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(ii) Interpret the important principles of marketing campaign, which helped to increase the sales and reputation of the brand, with your critical comments on the case study" ] }, { "source": "apr/content.js", "jsonPath": [ "routes", "tee", "papers", 7, "sections", 14, "html" ], "route": "https://deep-learning-study-room.vercel.app/apr/#tee/papers/paper-2024-12-11/q7", "labels": [ "2024-12-11 · Semester V final / re-exam", "Q7 · Discuss the Case Study of ITC for its Business Strategy by explaining the following: (i) Completely discuss the case study. 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J_{\\mathrm{data}}+2\\lambda w" } ], "contexts": [ { "source": "data.js", "jsonPath": [ "books", "mt2", 5, "sections", 1, "blocks", 65 ], "route": "https://deep-learning-study-room.vercel.app/#mt2/read/6/mt2-c6-s1", "labels": [ "Regularization and generalization", "L1 and L2 add a cost for large weights" ] }, { "source": "data.js", "jsonPath": [ "books", "tee", 5, "sections", 1, "blocks", 65 ], "route": "https://deep-learning-study-room.vercel.app/#tee/read/6/tee-c6-s1", "labels": [ "Regularization and generalization", "L1 and L2 add a cost for large weights" ] } ], "svg": { "title": [], "description": [], "labels": [] }, "machineReadableRepresentation": "latex-and-original-svg" }, { "url": "https://deep-learning-study-room.vercel.app/assets/equations/5660f8c2c8b62a6f.svg", "path": "assets/equations/5660f8c2c8b62a6f.svg", "mime": "image/svg+xml", "bytes": 21210, "sha256": "6d14461a89288765383e7fe02da936a89a626f47e21d6225f2dd3762ce107b36", "hashNormalization": "LF", "kinds": [ 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"source": "data.js", "jsonPath": [ "books", "tee", 16, "sections", 17, "answer", 17 ], "route": "https://deep-learning-study-room.vercel.app/#tee/read/17/tee-c17-s16", "labels": [ "Numerical practice from direct to mixed", "Drill 16 Batch and sequential SGD" ] } ], "svg": { "title": [], "description": [], "labels": [] }, "machineReadableRepresentation": "latex-and-original-svg" }, { "url": "https://deep-learning-study-room.vercel.app/assets/equations/77839b654a5d343b.svg", "path": "assets/equations/77839b654a5d343b.svg", "mime": "image/svg+xml", "bytes": 6530, "sha256": "8ce268f24951ce8d11e02cb59519acfc5cf6ce0d627002f31b884891b6bfaeaa", "hashNormalization": "LF", "kinds": [ "equation" ], "equationLatex": [ "Y_{1,3}=(0)+(-2)=-2" ], "annotations": [ { "type": "alt", "text": "Y_{1,3}=(0)+(-2)=-2" } ], "contexts": [ { "source": "data.js", "jsonPath": [ "books", "mt2", 16, "sections", 41, "answer", 77 ], "route": "https://deep-learning-study-room.vercel.app/#mt2/read/17/mt2-c17-s40", "labels": [ "MT2 numerical practice from direct to mixed", "Drill 40 Mixed convolution pool dense and loss" ] }, { "source": "data.js", "jsonPath": [ "books", "tee", 16, "sections", 47, "answer", 77 ], "route": "https://deep-learning-study-room.vercel.app/#tee/read/17/tee-c17-s46", "labels": [ "Numerical practice from direct to mixed", "Drill 46 Mixed convolution pool dense and loss" ] } ], "svg": { "title": [], "description": [], "labels": [] }, "machineReadableRepresentation": "latex-and-original-svg" }, { "url": "https://deep-learning-study-room.vercel.app/assets/equations/7790608d36a6c436.svg", "path": "assets/equations/7790608d36a6c436.svg", "mime": "image/svg+xml", "bytes": 19410, "sha256": "4f1c4377519438f1ab87195df15283d407962e322290554a532bf0b14be166ae", "hashNormalization": "LF", "kinds": [ "equation", "responsive-image" ], "equationLatex": [ "W^{(2)}_{1,2,\\mathrm{new}}=0.24-1(0.010710144)\\approx 0.229289856" ], "annotations": [ { "type": "alt", "text": 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W^{(1)}_{3,1}}=\\delta_{h_3}x_1" } ], "contexts": [ { "source": "data.js", "jsonPath": [ "books", "tee", 13, "sections", 5, "answer", 177 ], "route": "https://deep-learning-study-room.vercel.app/#tee/read/14/tee-c14-s4", "labels": [ "Re examination February 2024 fully solved", "R24 Q2a" ] } ], "svg": { "title": [], "description": [], "labels": [] }, "machineReadableRepresentation": "latex-and-original-svg" }, { "url": "https://deep-learning-study-room.vercel.app/assets/equations/c660725ff6ab02eb.svg", "path": "assets/equations/c660725ff6ab02eb.svg", "mime": "image/svg+xml", "bytes": 18661, "sha256": "4e47826812990686733b52d77b73ac9c799cc9d4a764e0872cd758cd7da98c4b", "hashNormalization": "LF", "kinds": [ "equation" ], "equationLatex": [ "\\sigma(z)=\\frac1{1+e^{-z}},\\quad C=fC_{\\mathrm{previous}}+i\\widetilde C,\\quad h=o\\tanh(C)" ], "annotations": [ { "type": "alt", "text": "\\sigma(z)=\\frac1{1+e^{-z}},\\quad C=fC_{\\mathrm{previous}}+i\\widetilde C,\\quad h=o\\tanh(C)" } ], 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