German Grammar: understanding derselbe – Day 17
As always let’s start by vocabulary of today and then we make the grammar :
13
Aug
- 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
- 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
Follow us on LinkedIn