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    Question about Sipser theory of computation book. Computer Science

    August 31, 2020

    Question about Sipser theory of computation book. Computer Science

    Question about Sipser theory of computation book.

    Posted: 30 Aug 2020 05:55 PM PDT

    Hello guys,

    So i have been reading this book, currently on chapter 3. My question is regarding the first two chapters, they cover the formalization of simple models like non/deterministic finite automatas. I fail to understand the practical implications of this structures as well as why there is a need to formalize them.

    An example is why there are certain limitations for the most basic finite automata that seem auto imposed when you could define pushdown finite automata in the first place. ( not even mentioning a Turing machine)

    Thanks!

    submitted by /u/surface33
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    How To Cartoonize a Live Video Footage Using Computer Vision

    Posted: 30 Aug 2020 09:43 AM PDT

    Car Park Code

    Posted: 31 Aug 2020 03:45 AM PDT

    What's really wrong with my random_mini_batches function? [Deep Learning]

    Posted: 31 Aug 2020 01:12 AM PDT

    minibatches = random_mini_batches(X_train, Y_train, minibatch_size, seed)

    X_Train(11[features]x4898[instances])

    Y_Train(11[classes: 0-10]x4898)

    minibatch_size = 32

    def random_mini_batches(X, Y, mini_batch_size = 64, seed = 0): m = X.shape[1] # number of training examples mini_batches = [] np.random.seed(seed) # Step 1: Shuffle (X, Y) permutation = list(np.random.permutation(m)) shuffled_X = X[:, permutation] shuffled_Y = Y[:, permutation].reshape((Y.shape[0],m)) # Step 2: Partition (shuffled_X, shuffled_Y). Minus the end case. num_complete_minibatches = math.floor(m/mini_batch_size) # number of mini batches of size mini_batch_size in your partitionning for k in range(0, num_complete_minibatches): mini_batch_X = shuffled_X[:, k * mini_batch_size : k * mini_batch_size + mini_batch_size] mini_batch_Y = shuffled_Y[:, k * mini_batch_size : k * mini_batch_size + mini_batch_size] mini_batch = (mini_batch_X, mini_batch_Y) mini_batches.append(mini_batch) # Handling the end case (last mini-batch < mini_batch_size) if m % mini_batch_size != 0: mini_batch_X = shuffled_X[:, num_complete_minibatches * mini_batch_size : m] mini_batch_Y = shuffled_Y[:, num_complete_minibatches * mini_batch_size : m] mini_batch = (mini_batch_X, mini_batch_Y) mini_batches.append(mini_batch) return mini_batches
    submitted by /u/JacksonSteel
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    environment variables?

    Posted: 30 Aug 2020 07:51 PM PDT

    Sometimes When I am installing a new IDE or distribution I am asked to set Environment Variables , What are those actually?

    submitted by /u/ashhwathama
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    Resource for learning about Pseudo Random Number Generators?

    Posted: 30 Aug 2020 05:01 AM PDT

    I'm interested to learn more about PRNGs. I started reading dozens of Wikipedia pages but it doesn't follow a "pedagogical flow", like how different methods are building on top of each other or what's the theory behind each PRNGs. Wikipedia pages are more suitable for those who already have a grasp on the material or want to copy a pseudocode or sth.

    Is there any (mini) books or book chapters on PRNGs?

    I'm a software developer and in the senior year of a CS bachelor program .

    submitted by /u/fiveMop
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    Identity as a service in cloud computing

    Posted: 30 Aug 2020 05:19 AM PDT
    Read more
    at

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