395 skills found · Page 6 of 14
Conceptual-Inertia / ConceptumConceptum is a stack-based, lightweight, Turing-equivalent JIT virtual machine running a small set of bytecodes for benchmarking VM performance.
NishanthSpShetty / Stack VMSimple Stack based virtual machine
JLWalsh / StackVMA stack-based virtual machine written in C
svenslaggare / StackJITSimple virtual machine for a stack-based assembler language.
tim-group / StackbuilderStackbuilder builds stacks of virtual machines
hallboyone / RaspberryLatteA full stack suite to add smarts to a single boiler espresso machine using a Raspberry Pi Pico as the brains.
rcarmo / Azure ToolboxA Docker container with Azure Resource Manager administration tools and a machine/deep learning stack
rexich / Dockerific Elk VmDockerized ELK stack with some maintenance scripts for a bare metal host or virtual machine
reddyprasade / Machine Learning Interview PreparationPrepare to Technical Skills Here are the essential skills that a Machine Learning Engineer needs, as mentioned Read me files. Within each group are topics that you should be familiar with. Study Tip: Copy and paste this list into a document and save to your computer for easy referral. Computer Science Fundamentals and Programming Topics Data structures: Lists, stacks, queues, strings, hash maps, vectors, matrices, classes & objects, trees, graphs, etc. Algorithms: Recursion, searching, sorting, optimization, dynamic programming, etc. Computability and complexity: P vs. NP, NP-complete problems, big-O notation, approximate algorithms, etc. Computer architecture: Memory, cache, bandwidth, threads & processes, deadlocks, etc. Probability and Statistics Topics Basic probability: Conditional probability, Bayes rule, likelihood, independence, etc. Probabilistic models: Bayes Nets, Markov Decision Processes, Hidden Markov Models, etc. Statistical measures: Mean, median, mode, variance, population parameters vs. sample statistics etc. Proximity and error metrics: Cosine similarity, mean-squared error, Manhattan and Euclidean distance, log-loss, etc. Distributions and random sampling: Uniform, normal, binomial, Poisson, etc. Analysis methods: ANOVA, hypothesis testing, factor analysis, etc. Data Modeling and Evaluation Topics Data preprocessing: Munging/wrangling, transforming, aggregating, etc. Pattern recognition: Correlations, clusters, trends, outliers & anomalies, etc. Dimensionality reduction: Eigenvectors, Principal Component Analysis, etc. Prediction: Classification, regression, sequence prediction, etc.; suitable error/accuracy metrics. Evaluation: Training-testing split, sequential vs. randomized cross-validation, etc. Applying Machine Learning Algorithms and Libraries Topics Models: Parametric vs. nonparametric, decision tree, nearest neighbor, neural net, support vector machine, ensemble of multiple models, etc. Learning procedure: Linear regression, gradient descent, genetic algorithms, bagging, boosting, and other model-specific methods; regularization, hyperparameter tuning, etc. Tradeoffs and gotchas: Relative advantages and disadvantages, bias and variance, overfitting and underfitting, vanishing/exploding gradients, missing data, data leakage, etc. Software Engineering and System Design Topics Software interface: Library calls, REST APIs, data collection endpoints, database queries, etc. User interface: Capturing user inputs & application events, displaying results & visualization, etc. Scalability: Map-reduce, distributed processing, etc. Deployment: Cloud hosting, containers & instances, microservices, etc. Move on to the final lesson of this course to find lots of sample practice questions for each topic!
src-d / ModelsMachine learning models for MLonCode trained using the source{d} stack
lcmd-epfl / Q StackStack of codes for dedicated pre- and post-processing tasks for Quantum Machine Learning (QML)
Ran-Jia / GeoStackA stacking method of machine learning applied to geological geophysical datasets for 3D geological modeling
x676w / Stack VmJavaScript stack-based virtual machine obfuscator
PacktPublishing / Machine Learning With Elastic Stack Second EditionMachine Learning with Elastic Stack - Second Edition, published by Packt
haghish / ShapleyWeighted Shapley Values and Weighted Confidence Intervals for Multiple Machine Learning Models and Stacked Ensembles
Illation / BytecodeVMA basic Virtual machine operating on bytecode with a stack and Van Neumann Architecture. Built in Assembler.
vanvuongngo / ClaraNClaraN — Privacy-first, fully local AI workspace with Ollama LLM chat, tool calling, agent builder, Stable Diffusion, and embedded n8n-style automation. Backend in Rust. Just your stack, your machines.
xaderfos / Uxntal ModeA bare bones Emacs major mode for Uxntal, the programming language of the Uxn stack-machine.
emretuna / .dotfiles.dotfiles I used both on my linux machine and osx as a Full-stack developer.Fell free to use/fork or inspire.
Rushikesh8983 / MastersDataScience Deep Learning ProjectLanguage Translation In this project, you’re going to take a peek into the realm of neural network machine translation. You’ll be training a sequence to sequence model on a dataset of English and French sentences that can translate new sentences from English to French. Get the Data Since translating the whole language of English to French will take lots of time to train, we have provided you with a small portion of the English corpus. """ DON'T MODIFY ANYTHING IN THIS CELL """ import helper import problem_unittests as tests source_path = 'data/small_vocab_en' target_path = 'data/small_vocab_fr' source_text = helper.load_data(source_path) target_text = helper.load_data(target_path) Explore the Data Play around with view_sentence_range to view different parts of the data. view_sentence_range = (0, 10) """ DON'T MODIFY ANYTHING IN THIS CELL """ import numpy as np print('Dataset Stats') print('Roughly the number of unique words: {}'.format(len({word: None for word in source_text.split()}))) sentences = source_text.split('\n') word_counts = [len(sentence.split()) for sentence in sentences] print('Number of sentences: {}'.format(len(sentences))) print('Average number of words in a sentence: {}'.format(np.average(word_counts))) print() print('English sentences {} to {}:'.format(*view_sentence_range)) print('\n'.join(source_text.split('\n')[view_sentence_range[0]:view_sentence_range[1]])) print() print('French sentences {} to {}:'.format(*view_sentence_range)) print('\n'.join(target_text.split('\n')[view_sentence_range[0]:view_sentence_range[1]])) Dataset Stats Roughly the number of unique words: 227 Number of sentences: 137861 Average number of words in a sentence: 13.225277634719028 English sentences 0 to 10: new jersey is sometimes quiet during autumn , and it is snowy in april . the united states is usually chilly during july , and it is usually freezing in november . california is usually quiet during march , and it is usually hot in june . the united states is sometimes mild during june , and it is cold in september . your least liked fruit is the grape , but my least liked is the apple . his favorite fruit is the orange , but my favorite is the grape . paris is relaxing during december , but it is usually chilly in july . new jersey is busy during spring , and it is never hot in march . our least liked fruit is the lemon , but my least liked is the grape . the united states is sometimes busy during january , and it is sometimes warm in november . French sentences 0 to 10: new jersey est parfois calme pendant l' automne , et il est neigeux en avril . les états-unis est généralement froid en juillet , et il gèle habituellement en novembre . california est généralement calme en mars , et il est généralement chaud en juin . les états-unis est parfois légère en juin , et il fait froid en septembre . votre moins aimé fruit est le raisin , mais mon moins aimé est la pomme . son fruit préféré est l'orange , mais mon préféré est le raisin . paris est relaxant en décembre , mais il est généralement froid en juillet . new jersey est occupé au printemps , et il est jamais chaude en mars . notre fruit est moins aimé le citron , mais mon moins aimé est le raisin . les états-unis est parfois occupé en janvier , et il est parfois chaud en novembre . Implement Preprocessing Function Text to Word Ids As you did with other RNNs, you must turn the text into a number so the computer can understand it. In the function text_to_ids(), you'll turn source_text and target_text from words to ids. However, you need to add the <EOS> word id at the end of target_text. This will help the neural network predict when the sentence should end. You can get the <EOS> word id by doing: target_vocab_to_int['<EOS>'] You can get other word ids using source_vocab_to_int and target_vocab_to_int. def text_to_ids(source_text, target_text, source_vocab_to_int, target_vocab_to_int): """ Convert source and target text to proper word ids :param source_text: String that contains all the source text. :param target_text: String that contains all the target text. :param source_vocab_to_int: Dictionary to go from the source words to an id :param target_vocab_to_int: Dictionary to go from the target words to an id :return: A tuple of lists (source_id_text, target_id_text) """ # TODO: Implement Function source_id_text = [[source_vocab_to_int[word] for word in sentence.split()] \ for sentence in source_text.split('\n')] target_id_text = [[target_vocab_to_int[word] for word in sentence.split()] + [target_vocab_to_int['<EOS>']] \ for sentence in target_text.split('\n')] return source_id_text, target_id_text """ DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE """ tests.test_text_to_ids(text_to_ids) Tests Passed Preprocess all the data and save it Running the code cell below will preprocess all the data and save it to file. """ DON'T MODIFY ANYTHING IN THIS CELL """ helper.preprocess_and_save_data(source_path, target_path, text_to_ids) Check Point This is your first checkpoint. If you ever decide to come back to this notebook or have to restart the notebook, you can start from here. The preprocessed data has been saved to disk. import problem_unittests as tests """ DON'T MODIFY ANYTHING IN THIS CELL """ import numpy as np import helper (source_int_text, target_int_text), (source_vocab_to_int, target_vocab_to_int), _ = helper.load_preprocess() Check the Version of TensorFlow and Access to GPU This will check to make sure you have the correct version of TensorFlow and access to a GPU """ DON'T MODIFY ANYTHING IN THIS CELL """ from distutils.version import LooseVersion import warnings import tensorflow as tf from tensorflow.python.layers.core import Dense # Check TensorFlow Version assert LooseVersion(tf.__version__) >= LooseVersion('1.1'), 'Please use TensorFlow version 1.1 or newer' print('TensorFlow Version: {}'.format(tf.__version__)) # Check for a GPU if not tf.test.gpu_device_name(): warnings.warn('No GPU found. Please use a GPU to train your neural network.') else: print('Default GPU Device: {}'.format(tf.test.gpu_device_name())) TensorFlow Version: 1.1.0 Default GPU Device: /gpu:0 Build the Neural Network You'll build the components necessary to build a Sequence-to-Sequence model by implementing the following functions below: model_inputs process_decoder_input encoding_layer decoding_layer_train decoding_layer_infer decoding_layer seq2seq_model Input Implement the model_inputs() function to create TF Placeholders for the Neural Network. It should create the following placeholders: Input text placeholder named "input" using the TF Placeholder name parameter with rank 2. Targets placeholder with rank 2. Learning rate placeholder with rank 0. Keep probability placeholder named "keep_prob" using the TF Placeholder name parameter with rank 0. Target sequence length placeholder named "target_sequence_length" with rank 1 Max target sequence length tensor named "max_target_len" getting its value from applying tf.reduce_max on the target_sequence_length placeholder. Rank 0. Source sequence length placeholder named "source_sequence_length" with rank 1 Return the placeholders in the following the tuple (input, targets, learning rate, keep probability, target sequence length, max target sequence length, source sequence length) def model_inputs(): """ Create TF Placeholders for input, targets, learning rate, and lengths of source and target sequences. :return: Tuple (input, targets, learning rate, keep probability, target sequence length, max target sequence length, source sequence length) """ # TODO: Implement Function inputs = tf.placeholder(tf.int32, [None, None], 'input') targets = tf.placeholder(tf.int32, [None, None]) learning_rate = tf.placeholder(tf.float32, []) keep_prob = tf.placeholder(tf.float32, [], 'keep_prob') target_sequence_length = tf.placeholder(tf.int32, [None], 'target_sequence_length') max_target_len = tf.reduce_max(target_sequence_length) source_sequence_length = tf.placeholder(tf.int32, [None], 'source_sequence_length') return inputs, targets, learning_rate, keep_prob, target_sequence_length, max_target_len, source_sequence_length """ DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE """ tests.test_model_inputs(model_inputs) Tests Passed Process Decoder Input Implement process_decoder_input by removing the last word id from each batch in target_data and concat the GO ID to the begining of each batch. def process_decoder_input(target_data, target_vocab_to_int, batch_size): """ Preprocess target data for encoding :param target_data: Target Placehoder :param target_vocab_to_int: Dictionary to go from the target words to an id :param batch_size: Batch Size :return: Preprocessed target data """ # TODO: Implement Function go = tf.constant([[target_vocab_to_int['<GO>']]]*batch_size) # end = tf.slice(target_data, [0, 0], [-1, batch_size]) end = tf.strided_slice(target_data, [0, 0], [batch_size, -1], [1, 1]) return tf.concat([go, end], 1) """ DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE """ tests.test_process_encoding_input(process_decoder_input) Tests Passed Encoding Implement encoding_layer() to create a Encoder RNN layer: Embed the encoder input using tf.contrib.layers.embed_sequence Construct a stacked tf.contrib.rnn.LSTMCell wrapped in a tf.contrib.rnn.DropoutWrapper Pass cell and embedded input to tf.nn.dynamic_rnn() from imp import reload reload(tests) def encoding_layer(rnn_inputs, rnn_size, num_layers, keep_prob, source_sequence_length, source_vocab_size, encoding_embedding_size): """ Create encoding layer :param rnn_inputs: Inputs for the RNN :param rnn_size: RNN Size :param num_layers: Number of layers :param keep_prob: Dropout keep probability :param source_sequence_length: a list of the lengths of each sequence in the batch :param source_vocab_size: vocabulary size of source data :param encoding_embedding_size: embedding size of source data :return: tuple (RNN output, RNN state) """ # TODO: Implement Function embed = tf.contrib.layers.embed_sequence(rnn_inputs, source_vocab_size, encoding_embedding_size) def lstm_cell(): lstm = tf.contrib.rnn.BasicLSTMCell(rnn_size) return tf.contrib.rnn.DropoutWrapper(lstm, keep_prob) stacked_lstm = tf.contrib.rnn.MultiRNNCell([lstm_cell() for _ in range(num_layers)]) # initial_state = stacked_lstm.zero_state(source_sequence_length, tf.float32) return tf.nn.dynamic_rnn(stacked_lstm, embed, source_sequence_length, dtype=tf.float32) # initial_state=initial_state) """ DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE """ tests.test_encoding_layer(encoding_layer) Tests Passed Decoding - Training Create a training decoding layer: Create a tf.contrib.seq2seq.TrainingHelper Create a tf.contrib.seq2seq.BasicDecoder Obtain the decoder outputs from tf.contrib.seq2seq.dynamic_decode def decoding_layer_train(encoder_state, dec_cell, dec_embed_input, target_sequence_length, max_summary_length, output_layer, keep_prob): """ Create a decoding layer for training :param encoder_state: Encoder State :param dec_cell: Decoder RNN Cell :param dec_embed_input: Decoder embedded input :param target_sequence_length: The lengths of each sequence in the target batch :param max_summary_length: The length of the longest sequence in the batch :param output_layer: Function to apply the output layer :param keep_prob: Dropout keep probability :return: BasicDecoderOutput containing training logits and sample_id """ # TODO: Implement Function helper = tf.contrib.seq2seq.TrainingHelper(dec_embed_input, target_sequence_length) decoder = tf.contrib.seq2seq.BasicDecoder(dec_cell, helper, encoder_state, output_layer) dec_train_logits, _ = tf.contrib.seq2seq.dynamic_decode(decoder, maximum_iterations=max_summary_length) # for tensorflow 1.2: # dec_train_logits, _, _ = tf.contrib.seq2seq.dynamic_decode(decoder, maximum_iterations=max_summary_length) return dec_train_logits # keep_prob/dropout not used? """ DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE """ tests.test_decoding_layer_train(decoding_layer_train) Tests Passed Decoding - Inference Create inference decoder: Create a tf.contrib.seq2seq.GreedyEmbeddingHelper Create a tf.contrib.seq2seq.BasicDecoder Obtain the decoder outputs from tf.contrib.seq2seq.dynamic_decode def decoding_layer_infer(encoder_state, dec_cell, dec_embeddings, start_of_sequence_id, end_of_sequence_id, max_target_sequence_length, vocab_size, output_layer, batch_size, keep_prob): """ Create a decoding layer for inference :param encoder_state: Encoder state :param dec_cell: Decoder RNN Cell :param dec_embeddings: Decoder embeddings :param start_of_sequence_id: GO ID :param end_of_sequence_id: EOS Id :param max_target_sequence_length: Maximum length of target sequences :param vocab_size: Size of decoder/target vocabulary :param decoding_scope: TenorFlow Variable Scope for decoding :param output_layer: Function to apply the output layer :param batch_size: Batch size :param keep_prob: Dropout keep probability :return: BasicDecoderOutput containing inference logits and sample_id """ # TODO: Implement Function start_tokens = tf.constant([start_of_sequence_id]*batch_size) helper = tf.contrib.seq2seq.GreedyEmbeddingHelper(dec_embeddings, start_tokens, end_of_sequence_id) decoder = tf.contrib.seq2seq.BasicDecoder(dec_cell, helper, encoder_state, output_layer) dec_infer_logits, _ = tf.contrib.seq2seq.dynamic_decode(decoder, maximum_iterations=max_target_sequence_length) # for tensorflow 1.2: # dec_infer_logits, _, _ = tf.contrib.seq2seq.dynamic_decode(decoder, maximum_iterations=max_target_sequence_length) return dec_infer_logits """ DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE """ tests.test_decoding_layer_infer(decoding_layer_infer)
