizer for Your Deep Learning Model
    • AdaGrad, RMSProp, and Adam: A Comparative Guide to Optimization Algorithms – day 38
    • A Comprehensive Guide to AdaGrad: Origins, Mechanism, and Mathematical Proof – day 37
    • Nag as optimiser in deep learning – day 36
    • Momentum – part 3 – day 35
    • Momentum vs Normalisation in Deep learning -part 2 – day 34
    • Momentum Optimization in Machine Learning: A Detailed Mathematical Analysis and Practical Application – day 33
    • Mastering Deep Neural Network Optimization: Techniques and Algorithms for Faster Training – day 32
    • fundamentals of labeled and unlabeled data in machine learning – day 31
    • How do Transfer Learning in Deep Learning Model – with a example – day 30
    • Transfer learning – day 29
    • Understanding Gradient Clipping in Deep Learning – day 28
    • Batch normalisation – trainable and non trainable – day 27
    • Batch normalisation part 2 – day 26
    • Batch Normalization – day 25
    • Activation function progress in deep learning, Relu, Elu, Selu, Geli , mish, etc – include table and graphs – day 24
    • Weight initialazation part 2 – day 23
    • How Create API by Deep Learning to Earn Money and what is the Best Way for Mac Users – Breaking studies on day 22
    • Weight initialisation in Deep Learning well explained _ Day 21
    • Vanishing gradient explained in detail _ Day 20
    • Mastering Hyperparameter Tuning & Neural Network Architectures: Exploring Bayesian Optimization_ Day 19
    • Automatic vs Manual optimisation in Keras_. day 18
    • Hyperparameter Tuning with Keras Tuner _ Day 17
    • Day 16 – TensorFlow: Using TensorBoard, Callbacks, and Model Saving in Keras
    • Sequential vs Functional Keras API Part 2 explanation _ Day 15
    • sequential , functional and model subclassing API in Keras _ day 14
    • What is Keras _ day 13
    • Activation Function, Hidden Layer and non linearity. _ day 12
    • Activation Function _ day 11
    • Regression vs Classification Multi Layer Perceptrons (MLPs) _ day 10
    • Deep Learning _ Perceptrons – day 9
    • 3 Types of Gradient Decent Types : Batch, Stochastic & Mini-Batch _ Day 8
    • What is Gradient Decent in Machine Learning? _ Day 7
    • Can we make prediction without need of going through iteration ? yes with the Normal Equation _ Day 6
    • Day 4 __ Regression & Classification with MNIST
    • Models based, Instance Models, Train-Test Splits: The Building Blocks of Machine Learning Explained – Day 3
    • integrate ML into iOS Apps _ Day 2
    • Machine learning (ML) Overview _ Day 1
  • About us
    • Contact us
+ 4366565788987
Menu

Tag Archives: optimal gradient decent

Home » Posts Tagged "optimal gradient decent"
18 Jul
Machine Learning Overview

What is Gradient Decent in Machine Learning? _ Day 7

  • January 14, 2025
  • By ingoampt
Mastering Gradient Descent in Machine Learning Mastering Gradient Descent: A Comprehensive Guide to Optimizing Machine Learning Model...

Continue reading

© 2025 INGOAMPT. All rights reserved

  • Home
  • Deep Learning 2024 – 2025
    • Announcement : ai chat by api
    • ONNX vs. Core ML: Choosing the Best Approach for Model Conversion in 2024
    • Core ML vs (PyTorch – TensorFlow)for deep learning integration on iOS app on Xcode using SWIFT
    • Announcement : New iOS app coming for Christmas
    • How to Monetize Your Deep Learning Skills and Models as a Solo Developer in 2024 – 2025
    • Understanding Tokens in Deep Learning: Types, Examples, and Use Cases -LLM
    • Understanding Computation Graphs in Pytorch vs Tensorflow
    • Compare iPad M4 chip, Mac M4 Chip , Cloud & GoogleColab for building deep learning model – day 10
    • Solo Developer’s Guide to Building Competitive Language Model Application – day 9
    • Where to Get Data for Machine Learning and Deep Learning Model Creation – day 8
    • GPU and Computing Technology Comparison 2024 – day 7
    • Fine-Tuning vs. Transfer Learning in Voice Synthesis
    • Fine-Tuning in Deep Learning with a practical example – day 6
    • DeepNet – Scaling Transformers to 1,000 Layers new for 2024 – day 5
    • Transformers seems most famous for deep learning on 2024 so lets learn it more – day 4
    • New on 2024 – 2025 are MLX and Transofermers so lets compare Custom Deep Learning Models for iOS with MLX on Apple Silicon vs. PyTorch – day 2
    • Deep Learning in 2024: Continued Insights and Strategies – day 1
  • Application
    • Ai Academy : Deep Learning
    • German Grammar B1
    • ai chat by api Application
    • Flash Card
    • Video Voice Change
    • Todo
    • background img remove INGOAMPT
    • Video Voice Edit INGOAMPT
  • Machine learning _ Deep learning ( day by day )
    • Reinforcement Learning: An Evolution from Games to Real-World Impact – day 77
    • How Dalle Image Generator works ? – day 76
    • Breaking Down Diffusion Models in Deep Learning – Day 75
    • Understanding Unsupervised Pretraining Using Stacked Autoencoders – day 74
    • Unlock the Secrets of Autoencoders, GANs, and Diffusion Models – Why You Must Know Them? -Day 73
    • The Rise of Transformers in Vision and Multimodal Models – Hugging Face – day 72
    • Mastering NLP: Unlocking the Math Behind It for Breakthrough Insights with a scientific paper study – day 71
    • How ChatGPT Work Step by Step – day 70
    • Can ChatGPT Truly Understand What We’re Saying? A Powerful Comparison with BERT” – day 69
    • Leveraging Scientific Research to Uncover How ChatGPT Supports Clinical and Medical Applications – day 68
    • Do you want to read a summery of what is BERT in 2 min read? (Bidirectional Encoder Representations from Transformers) – day 67
    • Transformers Deep Learning – day 66
    • The Revolution of Transformer Models – day 65
    • why transformers are better for NLP ? Let’s see the math behind it – Day 64
    • Natural Language Processing (NLP) and RNN – day 63
    • RNN, Layer Normalization, and LSTMs – Part 8 of RNN Deep Learning- day 62
    • Iterative Forecasting which is Predicting One Step at a Time 2- Direct Multi-Step Forecasting with RNN 3- Seq2Seq Models for Time Series Forecasting – day 61
    • Step-by-Step Explanation of RNN for Time Series Forecasting – part 6 – day 60
    • To learn what is RNN (Recurrent Neural Networks ) why not understand ARIMA, SARIMA first ? – RNN Learning – Part 5 – day 59
    • Understanding RNNs: why compare it with Feedforward Neural Networks with simple Example to show the Math Behind it ? – DAY 58 INGOAMPT
    • Time Series Forecasting with Recurrent Neural Networks (RNNs) – part 3 – day 57
    • Understanding Recurrent Neural Networks (RNNs) – part 2 – day 56
    • RNN Deep Learning – Part 1 – Day 55
    • Mathematics Behind CNN or Convolutional Neural Network in Deep Learning – Day 54
    • CNN- Convolutional Neural Networks – DAY 53
    • Deep Learning Models integration for iOS Apps – briefly explained – Day 52
    • How to Configure and Optimize Deep Neural Networks for Performance and Efficiency – Day 50
    • Learn Max-Norm Regularization to avoid overfitting : Theory and Importance in Deep Learning and proof – day 49
    • DropOut and Monte Carlo Dropout (MC Dropout)- Day 48
    • Understanding Regularization in Deep Learning – day 47
    • Comparing TensorFlow (Keras), PyTorch, & MLX – Day 46
    • Learning rate – part 4- 1-Cycle Scheduling, exponential decay and Cyclic Exponential Decay (CED) – day 45
    • Exploring Gradient Clipping and Weight Initialization in Deep Learning – day 44
    • Theory Behind 1Cycle Learning Rate Scheduling and Learning Rate Schedules – Day 43
    • The Power of Learning Rates in Deep Learning and Why Schedules Matter – Day 42
    • Deep Learning Optimizers: NAdam, AdaMax, AdamW, and NAG Comparison – day 41
    • Adam Optimizer deeply explained – day 40
    • Adam vs. SGD: Selecting the Right Optimizer for Your Deep Learning Model
    • AdaGrad, RMSProp, and Adam: A Comparative Guide to Optimization Algorithms – day 38
    • A Comprehensive Guide to AdaGrad: Origins, Mechanism, and Mathematical Proof – day 37
    • Nag as optimiser in deep learning – day 36
    • Momentum – part 3 – day 35
    • Momentum vs Normalisation in Deep learning -part 2 – day 34
    • Momentum Optimization in Machine Learning: A Detailed Mathematical Analysis and Practical Application – day 33
    • Mastering Deep Neural Network Optimization: Techniques and Algorithms for Faster Training – day 32
    • fundamentals of labeled and unlabeled data in machine learning – day 31
    • How do Transfer Learning in Deep Learning Model – with a example – day 30
    • Transfer learning – day 29
    • Understanding Gradient Clipping in Deep Learning – day 28
    • Batch normalisation – trainable and non trainable – day 27
    • Batch normalisation part 2 – day 26
    • Batch Normalization – day 25
    • Activation function progress in deep learning, Relu, Elu, Selu, Geli , mish, etc – include table and graphs – day 24
    • Weight initialazation part 2 – day 23
    • How Create API by Deep Learning to Earn Money and what is the Best Way for Mac Users – Breaking studies on day 22
    • Weight initialisation in Deep Learning well explained _ Day 21
    • Vanishing gradient explained in detail _ Day 20
    • Mastering Hyperparameter Tuning & Neural Network Architectures: Exploring Bayesian Optimization_ Day 19
    • Automatic vs Manual optimisation in Keras_. day 18
    • Hyperparameter Tuning with Keras Tuner _ Day 17
    • Day 16 – TensorFlow: Using TensorBoard, Callbacks, and Model Saving in Keras
    • Sequential vs Functional Keras API Part 2 explanation _ Day 15
    • sequential , functional and model subclassing API in Keras _ day 14
    • What is Keras _ day 13
    • Activation Function, Hidden Layer and non linearity. _ day 12
    • Activation Function _ day 11
    • Regression vs Classification Multi Layer Perceptrons (MLPs) _ day 10
    • Deep Learning _ Perceptrons – day 9
    • 3 Types of Gradient Decent Types : Batch, Stochastic & Mini-Batch _ Day 8
    • What is Gradient Decent in Machine Learning? _ Day 7
    • Can we make prediction without need of going through iteration ? yes with the Normal Equation _ Day 6
    • Day 4 __ Regression & Classification with MNIST
    • Models based, Instance Models, Train-Test Splits: The Building Blocks of Machine Learning Explained – Day 3
    • integrate ML into iOS Apps _ Day 2
    • Machine learning (ML) Overview _ Day 1
  • About us
    • Contact us
Do Not Forget to Check our IOS Apps in Applications section :)
Privacy Policies
  • INGOAMPT Privacy Policy
  • FlashCardINGOAMPT App Privacy Policy
  • Background Img Remove App Privacy Policy
  • ai chat by api App Privacy Policy
  • Ai Academy : Deep Learning app Privacy Policy
  • Planner ToDo App Privacy Policy
  • German Grammar B1 App Privacy Policy
  • Voice Edit App Privacy Policy
  • Text & Speech App Privacy Policy
Quick Links
  • Home
  • Published Applications
  • Education
LinkedIn Follow us on LinkedIn
© 2025 INGOAMPT. All rights reserved.

Teaching Artificial Intelligence (Deep Learning) + iOS Apps with Deep learning integration --> All by INGOAMPT Dismiss

don't miss our new posts. Subscribe for updates

We don’t spam! Read our privacy policy for more info.

Check your inbox or spam folder to confirm your subscription.