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海外TECH	DEV Community	You will not need another VSCode theme extension ever again after this 👉	https://dev.to/lakshitsomani/you-will-not-need-another-vscode-theme-extension-ever-again-after-this-383a	You will not need another VSCode theme extension ever again after this Unleash the power of Best Themes Redefined and bid farewell to the endless pursuit of the perfect VSCode VSCodium theme Brace yourself for a coding revolution unlike any other Say goodbye to the never ending cycle of theme hopping and embrace the one stop solution that will redefine your coding journey for eternity With Best Themes Redefined your quest for the ultimate theme ends here Immerse yourself in a world of breathtaking designs vibrant hues and unparalleled customization options Elevate your coding experience to new heights and experience a level of productivity and inspiration you never thought possible Get ready to say I have found the holy grail of VSCode themes Best Themes Redefined A distinguished collection of meticulously crafted themes that strike the perfect balance between aesthetics and functionality Immerse Yourself in Beauty and Creativity Unleash your imagination and surround yourself with a stunning array of vibrant visually captivating themes Best Themes Redefined offers an extensive collection of meticulously crafted themes each crafted to bring out the best in your code Immerse yourself in a world of vibrant colors stunning contrasts and carefully selected palettes meticulously designed to enhance your productivity and bring your code to life Let your creativity soar as you explore the diverse range of themes offered in this extension Each theme is thoughtfully curated with its own unique personality to cater to every coder s taste and preference Theme ListHere s a full list of the themes included in the Best Themes Redefined extension Best Themes Blueberry BananaBest Themes BrogrammerBest Themes Darcula DarkerBest Themes DarculaBest Themes Dark PhoenixBest Themes DarwinBest Themes Default Dark Plus BlackBest Themes Dracula Redefined No ItalicBest Themes Dracula RedefinedBest Themes Electron Best Themes ElectronBest Themes Gruvbox Concoctis DarkBest Themes Gruvbox DarkBest Themes Gruvbox Material DarkBest Themes Gruvbox NvChadBest Themes HorizonBest Themes KarmaBest Themes Laserwave ItalicBest Themes LaserwaveBest Themes Material Black ItalicBest Themes Material Black No ItalicBest Themes Material High Contrast ItalicBest Themes Material High Contrast No ItalicBest Themes Material Ocean ItalicBest Themes Material Ocean No ItalicBest Themes Monokai AwesomeBest Themes Monokai BlackBest Themes Monokai Night Time ItalicBest Themes Monokai Night Time No ItalicBest Themes Monokai Pirokai Arctic FrostBest Themes Monokai Pirokai Beach SunsetBest Themes Monokai Winter NightBest Themes Mystic CyanBest Themes Neon CityBest Themes Night Owl No ItalicBest Themes Night OwlBest Themes Nord ColdBest Themes Nord DarkBest Themes Ocean NightBest Themes One Dark Pro No ItalicBest Themes One Dark ProBest Themes One Monokai DarkerBest Themes One MonokaiBest Themes Outrun SpaceBest Themes Panda SyntaxBest Themes Pink PantherBest Themes Sia SynthwaveBest Themes Synthwave Black No Neon Best Themes Xcode Catalina BoldBest Themes Xcode CatalinaBest Themes Xcode Fusion Installation Guide Download using this link ORFollow these simple steps to install Best Themes Redefined and elevate your coding environment in editor itself Launch Visual Studio Code Go to the Extensions view by clicking on the square icon in the sidebar or pressing Ctrl Shift X Cmd Shift X on macOS Search for Best Themes Redefined in the extensions marketplace Click Install to add the extension to your workspace ️Once installed click on the gear icon in the lower left corner to open the settings menu ️Select Color Theme and choose your desired theme from the extensive list provided by Best Themes Redefined Voila Your code editor is now adorned with the beauty of your chosen theme Recommended Settings for the Best Experience ️To make the most of Best Themes Redefined we recommend configuring the following settings Use Nerd Font ️ Best Themes Redefined truly shines when paired with a Nerd Font Install a Nerd Font of your choice such as Fira Code Nerd Font or JetBrains Mono Nerd Font and set it as your editor s font family to unlock the full visual potential of the themes Enable Ligatures Ligatures are a typographic feature that combine two or more characters into a single glyph We recommend enabling ligatures for an enhanced coding experience To further enhance your productivity and coding experience we recommend these additional VS Code settings editor fontFamily JetbrainsMono Nerd Font FiraCode Nerd Font Hack Nerd Font monospace editor smoothScrolling true terminal integrated fontFamily JetbrainsMono Nerd Font FiraCode Nerd Font Hack Nerd Font monospace terminal integrated lineHeight editor fontLigatures true editor lineHeight editor minimap enabled false editor tabSize workbench iconTheme material icon theme editor semanticTokenColorCustomizations enabled true editor semanticHighlighting enabled true These settings will harmonize seamlessly with Best Themes Redefined ensuring a visually cohesive and productive coding environment About the Developer ‍Hey there Best Themes Redefined is developed and maintained by me As a passionate software engineer I thrive on innovative ideas and customizations that elevate the coding experience With an artistic flair and technical expertise I craft visually stunning themes that push the boundaries of creativity Collaborating with developers worldwide I continuously refine and enhance the collection to inspire productivity and ignite imagination Join me on this exciting journey as we redefine coding aesthetics making every line of code a work of art Together let s shape the future of development and unlock new realms of possibility Get Started with Best Themes Redefined Today Get ready to embark on a coding journey like no other with Best Themes Redefined Let your creativity soar as you dive into the depths of these captivating themes invigorating your coding experience with every keystroke Install the extension choose your favorite theme and let the beauty and creativity unfold before your eyes Elevate your productivity enjoy coding like never before and watch your imagination take flight Happy coding Share Your Feedback and Spread the Word ️We value your feedback and would love to hear your thoughts on Best Themes Redefined If you have any suggestions feature requests or just want to share your experience don t hesitate to reach out to us Your feedback helps us improve and create even better themes for you If you find Best Themes Redefined to be a productivity boosting addition to your coding arsenal we encourage you to share it with your fellow developers and friends Let them experience the beauty and creativity that Best Themes Redefined brings to their coding environment Together we can create a community of inspired coders using the best themes out there Github Repo here	2023-05-21 18:42:22
海外TECH	DEV Community	Deep Neural Network from Scratch in Rust 🦀 - Part 3- Forward Propagation	https://dev.to/akshayballal/deep-neural-network-from-scratch-in-rust-part-3-forward-propagation-2jp5	Deep Neural Network from Scratch in Rust Part Forward Propagation In the previous part of our blog series we discussed how to initialize a neural network NN model with specified layers and hidden units Now in this part we will explore the forward propagation algorithm a fundamental step in the NN s prediction process Before we delve into the coding aspect let s understand the mathematical concepts underlying forward propagation We will use the following notations Z l Logit Matrix for layer l It represents thelinear transformation of the inputs for a particular layer A l Activation matrix for layer l It representsthe output or activation values of the neurons in a specific layer W l Weights matrix for layer l It contains theweights connecting the neurons of layer l to the neurons oflayer l b l Bias matrix for layer l It contains the biasvalues added to the linear transformation of the inputs for layerl Additionally we have the input matrix denoted as X which is equal to the activation matrix A of the input layer To perform forward propagation we need to follow these two steps for each layer Calculate the logit matrix for each layer using the following expression Z l W l A l b l In simpler terms the logit matrix for layer l is obtained by taking the dot product of the weight matrix W l and the activation matrix A l from the previous layer and then adding the bias matrix b l This step represents the linear transformation of the inputs for the current layer Calculate the activation matrix from the logit matrix using an activation function A l ActivationFunction Z l Here the activation function can be any non linear function applied element wise to the elements of the logit matrix Popular activation functions include sigmoid tanh and relu In our model we will use the relu activation function for all intermediate layers and sigmoid for the last layer classifier layer This step introduces non linearity into the network allowing it to learn and model complex relationships in the data For n l number of hidden units in layer l and m number of examples these are the shapes of each matrix Z l ⇾ n l x m W l ⇾ n l x n l b l ⇾ n l x A l ⇾ n l x m During the forward propagation process we will store the weight matrix bias matrix and logit matrix as cache This stored information will prove useful in the subsequent step of backward propagation where we update the model s parameters based on the computed gradients By performing forward propagation our neural network takes the input data through all the layers applying linear transformations and activation functions and eventually produces a prediction or output at the final layer DependenciesAdd this line to the Cargo toml file num integer Cache StructsFirst in the lib rs file we will define two structs LinearCache and ActivationCache lib rsuse num integer Roots derive Clone Debug pub struct LinearCache pub a Array lt f gt pub w Array lt f gt pub b Array lt f gt derive Clone Debug pub struct ActivationCache pub z Array lt f gt The LinearCache struct stores the intermediate values needed for each layer It includes the activation matrix a weight matrix w and bias matrix b These matrices are used to calculate the logit matrix z in the forward propagation process The ActivationCache struct stores the logit matrix z for each layer This cache is essential for later stages such as backpropagation where the stored values are required Define Activation FunctionsNext let us define the non linear activation functions that we will be using relu and sigmoid lib rspub fn sigmoid z amp f gt f E powf z pub fn relu z amp f gt f match z gt true gt z false gt pub fn sigmoid activation z Array lt f gt gt Array lt f gt ActivationCache z mapv x sigmoid amp x ActivationCache z pub fn relu activation z Array lt f gt gt Array lt f gt ActivationCache z mapv x relu amp x ActivationCache z Activation functions introduce non linearity to neural networks and play a crucial role in the forward propagation process The code provides implementations for two commonly used activation functions sigmoid and relu The sigmoid function takes a single value z as input and returns the sigmoid activation which is calculated using the sigmoid formula e z The sigmoid function maps the input value to a range between and enabling the network to model non linear relationships The relu function takes a single value z as input and applies the Rectified Linear Unit ReLU activation If z is greater than zero the function returns z otherwise it returns zero ReLU is a popular activation function that introduces non linearity and helps the network learn complex patterns Both sigmoid and relu functions are used for individual values or as building blocks for the matrix based activation functions The code also provides two matrix based activation functions sigmoid activation and relu activation These functions take a D matrix z as input and apply the respective activation function element wise using the mapv function The resulting activation matrix is returned along with an ActivationCache struct that stores the corresponding logit matrix Linear Forward lib rspub fn linear forward a amp Array lt f gt w amp Array lt f gt b amp Array lt f gt gt Array lt f gt LinearCache let z w dot a b let cache LinearCache a a clone w w clone b b clone return z cache The linear forward function takes the activation matrix a weight matrix w and bias matrix b as inputs It performs the linear transformation by calculating the dot product of w and a and then adding b to the result The resulting matrix z represents the logits of the layer The function returns z along with a LinearCache struct that stores the input matrices for later use in backward propagation Linear Forward Activation lib rspub fn linear forward activation a amp Array lt f gt w amp Array lt f gt b amp Array lt f gt activation amp str gt Result lt Array lt f gt LinearCache ActivationCache String gt match activation sigmoid gt let z linear cache linear forward a w b let a next activation cache sigmoid activation z return Ok a next linear cache activation cache relu gt let z linear cache linear forward a w b let a next activation cache relu activation z return Ok a next linear cache activation cache gt return Err wrong activation string to string The linear forward activation function builds upon the linear forward function It takes the same input matrices as linear forward along with an additional activation parameter indicating the activation function to be applied The function first calls linear forward to obtain the logits z and the linear cache Then depending on the specified activation function it calls either sigmoid activation or relu activation to compute the activation matrix a next and the activation cache The function returns a next along with a tuple of the linear cache and activation cache wrapped in a Result enum If the specified activation function is not supported an error message is returned Forward Propagationimpl DeepNeuralNetwork Initializes the parameters of the neural network Returns a Hashmap dictionary of randomly initialized weights and biases pub fn initialize parameters amp self gt HashMap lt String Array lt f gt gt same as last part pub fn forward amp self x amp Array lt f gt parameters amp HashMap lt String Array lt f gt gt gt Array lt f gt HashMap lt String LinearCache ActivationCache gt let number of layers self layers len let mut a x clone let mut caches HashMap new for l in number of layers let w string W amp l to string join to string let b string b amp l to string join to string let w amp parameters amp w string let b amp parameters amp b string let a temp cache temp linear forward activation amp a w b relu unwrap a a temp caches insert l to string cache temp Compute activation of last layer with sigmoid let weight string W amp number of layers to string join to string let bias string b amp number of layers to string join to string let w amp parameters amp weight string let b amp parameters amp bias string let al cache linear forward activation amp a w b sigmoid unwrap caches insert number of layers to string cache return al caches The forward method in the DeepNeuralNetwork implementation performs the forward propagation process for the entire neural network It takes the input matrix x and the parameters weights and biases as inputs The method initializes the a matrix as a copy of x and creates an empty hashmap caches to store the caches for each layer Next it iterates over each layer except the last layer in a for loop For each layer it retrieves the corresponding weights w and biases b from the parameters using string concatenation It then calls linear forward activation with a w b and the activation function set to relu The resulting activation matrix a temp and the cache cache temp are stored in the caches hashmap using the layer index as the key The a matrix is updated to a temp for the next iteration After processing all intermediate layers the activation of the last layer is computed using the sigmoid activation function It retrieves the weights w and biases b for the last layer from the parameters and calls linear forward activation with a w b and the activation function set to sigmoid The resulting activation matrix al and the cache cache are stored in the caches hashmap using the last layer index as the key Finally the method returns the final activation matrix al and the caches hashmap containing all the caches for each layer Here al is the activation of the final layer and will be used to make the predictions during the inference part of our process That is all for Forward PropagationIn conclusion we ve covered an important aspect of building a deep neural network in this blog post forward propagation We learned how the input data moves through the layers undergoes linear transformations and is activated using different functions But our journey doesn t end here In the next blog post we ll dive into exciting topics like loss function and backward propagation We ll explore how to measure the error between predictions and actual outputs and how to use that error to update our model These steps are crucial for training the neural network and improving its performance So stay tuned for the next blog post where we ll understand and implement a binary cross entropy loss function and perform backpropagation My Website Twitter‍LinkedIn	2023-05-21 18:08:26
Apple	AppleInsider - Frontpage News	Withings Body Smart scale review: Consistently inconsistent	https://appleinsider.com/articles/23/05/21/withings-body-smart-scale-review-consistently-inconsistent?utm_medium=rss	Withings Body Smart scale review Consistently inconsistentThe Withings Body Smart scale tracks body composition metrics to reach your fitness goals but its drawbacks may outweigh its benefits Review Withings Body Smart scaleYou might need more than a standard bathroom scale for fitness objectives like losing fat or gaining muscle A basic body scale can only tell you how much you weigh but not how much of that weight is fat or muscle Read more	2023-05-21 18:07:02
Apple	AppleInsider - Frontpage News	How to recover Notes stored on your Mac	https://appleinsider.com/inside/macos/tips/how-to-recover-notes-stored-on-your-mac?utm_medium=rss	How to recover Notes stored on your MacApple s Notes app stores local copies of your Notes on your Mac Here s how to find them Apple s Notes app lets you store notes locally and in iCloud For both types of notes macOS makes a local copy and stores them for the Notes app to use Should you need to find these for any reason or if for some reason you need to recover the thumbnail images for notes you can do it locally on your Startup Disk You might want to do this if you ve accidentally deleted a note or want to recover a cached thumbnail from a note that was deleted long ago You might also want to use the local files if you want to reinstall macOS or set up a new Mac although in the latter case you re probably better off syncing all the Notes to iCloud and then re syncing the new Mac to it to download everything Read more	2023-05-21 18:17:11
Apple	AppleInsider - Frontpage News	How to back up your Mac's Contacts in macOS	https://appleinsider.com/inside/macos/tips/how-to-back-up-your-macs-contacts-in-macos?utm_medium=rss	How to back up your Mac x s Contacts in macOSThe Mac s Contacts app is incredibly useful for storing personal and work contact info Here s how to back up your Contacts database Contacts in macOSmacOS s Contacts app lives in the Applications folder at the root of your Startup Disk When you add new Contacts each page or vCard is stored in a local database on disk at Library Application Support AddressBook the Contacts app was called Address Book in earlier versions of macOS Read more	2023-05-21 18:17:53
海外TECH	Engadget	Beijing bans Chinese companies from using Micron chips in critical infrastructure	https://www.engadget.com/beijing-bans-chinese-companies-from-using-micron-chips-in-critical-infrastructure-183039607.html?src=rss	Beijing bans Chinese companies from using Micron chips in critical infrastructureChina s cybersecurity regulator has banned Chinese firms from buying chips from US memory manufacturer Micron Technology Per Reuters the Cyberspace Administration of China CAC said Sunday it found that the company s products pose “significant security risks to critical Chinese information infrastructure including state owned banks and telecom operators The ban comes after China announced a review of Micron imports in late March in a move that was seen at the time as retaliation for sanctions Washington has imposed on Chinese chipmakers in recent years Idaho based Micron is the largest memory manufacturer in the US The Chinese market accounts for about percent of the firm s annual revenue though the majority of companies importing Micron products into China are manufacturers making devices for sale in other parts of the world According to The Wall Street Journal the CAC s ban does not apply to non Chinese firms in China “We are evaluating the conclusion and assessing our next steps Micron told the outlet “We look forward to continuing to engage in discussions with Chinese authorities The CAC did not say what Micron products would be affected by the ban nor did it share details on what security concerns it had with the company s chips The ban is the latest development in an escalating feud over semiconductor technology between the US and China In recent months the Biden administration has moved to restrict its rival s access to advanced chipmaking equipment In January US Dutch and Japanese officials agreed to tighten export controls on lithography machines from ASL Nikon and Tokyo Electron As The Journal notes China has been trying to find ways to hit back at the US Micron was an easy target given that most Chinese companies can turn to suppliers like South Korea s SK Hynix to make up for any shortfall left by a ban This article originally appeared on Engadget at	2023-05-21 18:30:39
ニュース	BBC News - Home	Rishi Sunak to meet ethics adviser over Suella Braverman speeding claims	https://www.bbc.co.uk/news/uk-politics-65659053?at_medium=RSS&at_campaign=KARANGA	attorney	2023-05-21 18:42:03
ニュース	BBC News - Home	Greece election: Centre-right wins but set to miss out on majority	https://www.bbc.co.uk/news/world-europe-65666261?at_medium=RSS&at_campaign=KARANGA	outright	2023-05-21 18:56:24
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ビジネス	ダイヤモンド・オンライン - 新着記事	金融不安は長期化の公算大、危機の火種になり得るノンバンクの監督強化を - 数字は語る	https://diamond.jp/articles/-/323225	金融不安は長期化の公算大、危機の火種になり得るノンバンクの監督強化を数字は語る米国を中心に、世界的に金融システム不安が台頭している。	2023-05-22 03:45:00
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ビジネス	ダイヤモンド・オンライン - 新着記事	石油生産で大手撤退、相次ぐ中小参入 - WSJ PickUp	https://diamond.jp/articles/-/323217	wsjpickup	2023-05-22 03:30:00
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ビジネス	ダイヤモンド・オンライン - 新着記事	AIの進化に不安を感じる人へ…日本の著名ロボット開発者が今伝えたいこと - ニュースな本	https://diamond.jp/articles/-/323088	AIの進化に不安を感じる人へ…日本の著名ロボット開発者が今伝えたいことニュースな本画像や文章生成AIが爆発的に世の中に浸透する中で、世界や未来に不安を抱えている人は少なくありません。	2023-05-22 03:15:00
ビジネス	ダイヤモンド・オンライン - 新着記事	「本当に向いている仕事」と「実は向いていない仕事」どうやって見分けるのが正解？ - 佐久間宣行のずるい仕事術	https://diamond.jp/articles/-/322888	佐久間宣行	2023-05-22 03:10:00
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