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Images generated with Midjourney", "downloaded": true, "discovery": "saved_markdown" }, { "status": "ok", "url": "https://assets.insightmediagroup.io/media/wp-content/uploads/2025/02/13opdx0rZVIavS3hd-FtJbw.png", "bytes": 201934, "reused": true, "local_path": "attachments/5361b27a4c83/images/006.png", "src": "https://assets.insightmediagroup.io/media/wp-content/uploads/2025/02/13opdx0rZVIavS3hd-FtJbw.png", "alt": "The process of using certain neurons strengthens their connections.", "downloaded": true, "discovery": "saved_markdown" }, { "status": "ok", "url": "https://assets.insightmediagroup.io/media/wp-content/uploads/2025/02/1Ixk7vi9afHRoRx5dG_E37A.png", "bytes": 23465, "reused": true, "local_path": "attachments/5361b27a4c83/images/008.png", "src": "https://assets.insightmediagroup.io/media/wp-content/uploads/2025/02/1Ixk7vi9afHRoRx5dG_E37A.png", "alt": "Perceptrons in AI work with numbers, while Neurons within the brain work with electrochemical signals.", "downloaded": true, "discovery": "saved_markdown" }, { "status": "ok", "url": "https://assets.insightmediagroup.io/media/wp-content/uploads/2025/02/1M4Hg_rE7WmLFqpMjfg5P3Q.png", "bytes": 31065, "reused": true, "local_path": "attachments/5361b27a4c83/images/009.png", "src": "https://assets.insightmediagroup.io/media/wp-content/uploads/2025/02/1M4Hg_rE7WmLFqpMjfg5P3Q.png", "alt": "Perceptrons aggregate numbers to come up with an output, while neurons aggregate electrochemical signals to come up with an output.", "downloaded": true, "discovery": "saved_markdown" }, { "status": "ok", "url": "https://assets.insightmediagroup.io/media/wp-content/uploads/2025/02/1DHnCzMRpcBASTtxFMeAuFg.png", "bytes": 33362, "reused": true, "local_path": "attachments/5361b27a4c83/images/010.png", "src": "https://assets.insightmediagroup.io/media/wp-content/uploads/2025/02/1DHnCzMRpcBASTtxFMeAuFg.png", "alt": "Perceptrons output numbers, while neurons output electrochemical signals.", "downloaded": true, "discovery": "saved_markdown" }, { "status": "ok", "url": "https://assets.insightmediagroup.io/media/wp-content/uploads/2025/02/1-7K5ccbzd9_UO6UIRzxbOA.png", "bytes": 665357, "reused": true, "local_path": "attachments/5361b27a4c83/images/011.png", "src": "https://assets.insightmediagroup.io/media/wp-content/uploads/2025/02/1-7K5ccbzd9_UO6UIRzxbOA.png", "alt": "A neural network \\(left\\) vs the brain \\(right\\). src1 src2", "downloaded": true, "discovery": "saved_markdown" }, { "status": "ok", "url": "https://assets.insightmediagroup.io/media/wp-content/uploads/2025/02/11snQ4GLqxc-6tHjXaF6TtA.png", "bytes": 28408, "reused": true, "local_path": "attachments/5361b27a4c83/images/012.png", "src": "https://assets.insightmediagroup.io/media/wp-content/uploads/2025/02/11snQ4GLqxc-6tHjXaF6TtA.png", "alt": "Perceptrons output a continuous range of numbers, while Neurons either fire or they don't.", "downloaded": true, "discovery": "saved_markdown" }, { "status": "ok", "url": "https://assets.insightmediagroup.io/media/wp-content/uploads/2025/02/1cLcbZkQ4KCmyqsoIsjEQCg.png", "bytes": 61248, "reused": true, "local_path": "attachments/5361b27a4c83/images/013.png", "src": "https://assets.insightmediagroup.io/media/wp-content/uploads/2025/02/1cLcbZkQ4KCmyqsoIsjEQCg.png", "alt": "A neural network, with the weights leading into and out of a particular perceptron highlighted.", "downloaded": true, "discovery": "saved_markdown" }, { "status": "ok", "url": "https://assets.insightmediagroup.io/media/wp-content/uploads/2025/02/1Sd0t4WYtLZmhvF6w6VcQ6A.png", "bytes": 68607, "reused": true, "local_path": "attachments/5361b27a4c83/images/014.png", "src": "https://assets.insightmediagroup.io/media/wp-content/uploads/2025/02/1Sd0t4WYtLZmhvF6w6VcQ6A.png", "alt": "An example of how the value of a perceptron might be calculated. \\(0.3×0.3\\) + \\(0.7×0.1\\) +\\(-0.5×0.5\\)=-0.09", "downloaded": true, "discovery": "saved_markdown" }, { "status": "ok", "url": "https://assets.insightmediagroup.io/media/wp-content/uploads/2025/02/1gTOvMB96VO5qDn-VI7Me_w.png", "bytes": 142932, "reused": true, "local_path": "attachments/5361b27a4c83/images/015.png", "src": "https://assets.insightmediagroup.io/media/wp-content/uploads/2025/02/1gTOvMB96VO5qDn-VI7Me_w.png", "alt": "An example of how the value of a perceptron might be calculated when a bias term is included in the model. \\(0.3×0.3\\) + \\(0.7×0.1\\) +\\(-0.5×0.5\\) + 0.01 =-0.08", "downloaded": true, "discovery": "saved_markdown" }, { "status": "ok", "url": "https://assets.insightmediagroup.io/media/wp-content/uploads/2025/02/1q-oox60ZsuYVYbZkp2SpEg.png", "bytes": 43125, "reused": true, "local_path": "attachments/5361b27a4c83/images/016.png", "src": "https://assets.insightmediagroup.io/media/wp-content/uploads/2025/02/1q-oox60ZsuYVYbZkp2SpEg.png", "alt": "Examples of a variety of functions which, given some input, produce some output. The top three are linear, while the bottom three are non-linear.", "downloaded": true, "discovery": "saved_markdown" }, { "status": "ok", "url": "https://assets.insightmediagroup.io/media/wp-content/uploads/2025/02/1m_PdmmIOUeKQZDM59eehNw.png", "bytes": 50307, "reused": true, "local_path": "attachments/5361b27a4c83/images/017.png", "src": "https://assets.insightmediagroup.io/media/wp-content/uploads/2025/02/1m_PdmmIOUeKQZDM59eehNw.png", "alt": "By placing non-linear activation functions within a neural network, neural networks are capable of modeling complex relationships.", "downloaded": true, "discovery": "saved_markdown" }, { "status": "ok", "url": "https://assets.insightmediagroup.io/media/wp-content/uploads/2025/02/1U2_yx_M8XCD93RPOxvg9hQ.png", "bytes": 7126, "reused": true, "local_path": "attachments/5361b27a4c83/images/018.png", "src": "https://assets.insightmediagroup.io/media/wp-content/uploads/2025/02/1U2_yx_M8XCD93RPOxvg9hQ.png", "alt": "The ReLU activation function, where the output is equal to zero if the input is less than zero, and the output is equal to the input if the input is greater than zero.", "downloaded": true, "discovery": "saved_markdown" }, { "status": "ok", "url": "https://assets.insightmediagroup.io/media/wp-content/uploads/2025/02/1JYyCqZhkWAvTaZ7fPhnk9A.png", "bytes": 20144, "reused": true, "local_path": "attachments/5361b27a4c83/images/019.png", "src": "https://assets.insightmediagroup.io/media/wp-content/uploads/2025/02/1JYyCqZhkWAvTaZ7fPhnk9A.png", "alt": "The fundamental idea of training a neural network. You give it some data where you know what you want the output to be, compare the neural networks output with your desired result, then use how wrong the neural network was to update the parameters so it's less wrong.", "downloaded": true, "discovery": "saved_markdown" }, { "status": "ok", "url": "https://assets.insightmediagroup.io/media/wp-content/uploads/2025/02/1v2Fwu8wCaSoKiTBBpZknuQ.png", "bytes": 16704, "reused": true, "local_path": "attachments/5361b27a4c83/images/020.png", "src": "https://assets.insightmediagroup.io/media/wp-content/uploads/2025/02/1v2Fwu8wCaSoKiTBBpZknuQ.png", "alt": "A neural network with two inputs and a single output, with a hidden layer in-between allowing the model to make more complex predictions.", "downloaded": true, "discovery": "saved_markdown" }, { "status": "ok", "url": "https://assets.insightmediagroup.io/media/wp-content/uploads/2025/02/1UD0Z1G4QBuVvud3Sf8gOWg.png", "bytes": 81431, "reused": true, "local_path": "attachments/5361b27a4c83/images/021.png", "src": "https://assets.insightmediagroup.io/media/wp-content/uploads/2025/02/1UD0Z1G4QBuVvud3Sf8gOWg.png", "alt": "The neural network, with random weights and biases defined.", "downloaded": true, "discovery": "saved_markdown" }, { "status": "ok", "url": "https://assets.insightmediagroup.io/media/wp-content/uploads/2025/02/1pLo_h8SZrJbAjd42Qeyv_A.png", "bytes": 95972, "reused": true, "local_path": "attachments/5361b27a4c83/images/022.png", "src": "https://assets.insightmediagroup.io/media/wp-content/uploads/2025/02/1pLo_h8SZrJbAjd42Qeyv_A.png", "alt": "We'll apply the ReLU activation function to the value of our hidden perceptrons.", "downloaded": true, "discovery": "saved_markdown" }, { "status": "ok", "url": "https://assets.insightmediagroup.io/media/wp-content/uploads/2025/02/1NI1jil4RRhvW8m9yPrWTnQ.png", "bytes": 9239, "reused": true, "local_path": "attachments/5361b27a4c83/images/023.png", "src": "https://assets.insightmediagroup.io/media/wp-content/uploads/2025/02/1NI1jil4RRhvW8m9yPrWTnQ.png", "alt": "An example of the data that we'll be training off of.", "downloaded": true, "discovery": "saved_markdown" }, { "status": "ok", "url": "https://assets.insightmediagroup.io/media/wp-content/uploads/2025/02/15fKc61r8UC33OQqn5v0Gnw.png", "bytes": 86233, "reused": true, "local_path": "attachments/5361b27a4c83/images/024.png", "src": "https://assets.insightmediagroup.io/media/wp-content/uploads/2025/02/15fKc61r8UC33OQqn5v0Gnw.png", "alt": "Calculating the value of the hidden layer and output based on the input, including all major intermediary steps.", "downloaded": true, "discovery": "saved_markdown" }, { "status": "ok", "url": "https://assets.insightmediagroup.io/media/wp-content/uploads/2025/02/1NNXP11WMJkbRzxtcEI7eZQ.png", "bytes": 103429, "reused": true, "local_path": "attachments/5361b27a4c83/images/025.png", "src": "https://assets.insightmediagroup.io/media/wp-content/uploads/2025/02/1NNXP11WMJkbRzxtcEI7eZQ.png", "alt": "The training data has an input of 0.1 and 0.3, and the desired output \\(the average of the input\\) is 0.2. The prediction from the model was -0.1. Thus, the difference between the output and the desired output is 0.3.", "downloaded": true, "discovery": "saved_markdown" }, { "status": "ok", "url": "https://assets.insightmediagroup.io/media/wp-content/uploads/2025/02/1dIbrD12PNayTGNhOD75nmg.png", "bytes": 70585, "reused": true, "local_path": "attachments/5361b27a4c83/images/026.png", "src": "https://assets.insightmediagroup.io/media/wp-content/uploads/2025/02/1dIbrD12PNayTGNhOD75nmg.png", "alt": "The ultimate, activated output of the third perceptron, 0.00", "downloaded": true, "discovery": "saved_markdown" }, { "status": "ok", "url": "https://assets.insightmediagroup.io/media/wp-content/uploads/2025/02/13Whf1AhkWz6clG06cztpCw.png", "bytes": 71248, "reused": false, "content_type": "image/png", "local_path": "attachments/5361b27a4c83/images/body-433e00852086943c57da.png", "src": "https://assets.insightmediagroup.io/media/wp-content/uploads/2025/02/13Whf1AhkWz6clG06cztpCw.png", "alt": "", "downloaded": true, "discovery": "saved_markdown" }, { "status": "ok", "url": "https://assets.insightmediagroup.io/media/wp-content/uploads/2025/02/1aT1An9_PHK2UdYPg4P396g.png", "bytes": 93629, "reused": true, "local_path": "attachments/5361b27a4c83/images/027.png", "src": "https://assets.insightmediagroup.io/media/wp-content/uploads/2025/02/1aT1An9_PHK2UdYPg4P396g.png", "alt": "How we calculate how the weight should change. Here the symbol Δ\\(delta\\) means "change in", so Δw₈ means the "change in w₈"", "downloaded": true, "discovery": "saved_markdown" }, { "status": "ok", "url": "https://assets.insightmediagroup.io/media/wp-content/uploads/2025/02/1kmHCi-J7Fg3IV6aEynIagQ.png", "bytes": 97506, "reused": true, "local_path": "attachments/5361b27a4c83/images/028.png", "src": "https://assets.insightmediagroup.io/media/wp-content/uploads/2025/02/1kmHCi-J7Fg3IV6aEynIagQ.png", "alt": "Calculating how the weights leading into the output should change.", "downloaded": true, "discovery": "saved_markdown" }, { "status": "ok", "url": "https://assets.insightmediagroup.io/media/wp-content/uploads/2025/02/1yU1vGFGDcYF_bsYpi1EViQ.png", "bytes": 63695, "reused": true, "local_path": "attachments/5361b27a4c83/images/029.png", "src": "https://assets.insightmediagroup.io/media/wp-content/uploads/2025/02/1yU1vGFGDcYF_bsYpi1EViQ.png", "alt": "how the bias of the output should be updated.", "downloaded": true, "discovery": "saved_markdown" }, { "status": "ok", "url": "https://assets.insightmediagroup.io/media/wp-content/uploads/2025/02/15bZbQeeglMZ8eNSAMBADJg.png", "bytes": 74473, "reused": false, "content_type": "image/png", "local_path": "attachments/5361b27a4c83/images/body-5f32b00a815b46d229cf.png", "src": "https://assets.insightmediagroup.io/media/wp-content/uploads/2025/02/15bZbQeeglMZ8eNSAMBADJg.png", "alt": "Calculating how the activated output of the first hidden neuron should have changed by multiplying the desired change in the output by w₇.", "downloaded": true, "discovery": "saved_markdown" }, { "status": "ok", "url": "https://assets.insightmediagroup.io/media/wp-content/uploads/2025/02/1MG5zTdstAdlxDYriLA1dMw.png", "bytes": 76514, "reused": false, "content_type": "image/png", "local_path": "attachments/5361b27a4c83/images/body-5f5945e256e036f7cc4d.png", "src": "https://assets.insightmediagroup.io/media/wp-content/uploads/2025/02/1MG5zTdstAdlxDYriLA1dMw.png", "alt": "How much we want to change the un-activated value of the first hidden perceptron, based on back-propagating from the output.", "downloaded": true, "discovery": "saved_markdown" }, { "status": "ok", "url": "https://assets.insightmediagroup.io/media/wp-content/uploads/2025/02/1rb2kMoJK5HkgeBIvWeXYZA.png", "bytes": 108773, "reused": false, "content_type": "image/png", "local_path": "attachments/5361b27a4c83/images/body-a5aac8c29848f917bab6.png", "src": "https://assets.insightmediagroup.io/media/wp-content/uploads/2025/02/1rb2kMoJK5HkgeBIvWeXYZA.png", "alt": "Now that we've calculated how the first hidden neuron should change, we can calculate how we should update w₁ the same way we calculated how w₇ should be updated previously.", "downloaded": true, "discovery": "saved_markdown" }, { "status": "ok", "url": "https://assets.insightmediagroup.io/media/wp-content/uploads/2025/02/15zQm78JT5IjAvQSI0YdEdw.png", "bytes": 98243, "reused": false, "content_type": "image/png", "local_path": "attachments/5361b27a4c83/images/body-5db53156dd46c9c1dab1.png", "src": "https://assets.insightmediagroup.io/media/wp-content/uploads/2025/02/15zQm78JT5IjAvQSI0YdEdw.png", "alt": "By back propagating through the model, using a combination of values from the forward passes and desired changes from the backward pass at various points of the model, we can calculate how all parameters should change", "downloaded": true, "discovery": "saved_markdown" }, { "status": "ok", "url": "https://assets.insightmediagroup.io/media/wp-content/uploads/2025/02/0SOOewKyrD86bJlSQ.jpeg", "bytes": 180281, "reused": false, "content_type": "image/jpeg", "local_path": "attachments/5361b27a4c83/images/body-45f96194eb609b01245e.jpeg", "src": "https://assets.insightmediagroup.io/media/wp-content/uploads/2025/02/0SOOewKyrD86bJlSQ.jpeg", "alt": "Join IAEE", "downloaded": true, "discovery": "saved_markdown" }, { "status": "ok", "url": "https://assets.insightmediagroup.io/media/wp-content/uploads/2025/02/1Bn8-3lou0sBs8y2cf1sY8g.png", "bytes": 10709, "reused": false, "content_type": "image/png", "local_path": "attachments/5361b27a4c83/images/body-fe7b89890669846c57ea.png", "src": "https://assets.insightmediagroup.io/media/wp-content/uploads/2025/02/1Bn8-3lou0sBs8y2cf1sY8g.png", "alt": "The weight and bias matrix defined in a sample neural network.", "downloaded": true, "discovery": "saved_markdown" }, { "status": "ok", "url": "https://assets.insightmediagroup.io/media/wp-content/uploads/2025/02/02kqALjWcLyiGL9YQ.png", "bytes": 146582, "reused": false, "content_type": "image/png", "local_path": "attachments/5361b27a4c83/images/body-39e5089ad8315e01e205.png", "src": "https://assets.insightmediagroup.io/media/wp-content/uploads/2025/02/02kqALjWcLyiGL9YQ.png", "alt": "Thinking of a dense network as weighted connections on the left, and as matrix multiplication on the right. On the right hand side diagram, the vector on the left would be the input, the matrix in the center would be the weight matrix, and the vector on the right would be the output. Only a portion of values are included for readability. From my article on LoRA.", "downloaded": true, "discovery": "saved_markdown" }, { "status": "ok", "url": "https://assets.insightmediagroup.io/media/wp-content/uploads/2025/02/12gnRkuco9SpHeEpKaynhrQ.png", "bytes": 1880, "reused": false, "content_type": "image/png", "local_path": "attachments/5361b27a4c83/images/body-dc606751352fd4bdd235.png", "src": "https://assets.insightmediagroup.io/media/wp-content/uploads/2025/02/12gnRkuco9SpHeEpKaynhrQ.png", "alt": "the result of passing our data through the model. Our model is randomly defined, so this isn't a useful prediction, but it confirms that the model is working.", "downloaded": true, "discovery": "saved_markdown" }, { "status": "ok", "url": "https://assets.insightmediagroup.io/media/wp-content/uploads/2025/02/11f28YdNHVOI7goP3REmrfA.png", "bytes": 82759, "reused": false, "content_type": "image/png", "local_path": "attachments/5361b27a4c83/images/body-894ff02482ae5551d5e5.png", "src": "https://assets.insightmediagroup.io/media/wp-content/uploads/2025/02/11f28YdNHVOI7goP3REmrfA.png", "alt": "The modeling objective. Given two inputs \\(here plotted as x and y\\), the model needs to predict an output \\(here represented as color\\). 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