🚀 Just Built GoogLeNet (Inception v1) From Scratch Using TensorFlow! 🧠

1.Inception Module: Naïve vs. Dimension-Reduced Versions
a) Naïve Inception Module
• Applies four parallel operations directly to the input from the previous layer:
• 1x1 convolutions
• 3x3 convolutions
• 5x5 convolutions
• 3x3 max pooling
• Outputs of all four are concatenated along the depth axis for the next layer.
b) Dimension-Reduced Inception Module
• Enhances efficiency by adding 1x1 convolutions (“bottleneck layers”) before the heavier 3x3 and 5x5 convolutions and after the pooling branch.
• These 1x1 convolutions reduce feature dimensionality, decreasing computation and parameter count without losing representational power.
2. Stacked Modules and Network Structure
GoogLeNet stacks multiple Inception modules with dimension reduction, interleaved with standard convolutional and pooling layers. Its architecture can be visualized as a deep stack of these modules, providing both breadth (parallel multi-scale processing) and depth (repetitive stacking).
Key Elements:
• Initial “stem” layers: Traditional convolutions with larger filters (e.g., 7x7, 3x3) and max-pooling for early spatial reduction.
• Series of Inception modules: Each accepts the preceding layer’s output and applies parallel paths with 1x1, 3x3, 5x5 convolutions, and max-pooling, with dimension reduction.
• MaxPooling between certain groups to downsample spatial resolution.
• Two auxiliary classifiers (added during training, removed for inference) are inserted mid-network to encourage better gradient flow, combat vanishing gradients, and provide deep supervision.
• Final layers: Global average pooling, dropout for regularization, and a dense (softmax) classifier for the main output.
3. Auxiliary Classifiers
• Purpose: Deliver additional gradient signal deep into the network, helping train very deep architectures.
• Structure: Each consists of an average pooling, 1x1 convolution, flattening, dense layers, dropout, and a softmax output.
4. Implementation Highlights
• Efficient Multi-Branch Design: By combining filters of different sizes, the model robustly captures both fine and coarse image features.
• Parameter-saving Tricks: 1x1 convolutions before expensive layers drastically cut computational cost.
• Deep Supervision: Auxiliary classifiers support gradient propagation.
GitHub:[https://lnkd.in/gJGsYkFk]

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Lactures notes in ML.pdf

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Microsoft launched the best course on Generative AI!

The Free 21 lesson course is available on #Github and will teach you everything you need to know to start building #GenerativeAI applications.

Enroll: https://github.com/microsoft/generative-ai-for-beginners

https://github.com/microsoft/generative-ai-for-beginners 🩷

Stanford Lecture Notes on RNNs and LSTMs:

https://web.stanford.edu/~jurafsky/slp3/8.pdf

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LLM, SLM, FLM, and MoE: Understanding which architecture fits your specific use case has its advantage.

Modern AI development requires strategic thinking about architecture selection from day one. Each of these four approaches represents a fundamentally different trade-off between computational resources, specialized performance, and deployment flexibility.

The stakes are higher than most people realize, choosing the wrong architecture doesn't just impact performance metrics, it can derail entire projects, waste months of development cycles, and consume budgets that could have delivered significantly better results with the right initial architectural decision.

🔹 1. LLMs are strong at complex reasoning tasks : Their extensive pretraining on various datasets produces flexible models that handle intricate, multi-domain problems. These problems require a broad understanding and deep contextual insight.

🔹 2. SLMs focus on efficiency instead of breadth : They are designed with smaller datasets and optimized tokenization, making them suitable for mobile applications, edge computing, and real-time systems where speed and resource limits matter.

🔹 3. FLMs deliver domain expertise through specialization : By fine-tuning base models with domain-specific data and task-specific prompts, they consistently outperform general models in specialized fields like medical diagnosis, legal analysis, and technical support.

🔹 4. MoE architectures allow for smarter scaling : Their gating logic activates only the relevant expert layers based on the context. This feature makes them a great choice for multi-domain platforms and enterprise applications needing efficient scaling while keeping performance high.

The essential factor is aligning architecture capabilities with your actual needs: performance requirements, latency limits, deployment environment, and cost factors.

Success comes from picking the right tool for the task, not necessarily the most impressive one on paper.

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Best Practice for R :: Cheat Sheet

More: https://github.com/wurli/r-best-practice

#rstats #stats #datascience

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Statistical Signal Processing:

https://ee.stanford.edu/~gray/sp.pdf

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Autoencoder by Hand ✍️

The autoencoder model is the basis for training foundational models from a ton of data. We are talking about tens of billions of training examples, like a good portion of the Internet.

With that much data, it is not economically feasible to hire humans to label all of those data to tell a model what its targets are. Thus, people came up with many clever ideas to derive training targets from the training examples themselves [auto]matically.

The most straightforward idea is to just use the training data itself as the targets. This hands-on exercise demonstrates this idea.

more: https://www.byhand.ai/p/13-can-you-calculate-an-autoencoder

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