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    • 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
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    • 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
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    • 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
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Tag Archives: batch deep learning

Home » Posts Tagged "batch deep learning"
23 Aug
Machine Learning Overview

Batch normalisation – trainable and non trainable – day 27

  • August 23, 2024
  • By ingoampt
Understanding Trainable and Non-Trainable Parameters in Batch Normalization Demystifying Trainable and Non-Trainable Pa...

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22 Aug
Machine Learning Overview

Batch normalisation part 2 – day 26

  • August 22, 2024
  • By ingoampt
Introduction to Batch Normalization Batch normalization is a wide...

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© 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
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