Now Update the Weights: Here learning rate is the hyper parameter! A low learning rate can cause the model getting caught in local optima, while the high learning rate can cause the model to overshoot the general solution W1 += learning_rate * d_W1
b1 += learning_rate * d_b1
MACHINE LEARNING
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Learning Rate Impact on Weight Updates in Neural Networks
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Calculate Gradients dW1 d_b2 d_b1 for Neural Network Backpropagation
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Similarly calculate d_W1, d_b2 & d_b1 dW1: Gradient of the loss function wrt W1 d_b2: Gradient of the loss function wrt b2(bias of neuron in output layer) d_b1: Gradient of the loss function wrt b1(bias of neuron in hidden layer)
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Building a Simple 2-Layer Neural Network with Sigmoid Activation
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Below is the simple Neural Network consists of 2 layers: – Hidden Layer
– Output Layer First Initialize the size of layers along with the weights & biases. And also define the sigmoid activation function & it's derivative which is really key to introduce non-linearity. -
Neural Network Implementation From Scratch in Python
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Neural Network implemented from scratch in Python (step-by-step explanation with code):
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Jamba-Instruct Performance on Long-Context Question Answering Benchmarks
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(3/4) Jamba-Instruct results on long-context QA benchmarks, conducted using the same method outlined in section 5.2.2 of our Jamba base model whitepaper.
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Mistral Large and Mixtral 8x22B Performance on GSM1K Dataset
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Nice paper. Very good performance by Mistral Large and Mixtral 8x22B on the new GSM1K dataset! Results from Table D (plotted in Figure below) are also a good reminder that models are very sensitive to prompt. In general, it is also good to use sampling & majority voting to
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KAN: Kolmogorov-Arnold Networks – Questioning Fundamental ML Building Blocks
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KAN: Kolmogorov-Arnold Networks https://
arxiv.org/abs/2404.19756 This paper is so fun to read. We need more works like this, questioning our fundamental building blocks. -
Scaling AI Performance Through Increased Training Data
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「投入するデータを増やすことでよりよいAIという約束された未来が訪れます。規模が大きいのでもちろん大変ではありますが、アルゴリズム的には今までやってきたことの延長線上のことです。特別な創意があるわけではなく、みんながほとんど同じことをしています。」 @iwiwi https://
s.u-tokyo.ac.jp/ja/rigakuru/al
umni/gc4Ne8rw/
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Build RAG App with Streamlit Text Input and Answer Display
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8. Ask question about the website and get the answer
• Create a text input for the user to enter their question using 'st.text_input()'
• If a question is asked, get the answer from the RAG app and display it using 'st.write()' -
Loading and Chunking Webpage Data with WebBaseLoader
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4. Load the webpage data using `WebBaseLoader` and split it into chunks.