├── 1.The Machine Learning Landscape ├── 1.Machine Learning what & why & types.png ├── 2.Main Challenges of ML.png ├── 3.Batch vs Online Learning(types of ML).png ├── 4.Instance-Based vs Model-Based Learning(types of ML).png └── 5.validation set for hyperparameter tuning and selecting a ML model.png ├── 10.Perceptron & MLP ├── 1.TLU.png ├── 2.Perceptron.png └── 3.training Perceptron & its limitations & MLP.png ├── 11.DNN ├── Design ANN model │ ├── (1) Train │ │ ├── (1) initializing and changing Weights │ │ │ ├── Random Thetas or Weights.png │ │ │ └── better Weight Initialization.png │ │ ├── (2) Forward Propagation │ │ │ ├── 1.Neural Networks Forward Equation .png │ │ │ ├── 2.Forward Equation for each layer of neurons.png │ │ │ ├── 3.Neural Network forward process.png │ │ │ ├── 4.iteration & batch size & epoch.png │ │ │ └── activstion function │ │ │ │ ├── 1.why activation functions.png │ │ │ │ ├── 2.why not sigmoid or tanh in hidden layers.png │ │ │ │ ├── 3.better activation functions part1.png │ │ │ │ ├── 3.better activation functions part2.png │ │ │ │ ├── 3.better activation functions part3.png │ │ │ │ ├── 4.how Activation function works2.png │ │ │ │ ├── 4.how activation function works1.png │ │ │ │ ├── 4.how the activation function works3.png │ │ │ │ └── all Activation Functoins types.png │ │ ├── (3) Cost Error Of NN │ │ │ ├── Error Of Neural Networks.png │ │ │ └── Loss functions why & what & how.png │ │ ├── (4) Backward Propagation │ │ │ ├── 1- how to minimize Loss function by all weights if there is a relation between them.png │ │ │ ├── 2- prove the relation between the weights and Loss function.png │ │ │ ├── 3- how to differentiate loss function with respect to each weight in details.png │ │ │ ├── 4- how to differentiate loss function with respect to each weight in details with Z.png │ │ │ ├── Faster Optimizers than GD │ │ │ │ ├── 1.Vanilla GD and its problems.png │ │ │ │ ├── 2.SGD optimizer & Momentum Optimizer.png │ │ │ │ ├── 3.Nesterov Accelerated Gradient.png │ │ │ │ ├── 4.AdaGrad & RMSProp optimizers.png │ │ │ │ ├── 5.Adam optimizer and its variants.png │ │ │ │ ├── Difference Between Optimizers.png │ │ │ │ └── comparison between optimizers.png │ │ │ └── Learning Rate │ │ │ │ ├── Learning Rate Scheduling part1.png │ │ │ │ ├── Learning Rate Scheduling part2.png │ │ │ │ └── Learning rate effect on model performance.png │ │ └── Steps of Training NN.png │ └── (2) Test │ │ ├── Bias and Variance.png │ │ └── knowing Bias and Variance by Cost function.png ├── Design Artificial Neural Network model.py ├── Problems & Solutions │ ├── 2.Vanishing& Exploding GD (Unstable GD) │ │ ├── 1.The Vanishing Exploding Gradients problems.png │ │ ├── 2.Batch Normalization │ │ │ ├── 1.Normalization& standardization difference.png │ │ │ ├── 2.Batch Normalization.png │ │ │ ├── 3.Batch Normalization.png │ │ │ ├── 4.Batch Normalization.png │ │ │ ├── 5.Batch Normalization Speeds up Training.png │ │ │ └── 6.Batch Normalization works as regularizer.png │ │ └── 3.Gradient Clipping.png │ └── Avoiding Overfitting Through Regularization │ │ ├── Dropout.png │ │ └── Regularization techniques.png └── types of neural networks.png ├── 12.CNN ├── (1) Train │ ├── (1) initialization of kernal weights && kernal Biases │ │ ├── Random Thetas or Weights.png │ │ └── better kernal Weights Initialization.png │ ├── (2) Forward Propagation │ │ ├── 1- convolutional layer │ │ │ ├── 1.png │ │ │ ├── 2.png │ │ │ ├── 3.png │ │ │ └── 4.png │ │ ├── 2-Pooling Layer │ │ │ ├── pooling layer.png │ │ │ └── types of pooling layers.png │ │ ├── 3-Fully Connected Layer │ │ │ └── Fully Connected Layer.png │ │ ├── CNN process.png │ │ └── forward propagation process.png │ ├── (3) Cost Function │ │ ├── Error Of Neural Networks.png │ │ └── Loss functions why & what & how for fully connected layer.png │ ├── (4) Backward Propagation │ │ ├── (1) Backward propagation.png │ │ ├── (2) Loss function && Kernels.png │ │ ├── (3) Loss function && Biases.png │ │ ├── (4) Loss function && input (1).png │ │ ├── (5) Loss function && input (2).png │ │ └── Loss Function && input && Kernels && Baises results.png │ ├── definitions.png │ └── steps of Training CNN.png ├── CNN architectures & transfer learning │ ├── CNN architectures │ │ ├── AlexNet │ │ │ └── AlexNet architecture.png │ │ ├── GoogLeNet │ │ │ ├── GoogLeNet architecture.png │ │ │ └── the architecture of an inception module.png │ │ ├── LeNet-5 │ │ │ └── LeNet-5 architecture.png │ │ ├── Other Noteworthy Architectures │ │ │ ├── CSPNet architecture.png │ │ │ ├── DenseNet architecture.png │ │ │ ├── MobileNet architecture.png │ │ │ ├── ResNeXt architecture.pnd.jfif │ │ │ └── efficientnet architecture.png │ │ ├── ResNet │ │ │ └── ResNet architecture.png │ │ ├── SENet │ │ │ ├── SE block architecture.png │ │ │ └── SE-Inception module (left) and SE-ResNet unit (right).png │ │ ├── VGGNet │ │ │ └── VGGNet architecture.jpg │ │ └── Xception │ │ │ └── Xception architecture.png │ ├── Pretrained models available in Keras.png │ └── Transfer Learning Technique using CNN architectures │ │ ├── 1.Transfer Learning part1.png │ │ ├── 1.Transfer Learning part2.png │ │ └── 2.General Steps for Transfer Learning with Pre-trained Models.png └── Needing for CNN instead of DNN.png ├── 13.RNN ├── 1.Recurrent Cells (one hidden layer) │ ├── 1.Vanilla-Basic- Simple RNN cell │ │ ├── 1-Train │ │ │ ├── (1) initialization weights │ │ │ │ └── better Weight Initialization.png │ │ │ ├── (2) feedforward │ │ │ │ ├── (1) Forward Propagation in RNN.png │ │ │ │ └── (2) Forward Propagation in RNN.png │ │ │ ├── (3) Cost Function │ │ │ │ └── Cost function.png │ │ │ └── (4) Backward Propagation through time (BPTT) │ │ │ │ └── Backword Propagation in RNN.png │ │ └── Exploding && Vanishing problems │ │ │ ├── 1.Exploding & Vanishing problem with the solutions.png │ │ │ ├── 2.Why Vanilla RNN effect on Gradient Descent by math.png │ │ │ ├── 3.Layer Normalization Solution part1.png │ │ │ └── 3.Layer Normalization Solution part2.png │ ├── 1.different RNN Cells.png │ ├── 2.Long Short Term Memory(LSTM) Cell │ │ ├── 1.LSTM Cell.png │ │ ├── 2.LSTM Recurrent Cell.png │ │ └── 3.How LSTM RNN solves the Unstable Gradient Descent.png │ ├── 2.Types of RNNs Recurrent Cells .png │ ├── 3.Gated Recurrent Unit(GRU) Cell │ │ ├── 1.GRU Cell.png │ │ ├── 2.GRU Cell.png │ │ └── 3.GRU Recurrent Cell.png │ ├── 3.the problems with Simple RNNs, LSTMs, GRUs Cells.png │ └── Bidirectional Recurrent Cell │ │ ├── 1.Bidirectional RNN.png │ │ ├── 2.Bidirectional RNN.png │ │ └── 3.Bidirectional RNN.png ├── 2.Deep RNN │ ├── 1.Deep RNN.png │ └── 2.Deep RNN.png ├── 3.Word Embedding │ ├── 1.Embedding Space.png │ ├── 2.Pre-WordEmbbeding and WordEmbbeding Techniques.png │ └── 3.Word2Vec.png ├── 4.Seq2Seq by basic Encoder Decoder model │ ├── 1.seq2seq by Encoder Decoder Without Attention.png │ ├── 2.Attention Technique.png │ ├── 3.LSTM and Attention.png │ └── 4.seq2seq by Encoder Decoder With Attention.png ├── 5.Transformers │ ├── (1) NLP Transformers │ │ ├── Decoder-Only Transformer │ │ │ ├── 1.The Structure │ │ │ │ ├── (1) position Encoded values for io.png │ │ │ │ ├── (2) masked self attention for input and output words.png │ │ │ │ ├── (3) masked self attention for input and output words.png │ │ │ │ ├── (4) Residual connections for io.png │ │ │ │ ├── (5) parallel computations for io.png │ │ │ │ ├── (6) Encoding prompt or input (predict next word).png │ │ │ │ └── (7) generate outputs.png │ │ │ └── the Dencoder's Main Role.png │ │ ├── Encoder Decoder Transformers │ │ │ ├── (1) position Encoded values for io.png │ │ │ ├── (2) self attention for input and output words.png │ │ │ ├── (3) self attention for the input and output words.png │ │ │ ├── (4) Residual connections for io.png │ │ │ ├── (5) parallel computations for io.png │ │ │ ├── (6) Encoder Decoder Attention.png │ │ │ └── (7) seq2seq by Encoder Decoder with Transformer.png │ │ ├── Encoder-Only Transformer │ │ │ └── the Encoder's Main Role.png │ │ ├── Transformers1.png │ │ └── pre-trained language models │ │ │ ├── BERT and GPT.png │ │ │ └── pre-trained language models.png │ ├── (2) Vision Transformers │ │ ├── (1) Vision Transformer │ │ │ ├── 1.vision transformer (ViT).png │ │ │ └── 1.vision transformer (ViT)2.png │ │ ├── DALL·E │ │ │ ├── CLIP.png │ │ │ └── DALL2.png │ │ ├── DINO Vision Transformer │ │ │ └── ...png │ │ ├── Flaminco (visual language model) │ │ │ └── image-20220511204113119.png │ │ ├── GATO (MultiModel model) │ │ │ ├── Screenshot 2024-03-04 201244.png │ │ │ ├── Screenshot 2024-03-04 201324.png │ │ │ └── Screenshot 2024-03-04 201351.png │ │ └── data-efficient image transformers (DeiTs) │ │ │ ├── Distillation.png │ │ │ └── Hard Distillation.png │ ├── (3) Multimodal Transformer │ │ └── multimodel.png │ └── pretrain and find tune models.png ├── 6.Time series │ ├── 1.time series.png │ ├── 2.ACF and PACF.png │ ├── 3.AutoRegression (AR).png │ ├── 4.ARMA & ARIMA models.png │ └── 5.SARIMA model.png └── The need for RNN instead of DNN.png ├── 14.UnSupervised DL ├── Autoencoders │ ├── (1) basic or vanilla Autoencoder.png │ ├── (2) Deep autoencoders.png │ ├── (3) Reconstruction Loss.png │ ├── (4) Autoencoders importance.png │ ├── (5) the difference between Encoders and PCAs.png │ ├── (6) convolutional AutoEncoder.png │ ├── (7) Denoising Autoencoders.png │ ├── (8) Sparse or regularized Autoencoders.png │ ├── (9) Variational Autoencoders.png │ └── important links.txt ├── Diffusion Models │ ├── Diffusion Models.png │ └── U-Nets.png └── Generative Adversarial Networks (GANs) │ ├── (1) Vanilla or basic GANs.png │ ├── (2) GANs challeges.png │ ├── (3) conditional GANs.png │ ├── (4) Deep Convolutional GANs (DCGANs).png │ ├── (5) Progressive Growing of GANs (ProGANs).png │ └── (6) StyleGANs.png ├── 15.Reinforcement learning (RL) ├── (1) RL │ ├── (1) RL.png │ ├── (2) the difference between RL and rest of ML.png │ ├── (3) Markov Decision Processes (MDPs).png │ ├── (4) Deterministic vs Stochastic Policies.png │ ├── (5) RL Algorithms.png │ ├── (6) Policy Gradients.png │ └── (7) Q-learning (Tabular Q-learning).png ├── (2) Deep RL │ ├── (1) Deep RL.png │ ├── (2) Neural Network Policy.png │ ├── (3) Two Neural networks.png │ ├── (4) Value Neural Network.png │ ├── (5) Policy Neural Network.png │ ├── (6) Deep Q Learning.png │ ├── (7) Deep Q-Learning (DQN).png │ └── (8) Deep Q-Learning Variants.png └── Some Popular RL Algorithms.png ├── 2.End-to-End Machine Learning Project ├── 1 Frame problem & get data & explore, visualize .png ├── 2.Data preparation.png ├── 3.select the model & find tune it.png ├── 4.cross validation.png ├── 5 present your solution & launch, maintain, monitor system.png ├── 6.Main steps of ML projects.png └── Exploring the AI Cycle in Production.png ├── 3.Classification ├── 1.Get & Split& Train on the data .png ├── 2.Performance Measures.png ├── 3.ROC and AUC in details.png ├── 4.confusion matrix.png ├── 5.confusion matrix.png └── 6.Mulitclass & Multilabel & Multioutput.png ├── 4.Training Models ├── 1.Why training & what Linear Regerssion and cost function.png ├── 1.types of Gradient Descent │ ├── 1.Gradient Descent.png │ ├── 2.Batch Gradient Descent.png │ ├── 3.Stochastic Gradient Descent.png │ └── 4.Mini Batch Gradient Descent.png ├── 10.Logistic Regression train and cost function.png ├── 11.Multinomial Logistic Regression.png ├── 2.Linear Regression.png ├── 3.Linear Regression Train.png ├── 4.Gradient Descent.png ├── 5.Dradient Descent Types.png ├── 6.Bias and Variance by Cost function.png ├── 7.Variance and Bias.png ├── 8.Polynomial& LearningCurves& Regularization.png ├── 9.Regularized linear models.png ├── LinearRegression.py ├── LogisticRegression.py └── Loss functions and matrics │ ├── Evaluation Metrics │ ├── Classification Evaluation Metrics │ │ ├── (1) Accuracy.png │ │ ├── (2) Precision & Recall for binary Classification.png │ │ ├── (3) F1 Score.png │ │ ├── (4) AUC.png │ │ └── (5) Cross Entropy.png │ └── Regression Evaluation Metrics │ │ ├── (1) MAE.png │ │ ├── (2) MSE.png │ │ ├── (3) RMSE.png │ │ ├── (4) R Squared.png │ │ └── (5) Cosine Similarity.png │ └── Loss functions and metrics.png ├── 5.Support Vector Machines ├── 1.Linear & NonLinear SVM classifier & time Complexity.png └── 2.SVM Regressor.png ├── 6.Decision Trees ├── 1.Visualizing & prediction & Training and cost function.png ├── 2.Time complexity & Regularization& DS Regressor.png ├── 3.Disadvantage and solutions.png ├── 4.Visualizing Decision Tree with the data.png ├── 5.Decision Tree with Entropy and information gain.png ├── 6.Decision Tree with Gini Impurity.png └── DecisionTree Classifier with entopy and information gain.py ├── 7.Ensemble Learning and Random Forests ├── 1.Voting & Bagging & Pasting Classifiers.png ├── 2.hard Voting classifier.png ├── 2.soft Voting classifier.png ├── 3.Bagging Classifier.png ├── 4.Random Forest.png ├── 5.Boosting & AdaBoost.png ├── 6.Boosting.png ├── 7.Gradient Boosting & Stacking.png ├── 8.the difference between Bagging and Boosting methods.png └── 9.Stacking method.png ├── 8.Dimensionality Reduction ├── 1.Curse Dimensionality & main approaches of DR.png ├── 2.PCA part1.png ├── 3.PCA using singular value decomposition (SVD).png ├── 4.PCA part2.png ├── 5.Random Projection & LLE and LDA.png └── 6.LDA in details.png ├── 9.Unsupervised Learning Techniques ├── 1.K-means.png ├── 2.K-means.png ├── 3.DBSCAN & other Clustering Algorithms.png ├── 4.Gaussian Mixtures.png └── 5.Gaussian Mixtures.png ├── Hands on Machine Learning with Scikit Learn and TensorFlow 3rd edition.png └── README.md /1.The Machine Learning Landscape/1.Machine Learning what & why & types.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/eslammohamedtolba/My-Hands-on-ML-Book-Summarization/HEAD/1.The Machine Learning Landscape/1.Machine Learning what & why & types.png -------------------------------------------------------------------------------- /1.The Machine Learning Landscape/2.Main Challenges of ML.png: 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