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 Explained – Day 3 Regression & Classification with MNIST. _ day 4 Mathematical Explanation behind SGD Algorithm in Machine Learning _ day 5 Can we make prediction without need of going through iteration ? yes with the Normal Equation _ Day 6 What is Gradient Decent in Machine Learning? _ Day 7 3 Types of Gradient Decent Types : Batch, Stochastic & Mini-Batch _ Day 8 Deep Learning _ Perceptrons – day 9 Regression vs Classification Multi Layer Perceptrons (MLPs) _ day 10 Activation Function _ day 11 Activation Function, Hidden Layer and non linearity. _ day 12 What is Keras _ day 13 sequential , functional and model subclassing API in Keras _ day 14 Sequential vs Functional Keras API Part 2 explanation _ Day 15 TensorFlow: Using TensorBoard, Callbacks, and Model Saving in Keras _. day 16 Hyperparameter Tuning with Keras Tuner _ Day 17 Automatic vs Manual optimisation in Keras_. day 18 Mastering Hyperparameter Tuning & Neural Network Architectures: Exploring Bayesian Optimization_ Day 19 Vanishing gradient explained in detail _ Day 20 Weight initialisation in Deep Learning well explained _ Day 21 How Create API by Deep Learning to Earn Money and what is the Best Way for Mac Users – Breaking studies on Day 22 Weight initialazation part 2 – day 23 Activation function progress in deep learning, Relu, Elu, Selu, Geli , mish, etc – include table and graphs – day 24 Batch Normalization – day 25 Batch normalisation part 2 – day 26 Batch normalisation – trainable and non trainable – day 27 Understanding Gradient Clipping in Deep Learning – day 28 Transfer learning – day 29 How do Transfer Learning in Deep Learning Model – with an example – Day 30 Fundamentals of labeled vs unlabeled data in Machine Learning – Day 31 Mastering Deep Neural Network Optimization: Techniques and Algorithms for Faster Training – Day 32 Momentum Optimization in Machine Learning: A Detailed Mathematical Analysis and Practical Application – Day 33 Momentum vs Normalization in Deep learning -Part 2 – Day 34 Momentum – part 3 – day 35 Nag as optimiser in deep learning – day 36 A Comprehensive Guide to AdaGrad: Origins, Mechanism, and Mathematical Proof – Day 37 AdaGrad vs RMSProp vs Adam: Why Adam is the Most Popular? – Day 38 Adam vs SGD vs AdaGrad vs RMSprop vs AdamW – Day 39 Adam Optimizer deeply explained by Understanding Local Minimum – Day 40 Deep Learning Optimizers: NAdam, AdaMax, AdamW, and NAG Comparison – Day 41 The Power of Learning Rates in Deep Learning and Why Schedules Matter – Day 42 Theory Behind 1Cycle Learning Rate Scheduling & Learning Rate Schedules – Day 43 Exploring Gradient Clipping & Weight Initialization in Deep Learning – Day 44 Learning Rate – 1-Cycle Scheduling, exponential decay and Cyclic Exponential Decay (CED) – Part 4 – Day 45 Comparing TensorFlow (Keras), PyTorch, & MLX – Day 46 Understanding Regularization in Deep Learning – Day 47 DropOut and Monte Carlo Dropout (MC Dropout)- Day 48 Learn Max-Norm Regularization to avoid overfitting : Theory and Importance in Deep Learning and proof – Day 49 Deep Neural Networks vs Dense Network – Day 50 Deep Learning Examples, Short OverView – Day 51 Deep Learning Models integration for iOS Apps – briefly explained – Day 52 CNN – Convolutional Neural Networks explained by INGOAMPT – DAY 53 Mastering the Mathematics Behind CNN or Convolutional Neural Networks in Deep Learning – Day 54 RNN Deep Learning – Part 1 – Day 55 Understanding Recurrent Neural Networks (RNNs) – part 2 – day 56 Time Series Forecasting with Recurrent Neural Networks (RNNs) – part 3 – day 57 Understanding RNNs: Why Not compare it with Feedforward Neural Networks with simple Example to show the Math Behind it ? – DAY 58 To learn what is RNN (Recurrent Neural Networks ) why not understand ARIMA, SARIMA first ? – RNN Learning – Part 5 – day 59 Step-by-Step Explanation of RNN for Time Series Forecasting – part 6 – day 60 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 RNN, Layer Normalization, and LSTMs – Part 8 of RNN Deep Learning- day 62 Natural Language Processing (NLP) and RNN – day 63 why transformers are better for NLP ? Let’s see the math behind it – Day 64 The Revolution of Transformer Models – day 65 Transformers Deep Learning – day 66 Do you want to read a summery of what is BERT in 2 min read? (Bidirectional Encoder Representations from Transformers) – day 67 Leveraging Scientific Research to Uncover How ChatGPT Supports Clinical and Medical Applications – day 68 Can ChatGPT Truly Understand What We’re Saying? A Powerful Comparison with BERT” – day 69 How ChatGPT Work Step by Step – day 70 Mastering NLP: Unlocking the Math Behind It for Breakthrough Insights with a scientific paper study – day 71 The Rise of Transformers in Vision and Multimodal Models – Hugging Face – day 72 Unlock the Secrets of Autoencoders, GANs, and Diffusion Models – Why You Must Know Them? -Day 73 Understanding Unsupervised Pretraining Using Stacked Autoencoders – day 74 Breaking Down Diffusion Models in Deep Learning – Day 75 Generative Adversarial Network (GANs) Deep Learning – day 75 How Dalle Image Generator works ? – day 76 Reinforcement Learning: An Evolution from Games to Real-World Impact – day 77

don't miss our new posts. Subscribe for updates

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