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Difference Between LSTM & GRU.
Difference Between LSTM & GRU Table Of Contents: Structure and Complexity. Cell State vs. Hidden State Gating Mechanism Parameter Efficiency Performance Interpretability Use Cases Summary Table Conclusion
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How LSTM Network Solves The Issue Of Vanishing Gradient Problem?
How Vanishing Gradient Problems Solved By The LSTM ? Table Of Contents: What Is Vanishing Gradient Problem? Why Does It Occurs In RNN? (1) What Is Vanishing Gradient Problem? The vanishing gradient problem is a challenge that occurs during the training of deep neural networks, particularly in networks with many layers. It arises when the gradients of the loss function, which are used to update the network’s weights, become extremely small as they are backpropagated through the layers of the network. This is the gradient calculation with only one layer. With only one layer you can see that three terms
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Forward Pass Of LSTM Network.
Forward Pass Of LSTM Network Syllabus: Forward Pass In LSTM Neural Network. Answer: Forward Pass Work: Input Layer: 1st Hidden Layer: Each cell in the LSTM layer will output two values one is Cell state (Ct) and another one is hidden state (ht). This two value will be passed to the next layer as a tuple. Example:
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What Is Backpropagation?
What Is Back Propagation Syllabus: What Is Backpropagation? Steps Of Backpropagation. Math’s In Backpropagation. (1) What Is Backpropagation? Backpropagation, short for backward propagation of errors, is a supervised learning algorithm used to train artificial neural networks. It adjusts the weights of the network to minimize the error between the predicted outputs and the actual target values. Here’s how it works: (2) Steps Of Backpropagation (3) Math’s Behind Backpropagation Forward Propagation: Loss Calculation: Back Propagation:
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RNN – All Doubts Answers!
RNN – All Doubts Answers! Syllabus: Explain Me The Embedding LAYER. RNN One To One Architecture RNN One To Many Architecture RNN Many To One Architecture RNN Many To Many Architecture how in case of One-to-One Architecture input_length can be 5 but not in one to many processes the sequence as a whole. and entire input sequence step by step (or token by token). what is the difference Architecture of Recurrent Neural Network (RNN) Why RNN Uses Same Weights For Every Words? Super Note: The RNN architecture depends on how it takes input and produces output. In One-To-One architecture
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CNN – Full CNN Architecture
CNN – Full CNN Architecture Syllabus: Architecture Diagram Of CNN. Input Layer Of CNN. Convolutional Layers Of CNN. Activation Function Of CNN. Pooling Layers Of CNN. Fully Connected (Dense) Layers. Output Layer Of CNN. Example CNN Architecture. (1) Architecture Diagram Of CNN (2) Input Layer Of CNN (3) Convolutional Layers Of CNN. (4) Activation Function (5) Pooling Layers Of CNN. (6) Fully Connected (Dense) Layers (7) Output Layer
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CNN – If We Are Flattening The Feature Map Aren’t We Loose The Spatial Information?
CNN – If We Are Flattening The Feature Map Aren’t We Loose The Spatial Information? Syllabus: What Happens During Flattening? Loss of Spatial Information Why Flattening is Used Despite This Limitation? Alternatives to Flattening Example Conclusion Answer: Yes, flattening a feature map in a Convolutional Neural Network (CNN) leads to the loss of spatial information because the structured 2D or 3D representation of the features is converted into a 1D vector. While this is acceptable for certain tasks (like classification), it does come with trade-offs. What Happens During Flattering? Loss Of Spatial Information: Why Flattening is Used Despite This Limitation:
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CNN – Why We Add Fully Connected Layer In CNN?
CNN – Why We Add Fully Connected layer In CNN? Syllabus: Why Fully Connected Layer Is Added? Why Fully Connected Layer Is Added To CNN Adding a fully connected (FC) layer in a Convolutional Neural Network (CNN) serves several critical purposes. It allows the network to perform classification or regression by combining the extracted features from previous layers into a final output. Here’s why the fully connected layer is important: Example Of Fully Connected Layer: Advantages Of Fully Connected Layer: Limitation Of Fully Connected Layer: Conclusion:
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CNN – Pooling Layer In CNN
CNN – Pooling Operation Syllabus: What Is Pooling In CNN ? Key Objectives Of Pooling. Types Of Pooling. Parameters Of Pooling. Example Of Pooling. Advantages Of Pooling. Disadvantages Of Pooling. Applications Of Pooling. Pooling Vs Convolution. Alternative To Pooling. Conclusion. (1) What Is Pooling In CNN ? Pooling is a critical operation in Convolutional Neural Networks (CNNs) that reduces the spatial dimensions of feature maps while retaining important information. It enhances computational efficiency and helps in achieving translational invariance. (2) Key Objectives Of Pooling (3) Types Of Pooling (4) Parameters Of Pooling (5) Mathematical Representation (6) Example Of Max Pooling
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CNN – Problem With Convolution Operation.
Problems With Convolution Operation Syllabus: Loss of Spatial Information Translation Variance Over-Sensitivity to Local Features Down sampling Effects (Due to Pooling) Difficulty with Non-Uniform Transformations Overfitting Computational Complexity Gradient Vanishing or Exploding Limited View of the Input (Receptive Field Problem) Class Imbalance Impact Lack of Explainability (1) Loss of Spatial Information (2) Translation Variance (3) Over-Sensitivity to Local Features (4) Down Sampling Effects (Due to Pooling) (5) Difficulty with Non-Uniform Transformations (6) Loss of Spatial Information (7) Loss of Spatial Information (8) Loss of Spatial Information (9) Loss of Spatial Information (10) Loss of Spatial Information (11) Loss of Spatial
