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The wait is over! - it’s time to celebrate the champion startups of the world. Shout out to all of the participants in our ! You did a wonderful job showcasing your incredible startups and powering through another year. 2023 Startups of the Year is a community-driven voting platform brought to you by HackerNoon that recognizes and celebrates tech startups changing technology and the world for the better. The award started on May 8th, 2023, where the internet nominated and voted for 30k startups around the world. The voting period remained open until December 31, 2023, and now we are here with the winner announcement after a comprehensive evaluation by the HackerNoon team. Startups of the Year 2023 To see the top 3 startups in each respective city, visit and pick a city. hackernoon.com/startups You’ll get a little surprise 😉 Here are the Startups of the Year Regional Announcements: North America Europe Asia Africa South America Oceania Misc/Remote From all of us at HackerNoon, thank YOU for your excellent campaign perseverance 🥺 This year has been awesome! Startups of the Year 2023 is concluded with 623k+ total votes, up from 215k+ votes in the inaugural startup competition. We look forward to seeing you again in the next campaign (stay tuned 😉)! Without further ado, let us kick off the announcement by celebrating the cities with top votes per region for 2023 🎉 North America (11,697 votes): Winner: Bloomfilter Cleveland, Ohio (8,835 votes) - Winner: SerpApi Austin, Texas , California (5,564 votes) - Winner: Finch San Francisco (4,258 votes) - Winner: RELAYTO SoMa, California - (3339 votes) Winner: Windscribe Toronto, Canada, North America (3304 votes) - Winner: Vouched Seattle (2716 votes) - Winner: Seccuri Palo Alto, CA, North America (2671 votes) - Winner: Salsa Valley Miami, FL, North America (2,613 votes) - Winner: Majhi Group Manhattan, New York (2423 votes) - Winner: Flieber, Inc. New York City, NY, North America See the complete announcement for the . Startups of the Year, North America, here Europe (45,612 votes) - Winner: Algomo London, UK (2,344votes) - Winner: Data Lake Warsaw, Poland (1,955 votes) - Winner: Hoken Tech Bari, Italy (1716 votes) - Winner: Not8 Amsterdam, Netherlands (1657 votes) - Winner: KYVE Frankfurt Am Main, Germany (1566 votes) - Winner: BazaIT Kyiv, Ukraine (1496 votes) - Winner:  Unity Network Tallinn, Estonia (1,478 votes) - Winner: Volcaino Ghent, Belgium (1,288 votes) - Winner: Owl Me Dublin, Ireland (1198 votes) - Winner: Groover Paris, France See the complete announcement for the . Startups of the Year, Europe, here Oceania (3,350 votes) - Winner: Sharepass Melbourne (2,107 votes) - Winner: The Catfisher Project Perth, Australia (220 votes) - Winner: Gemini AI Solutions Auckland, New Zealand, Oceania (213 votes) - Winner: Nexustorage Wellington, New Zealand, Oceania (138 votes) - Winner: Coviu Sydney (124 votes) - Winner: Bopple Brisbane, Australia, Oceania (124 votes) - Winner: Regional CFO New South Wales, Australia, Oceania (106 votes) - Winner: Edit on the Spot Gold Coast, Australia, Oceania (93 votes) - Winner: Knowledge Satnav Canbera, Australia, Oceania , Australia, Oceania (48 votes) - Winner: Peptalk360 North Melbourne, Melbourne See the complete announcement for the . Startups of the Year, Oceania, here Asia (16,345 votes) - Winner: Serenity Shield Singapore (13,588 votes) - Winners: QUIXY Telangana, India (10,168 votes) - Winner: HealthySure Mumbai, India (3,865 votes) - Winner: InterviewBuddy Visakhapatnam, India (3,733 votes) - Winner: Omniflo Bengaluru, India (4589 votes) - Winner: MeNow Tel Aviv (3877 votes) - Winner: ForestVPN Dubai (2478 votes) - Winner: TechGraph Powai, Mumbai, India (1004 votes) - Winner: Reelo Ahmedabad, GJ, India (875 votes) - Winner: Debt Hunter Hong Kong, China See the complete announcement for the . Startups of the Year, Asia, here Africa (8,179 votes) - Winner: GO PLATFORM Algiers, Algeria (767 votes) - Winner: Tudu Accra, Ghana (415 votes) - Winner: Treford Lagos, Nigeria (265 votes) - Winner: Phalconwise Ogbomosho, Nigeria (157 votes) - Winner: AbuErdan Cairo (151 votes) - Winner: Eazipay Lekki (144 votes) - Winner: KaziConnect Dar es Salaam, Tanzania (101 votes) - Winner: faceATT Gauteng, South Africa (79 votes) - Winner: Dimagi Cape Town (71 votes) - Winner: Sabi Addis Ababa See the complete announcement for the . Startups of the Year, Africa, here South America (376 votes ) - Winner: UpGirl Santiago, Chile (217 votes) - Winner: Klever Sao Paulo, Brazil (191 votes) - Winner: Tributo Simple Buenos Aires, Argentina (127 votes) - Winner: Light-it Montevideo, Uruguay (87 votes) -Winner: Thousand Antioquia, Colombia (108 votes) - Winner: GetLavado Lima (101 votes) - Winner: Cogniflow Medellin (73 votes) - Winner: TPAGA Bogota (73 votes) - Winner: Reliv Quito (47 votes) - WInner: Quantex Cordoba See the complete announcement for the . Startups of the Year, South America, here Misc/Remote: Featured Startups (1500 votes) - Winner: CIC Chain Remote, Earth (1193 votes) - Winner: Reship Colony US Misc (146 Votes) - Winner: Partful UK, Misc (116 votes) - Winner: GoMining British Virgin Islands Misc (283 Votes) - Winner: tomorrowSpace Germany Misc (225 votes) - Winner: Dtravel Cayman Islands See the complete announcement for Misc/Remote startups . here Once again, HUGE CONGRATULATIONS and BIG PROPS to all of the top Startups of each region this year! The whole team at HackerNoon would like to thank you for your perseverance in the campaign, and we look forward to seeing you in the next campaign. About HackerNoon’s Startups Of The Year Startups of The Year is HackerNoon’s Flagship community-driven event celebrating startups that survived and thrived in 2023. 30,000 startups across 4200+ cities and six continents participated this year to be crowned the best startup in their city. Visit our page to learn more. FAQ Companies that blog more get 67% more leads - Jumpstart your company’s blogging journey by purchasing our . Business Blogging Package The 2023 Startups Of The Year is sponsored by: . . The winners get a free .Tech Domain, a HackerNoon NFT, and a Tech Company News Page. TECH Domains

Stand Up for the Champion Startups of the Year! 

বছরের চ্যাম্পিয়ন স্টার্টআপের জন্য দাঁড়ান!

Setzen Sie sich für die Champion-Startups des Jahres ein!

¡Defiende a las startups campeonas del año!

Défendez les startups championnes de l’année !

वर्ष के चैंपियन स्टार्टअप के लिए खड़े रहें!

今年のチャンピオンスタートアップのために立ち上がろう!

올해의 챔피언 스타트업을 위해 일어서세요!

Defenda as startups campeãs do ano!

Встаньте на защиту стартапов-чемпионов года!

Yılın Şampiyon Startup'ları için Ayağa Kalkın!

Hãy ủng hộ các công ty khởi nghiệp vô địch của năm!

为年度冠军初创企业挺身而出！

The fast and easy guide to the most popular Deep Learning framework in the world. Make sure to check out the other articles . here Overview In this installment we will be going over all the abstracted models that are currently available in TensorFlow and describe use cases for that particular model as well as simple sample code. Full sources of working examples are in the . TensorFlow In a Nutshell repo A recurrent neural network Recurrent Neural Networks Language Modeling, Machine translation, Word embedding, Text processing. Use Cases: Since the advent of Long Short Term Memory and Gated Recurrent Units, Recurrent Neural Networks have made leaps and bounds above other models in natural language processing. They can be fed vectors representing characters and be trained to generate new sentences based on the training set. The merit in this model is that it keeps the context of the sentence and derives meaning that “cat sat on the mat” means the cat is on the mat. Since the creation of TensorFlow writing these networks have become increasingly simpler. There are even hidden features covered by Denny Britz that make writing RNN’s even simpler heres a quick example. here import tensorflow as tfimport numpy as np X = np.random.randn(2, 10, 8) # Create input data X[1,6,:] = 0X_lengths = [10, 6] # The second example is of length 6 cell = tf.nn.rnn_cell.LSTMCell(num_units=64, state_is_tuple= )cell = tf.nn.rnn_cell.DropoutWrapper(cell=cell, output_keep_prob=0.5)cell = tf.nn.rnn_cell.MultiRNNCell(cells=[cell] * 4, state_is_tuple= ) True True outputs, last_states = tf.nn.dynamic_rnn(cell=cell,dtype=tf.float64,sequence_length=X_lengths,inputs=X) result = tf.contrib.learn.run_n({"outputs": outputs, "last_states": last_states},n=1,feed_dict= ) None Convolution Neural Network Convolution Neural Networks Image processing, Facial recognition, Computer Vision Use Cases: Convolution Neural Networks are unique because they’re created in mind that the input will be an image. CNNs perform a sliding window function to a matrix. The window is called a kernel and it slides across the image creating a convolved feature. from http://deeplearning.standford.edu/wiki/index.php/Feature_extraction_using_convolution Creating a convolved feature allows for edge detection which then allows for a to depict objects from pictures. network edge detection from GIMP manual The convolved feature to create this looks like this matrix below: Convolved feature from GIMP manual Here’s a sample of code to identify handwritten digits from the MNIST dataset. ### Convolutional networkdef max_pool_2x2(tensor_in):return tf.nn.max_pool(tensor_in, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME') def conv_model(X, y): reshape X to 4d tensor with 2nd and 3rd dimensions being image width and height final dimension being the number of color channels. X = tf.reshape(X, [-1, 28, 28, 1]) first conv layer will compute 32 features for each 5x5 patch with tf.variable_scope('conv_layer1'):h_conv1 = learn.ops.conv2d(X, n_filters=32, filter_shape=[5, 5],bias=True, activation=tf.nn.relu)h_pool1 = max_pool_2x2(h_conv1) second conv layer will compute 64 features for each 5x5 patch. with tf.variable_scope('conv_layer2'):h_conv2 = learn.ops.conv2d(h_pool1, n_filters=64, filter_shape=[5, 5],bias=True, activation=tf.nn.relu)h_pool2 = max_pool_2x2(h_conv2) # reshape tensor into a batch of vectors  
h\_pool2\_flat = tf.reshape(h\_pool2, \[-1, 7 \* 7 \* 64\]) densely connected layer with 1024 neurons. h_fc1 = learn.ops.dnn(h_pool2_flat, [1024], activation=tf.nn.relu, dropout=0.5)return learn.models.logistic_regression(h_fc1, y) Feed Forward Neural Networks Classification and Regression Use Cases: These networks consist of perceptrons in layers that take inputs that pass information on to the next layer. The last layer in the network produces the output. There is no connection between each node in a given layer. The layer that has no original input and no final output is called the hidden layer. The goal of this network is similar to other supervised neural networks using back propagation, to make inputs have the desired trained outputs. These are some of the simplest effective neural networks for classification and regression problems. We will show how easy it is to create a feed forward network to classify handwritten digits: def init_weights(shape):return tf.Variable(tf.random_normal(shape, stddev=0.01)) def model(X, w_h, w_o):h = tf.nn.sigmoid(tf.matmul(X, w_h)) # this is a basic mlp, think 2 stacked logistic regressionsreturn tf.matmul(h, w_o) # note that we dont take the softmax at the end because our cost fn does that for us mnist = input_data.read_data_sets("MNIST_data/", one_hot=True)trX, trY, teX, teY = mnist.train.images, mnist.train.labels, mnist.test.images, mnist.test.labels X = tf.placeholder("float", [None, 784])Y = tf.placeholder("float", [None, 10]) w_h = init_weights([784, 625]) # create symbolic variablesw_o = init_weights([625, 10]) py_x = model(X, w_h, w_o) cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(py_x, Y)) # compute coststrain_op = tf.train.GradientDescentOptimizer(0.05).minimize(cost) # construct an optimizerpredict_op = tf.argmax(py_x, 1) # Launch the graph in a sessionwith tf.Session() as sess:# you need to initialize all variablestf.initialize_all_variables().run() for i in range(100):for start, end in zip(range(0, len(trX), 128), range(128, len(trX)+1, 128)):sess.run(train_op, feed_dict={X: trX[start:end], Y: trY[start:end]})print(i, np.mean(np.argmax(teY, axis=1) ==sess.run(predict_op, feed_dict={X: teX, Y: teY}))) Linear Models Classification and Regression Use Cases: Linear models take X values and produce a line of best fit used for classification and regression of Y values. For example if you have a list of house sizes and their price in a neighborhood you can predict the price of house given the size using a linear model. One thing to note is that linear models can be used for multiple X features. For example in the housing example we can create a linear model given house sizes, how many rooms, how many bathrooms and price and predict price given a house with size, # of rooms, # of bathrooms. import numpy as npimport tensorflow as tf def weight_variable(shape):initial = tf.truncated_normal(shape, stddev=1)return tf.Variable(initial) # datasetxx = np.random.randint(0,1000,[1000,3])/1000.yy = xx[:,0] * 2 + xx[:,1] * 1.4 + xx[:,2] * 3 # modelx = tf.placeholder(tf.float32, shape=[None, 3])y_ = tf.placeholder(tf.float32, shape=[None])W1 = weight_variable([3, 1])y = tf.matmul(x, W1) # training and cost functioncost_function = tf.reduce_mean(tf.square(tf.squeeze(y) - y_))train_function = tf.train.AdamOptimizer(1e-2).minimize(cost_function) # create a sessionsess = tf.Session() # trainsess.run(tf.initialize_all_variables())for i in range(10000):sess.run(train_function, feed_dict={x:xx, y_:yy})if i % 1000 == 0:print(sess.run(cost_function, feed_dict={x:xx, y_:yy})) Support Vector Machines Currently only Binary Classification Use Cases: The general idea behind a SVM is that there is an optimal hyperplane for linearly separable patterns. For data that is not linearly separable we can use a kernel function to transform the original data into a new space. SVMs maximize the margin around separating the hyperplane. They work extremely well in high dimensional spaces and and are still effective if the dimensions are greater than the number of samples. def input_fn():return {'example_id': tf.constant(['1', '2', '3']),'price': tf.constant([[0.6], [0.8], [0.3]]),'sq_footage': tf.constant([[900.0], [700.0], [600.0]]),'country': tf.SparseTensor(values=['IT', 'US', 'GB'],indices=[[0, 0], [1, 3], [2, 1]],shape=[3, 5]),'weights': tf.constant([[3.0], [1.0], [1.0]])}, tf.constant([[1], [0], [1]]) price = tf.contrib.layers.real_valued_column('price')sq_footage_bucket = tf.contrib.layers.bucketized_column(tf.contrib.layers.real_valued_column('sq_footage'),boundaries=[650.0, 800.0])country = tf.contrib.layers.sparse_column_with_hash_bucket('country', hash_bucket_size=5)sq_footage_country = tf.contrib.layers.crossed_column([sq_footage_bucket, country], hash_bucket_size=10)svm_classifier = tf.contrib.learn.SVM(feature_columns=[price, sq_footage_bucket, country, sq_footage_country],example_id_column='example_id',weight_column_name='weights',l1_regularization=0.1,l2_regularization=1.0) svm_classifier.fit(input_fn=input_fn, steps=30)accuracy = svm_classifier.evaluate(input_fn=input_fn, steps=1)['accuracy'] Deep and Wide Models Recommendation systems, Classification and Regression Use Cases: Deep and Wide models were covered with greater detail in , so we won’t get too heavy here. A Wide and Deep Network combines a linear model with a feed forward neural net so that our predictions will have memorization and generalization. This type of model can be used for classification and regression problems. This allows for less feature engineering with relatively accurate predictions. Thus, getting the best of both worlds. Here’s a code snippet from . part two part two’s github def input_fn(df, train=False):"""Input builder function.""" Creates a dictionary mapping from each continuous feature column name (k) to the values of that column stored in a constant Tensor. continuous_cols = {k: tf.constant(df[k].values) for k in CONTINUOUS_COLUMNS} Creates a dictionary mapping from each categorical feature column name (k) to the values of that column stored in a tf.SparseTensor. categorical_cols = {k: tf.SparseTensor(indices=[[i, 0] for i in range(df[k].size)],values=df[k].values,shape=[df[k].size, 1])for k in CATEGORICAL_COLUMNS} Merges the two dictionaries into one. feature_cols = dict(continuous_cols)feature_cols.update(categorical_cols) Converts the label column into a constant Tensor. if train:label = tf.constant(df[SURVIVED_COLUMN].values)# Returns the feature columns and the label.return feature_cols, labelelse:return feature_cols m = build_estimator(model_dir)m.fit(input_fn=lambda: input_fn(df_train, True), steps=200)print m.predict(input_fn=lambda: input_fn(df_test))results = m.evaluate(input_fn=lambda: input_fn(df_train, True), steps=1)for key in sorted(results):print("%s: %s" % (key, results[key])) Random Forest Classification and Regression Use Cases: Random Forest model takes many different classification trees and each tree votes for that class. The forest chooses the classification having the most votes. Random Forests do not overfit, you can run as many treees as you want and it is relatively fast. Give it a try on the iris data with this snippet below: hparams = tf.contrib.tensor_forest.python.tensor_forest.ForestHParams(num_trees=3, max_nodes=1000, num_classes=3, num_features=4)classifier = tf.contrib.learn.TensorForestEstimator(hparams) iris = tf.contrib.learn.datasets.load_iris()data = iris.data.astype(np.float32)target = iris.target.astype(np.float32) monitors = [tf.contrib.learn.TensorForestLossMonitor(10, 10)]classifier.fit(x=data, y=target, steps=100, monitors=monitors)classifier.evaluate(x=data, y=target, steps=10) Bayesian Reinforcement Learning Classification and Regression Use Cases: In the contrib folder of TensorFlow there is a library called BayesFlow. BayesFlow has no documentation except for an example of the REINFORCE algorithm. This algorithm is proposed in a by Ronald Williams. paper ward ncrement = onnegative actor * ffset einforcement * haracteristic ligibility RE I N F O R C E This network trying to solve an immediate reinforcement task, adjusts the weights after getting the reinforcement value at each trial. At the end of each trial each weight is incremented by a learning rate factor multiplied by the reinforcement value minus the baseline multiplied by characteristic eligibility. Williams paper also discusses the use of back propagation to train the REINFORCE network. learning """Build the Split-Apply-Merge Model.Route each value of input [-1, -1, 1, 1] through one of thefunctions, plus_1, minus_1.  The decision for routing is made by4 Bernoulli R.V.s whose parameters are determined by a neural networkapplied to the input.  REINFORCE is used to update the NN parameters.Returns:The 3-tuple (route_selection, routing_loss, final_loss), where:- route_selection is an int 4-vector- routing_loss is a float 4-vector- final_loss is a float scalar."""inputs = tf.constant([[-1.0], [-1.0], [1.0], [1.0]])targets = tf.constant([[0.0], [0.0], [0.0], [0.0]])paths = [plus_1, minus_1]weights = tf.get_variable("w", [1, 2])bias = tf.get_variable("b", [1, 1])logits = tf.matmul(inputs, weights) + bias # REINFORCE forward steproute_selection = st.StochasticTensor(distributions.Categorical, logits=logits) Linear Chain Conditional Random Fields Sequential Data Use Cases: CRFs are conditional probability distributions that factoirze according to an undirected model. They predict a label for a single sample keeping context from the neighboring samples. CRFs are similar to Hidden Markov Models. CRFs are often used for image segmentation and object recognition, as well as shallow parsing, named entity recognition and gene finding. # Train for a fixed number of iterations.session.run(tf.initialize_all_variables())for i in range(1000):tf_unary_scores, tf_transition_params, _ = session.run([unary_scores, transition_params, train_op])if i % 100 == 0:correct_labels = 0total_labels = 0for tf_unary_scores_, y_, sequence_length_ in zip(tf_unary_scores, y, sequence_lengths):# Remove padding from the scores and tag sequence.tf_unary_scores_ = tf_unary_scores_[:sequence_length_]y_ = y_[:sequence_length_] # Compute the highest scoring sequence.  
    viterbi\_sequence, \_ = tf.contrib.crf.viterbi\_decode(  
        tf\_unary\_scores\_, tf\_transition\_params)  

    # Evaluate word-level accuracy.  
    correct\_labels += np.sum(np.equal(viterbi\_sequence, y\_))  
    total\_labels += sequence\_length\_  
  accuracy = 100.0 \* correct\_labels / float(total\_labels)  
  print("Accuracy: %.2f%%" % accuracy) Conclusion Ever since TensorFlow has been released the community surrounding the project has been adding more packages, examples and cases for using this amazing library. Even at the time of writing this article there are more models and sample code being written. It is amazing to see how much TensorFlow as grown in these past few months. The ease of use and diversity in the package are increasing overtime and don’t seem to be slowing down anytime soon. As always — Feel free to email me any questions or inquiries at camron@camron.xyz Originally posted at Camron.xyz

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