A crash course in six episodes for software developers who want to become machine learning practitioners.

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Deep Learning tensorflow-without-a-phd
Overview

Featured code sample

tensorflow-planespotting
Code from the Google Cloud NEXT 2018 session "Tensorflow, deep learning and modern convnets, without a PhD". Other samples from the "Tensorflow without a PhD" series are in this repository too. 		Tensorflow, deep learning and modern convnets, without a PhD

Tensorflow and deep learning without a PhD series by @martin_gorner.

A crash course in six episodes for software developers who want to learn machine learning, with examples, theoretical concepts, and engineering tips, tricks and best practices to build and train the neural networks that solve your problems.

Tensorflow and deep learning without a PhD

The basics of building neural networks for software engineers. Neural weights and biases, activation functions, supervised learning and gradient descent. Tips and best practices for efficient training: learning rate decay, dropout regularisation and the intricacies of overfitting. Dense and convolutional neural networks. This session starts with low-level Tensorflow and also has a sample of high-level Tensorflow code using layers and Datasets. Code sample: MNIST handwritten digit recognition with 99% accuracy. Duration: 55 min

What is batch normalisation, how to use it appropriately and how to see if it is working or not. Code sample: MNIST handwritten digit recognition with 99.5% accuracy. Duration: 25 min

 The superpower: batch normalization
Tensorflow, deep learning and recurrent neural networks, without a PhD

RNN basics: the RNN cell as a state machine, training and unrolling (backpropagation through time). More complex RNN cells: LSTM and GRU cells. Application to language modeling and generation. Tensorflow APIs for RNNs. Code sample: RNN-generated Shakespeare play. Duration: 55 min

Convolutional neural network architectures for image processing. Convnet basics, convolution filters and how to stack them. Learnings from the Inception model: modules with parallel convolutions, 1x1 convolutions. A simple modern convnet architecture: Squeezenet. Convenets for detection: the YOLO (You Look Only Once) architecture. Full-scale model training and serving with Tensorflow's Estimator API on Google Cloud ML Engine and Cloud TPUs (Tensor Processing Units). Application: airplane detection in aerial imagery. Duration: 55 min

 Tensorflow, deep learning and modern convnets, without a PhD
Tensorflow, deep learning and modern RNN architectures, without a PhD

Advanced RNN architectures for natural language processing. Word embeddings, text classification, bidirectional models, sequence to sequence models for translation. Attention mechanisms. This session also explores Tensorflow's powerful seq2seq API. Applications: toxic comment detection and langauge translation. Co-author: Nithum Thain. Duration: 55 min

A neural network trained to play the game of Pong from just the pixels of the game. Uses reinforcement learning and policy gradients. The approach can be generalized to other problems involving a non-differentiable step that cannot be trained using traditional supervised learning techniques. A practical application: neural architecture search - neural networks designing neural networks. Co-author: Yu-Han Liu. Duration: 40 min

 Tensorflow and deep reinforcement learning, without a PhD



Quick access to all code samples:
tensorflow-mnist-tutorial
dense and convolutional neural network tutorial 		tensorflow-rnn-tutorial
recurrent neural network tutorial using temperature series 		tensorflow-rl-pong
"pong" with reinforcement learning
tensorflow-planespotting
airplane detection model 		conversationai: attention-tutorial
Toxic comment detection with RNNs and attention



*Disclaimer: This is not an official Google product but sample code provided for an educational purpose*
Comments
  • periph_rois

    periph_rois

    Can you please explain the code for periph_rois ,I mean what it is for? and I am not able to understand the significance of 'expand' variable in traineryolo/box_utils.py

    opened by abajaj945 5
  • 403: Forbidden downloading mnist_data

    403: Forbidden downloading mnist_data

    Hello, I've just cloned the repo and trying out the first mnist_1.0_softmax example that attempts to download from https://storage.googleapis.com/cvdf-datasets/mnist/ currently not accessible... other sources?

    opened by diramazioni 5
  • Correct INSTALL.txt instructions for Windows

    Correct INSTALL.txt instructions for Windows

    After following the instructions on a Windows 10 machine without GPU support, I encountered the issue described here: https://github.com/tensorflow/tensorflow/issues/17393 I also found the fix at that same location. Also, when running mnist_1.0_softmax.py in the Anaconda shell, I had to use "python" instead of "python3".

    opened by IverBand 5
  • 02_RNN_generator_temperatures_playground:

    02_RNN_generator_temperatures_playground:

    utils_display.picture_this_4() does not run (without any changes).

    I get the below error information running jupyter on Ubuntu desktop 18.04. It is the same problem with:

    utils_display.picture_this_6()

    By commenting out these the notebook runs without problems.

    And thanks by the way for the great videos and playgrounds, they really upgrade ones neural network understanding and learning!

    AttributeError Traceback (most recent call last) in () 7 visu_dates = evdates[START_DATE:END_DATE] 8 ----> 9 utils_display.picture_this_4(visu_temperatures, visu_dates)

    ~/git-public/tensorflow-without-a-phd/tensorflow-rnn-tutorial/tutorial/utils_display.py in picture_this_4(temperatures, dates) 73 if interpolated_sequence: 74 # light shade of red just for visibility ---> 75 plt.axvspan(startdate+np.timedelta64(-5, 'D'), stopdate+np.timedelta64(6, 'D'), facecolor='#FFCCCC', alpha=1) 76 # actual interpolated region 77 plt.axvspan(startdate+np.timedelta64(-1, 'D'), stopdate+np.timedelta64(1, 'D'), facecolor='#FF8888', alpha=1)

    /usr/local/lib/python3.6/dist-packages/matplotlib/pyplot.py in axvspan(xmin, xmax, ymin, ymax, hold, **kwargs) 2753 mplDeprecation) 2754 try: -> 2755 ret = ax.axvspan(xmin, xmax, ymin=ymin, ymax=ymax, **kwargs) 2756 finally: 2757 ax._hold = washold

    /usr/local/lib/python3.6/dist-packages/matplotlib/axes/_axes.py in axvspan(self, xmin, xmax, ymin, ymax, **kwargs) 904 905 # first we need to strip away the units --> 906 xmin, xmax = self.convert_xunits([xmin, xmax]) 907 ymin, ymax = self.convert_yunits([ymin, ymax]) 908

    /usr/local/lib/python3.6/dist-packages/matplotlib/artist.py in convert_xunits(self, x) 189 if ax is None or ax.xaxis is None: 190 return x --> 191 return ax.xaxis.convert_units(x) 192 193 def convert_yunits(self, y):

    /usr/local/lib/python3.6/dist-packages/matplotlib/axis.py in convert_units(self, x) 1524 return x 1525 -> 1526 ret = self.converter.convert(x, self.units, self) 1527 return ret 1528

    /usr/local/lib/python3.6/dist-packages/matplotlib/dates.py in convert(value, unit, axis) 1814 The unit and axis arguments are not used. 1815 """ -> 1816 return date2num(value) 1817 1818 @staticmethod

    /usr/local/lib/python3.6/dist-packages/matplotlib/dates.py in date2num(d) 450 if not d.size: 451 return d --> 452 return _to_ordinalf_np_vectorized(d) 453 454

    ~/.local/lib/python3.6/site-packages/numpy/lib/function_base.py in call(self, *args, **kwargs) 2753 vargs.extend([kwargs[_n] for _n in names]) 2754 -> 2755 return self._vectorize_call(func=func, args=vargs) 2756 2757 def _get_ufunc_and_otypes(self, func, args):

    ~/.local/lib/python3.6/site-packages/numpy/lib/function_base.py in _vectorize_call(self, func, args) 2823 res = func() 2824 else: -> 2825 ufunc, otypes = self._get_ufunc_and_otypes(func=func, args=args) 2826 2827 # Convert args to object arrays first

    ~/.local/lib/python3.6/site-packages/numpy/lib/function_base.py in _get_ufunc_and_otypes(self, func, args) 2783 2784 inputs = [arg.flat[0] for arg in args] -> 2785 outputs = func(*inputs) 2786 2787 # Performance note: profiling indicates that -- for simple

    /usr/local/lib/python3.6/dist-packages/matplotlib/dates.py in _to_ordinalf(dt) 253 tzi = UTC 254 --> 255 base = float(dt.toordinal()) 256 257 # If it's sufficiently datetime-like, it will have a date() method

    AttributeError: 'numpy.datetime64' object has no attribute 'toordinal'

    opened by SinanGabel 4
  • AttributeError: module 'tensorflow' has no attribute 'set_random_seed'

    AttributeError: module 'tensorflow' has no attribute 'set_random_seed'

    when I run mnist_1.0_softmax.py in annconda cmd with python 3.6 on win10, it reported errors, who can help me with it? many thanks. I type the followings: (base) F:\tensorflow-without-a-phd\tensorflow-mnist-tutorial>python mnist_1.0_softmax.py

    then it reported: Traceback (most recent call last): File "mnist_1.0_softmax.py", line 17, in <module> import tensorflowvisu File "F:\tensorflow-without-a-phd\tensorflow-mnist-tutorial\tensorflowvisu.py", line 27, in <module> tf.set_random_seed(0) AttributeError: module 'tensorflow' has no attribute 'set_random_seed'

    opened by xgxofdream 3
  • Update the rl-pong example for tf 1.10

    Update the rl-pong example for tf 1.10

    There was a compatibility issue from TensorFlow 1.8 to 1.9, where the variable reuse behavior was changed when building a model with Keras.

    Related issue: https://github.com/GoogleCloudPlatform/tensorflow-without-a-phd/issues/20 (Possibly related issue: https://github.com/GoogleCloudPlatform/tensorflow-without-a-phd/issues/11)

    opened by dizcology 3
  • Update repository address

    Update repository address

    The 'git clone' command refers to 'https://github.com/martin-gorner/tensorflow-mnist-tutorial.git' which is the old MNIST repository (and contains only README).

    opened by nioperas06 3
  • NameError: name 'raw_input' is not defined

    NameError: name 'raw_input' is not defined

    After the end of the 1st epoch:

    >>>>>>> epoch 1
>>> Rollout phase
Traceback (most recent call last):
  File "/usr/lib/python3.5/runpy.py", line 193, in _run_module_as_main
    "__main__", mod_spec)
  File "/usr/lib/python3.5/runpy.py", line 85, in _run_code
    exec(code, run_globals)
  File "/home/pawel/M/outrun/tensorflow-without-a-phd/tensorflow-rl-pong/trainer/task.py", line 292, in <module>
    main(args)
  File "/home/pawel/M/outrun/tensorflow-without-a-phd/tensorflow-rl-pong/trainer/task.py", line 204, in main
    _ = raw_input('episode done, press Enter to replay')
NameError: name 'raw_input' is not defined

    raw_input() is not supported in Python 3.

    As per Guido's explanation: https://docs.python.org/3/whatsnew/3.0.html https://www.python.org/dev/peps/pep-3111/

    raw_input() was renamed to input()

    opened by pococito 3
  • Fix tkinter crashing on MacOS

    Fix tkinter crashing on MacOS

    Fix tkinter crashing on MacOS by apply the following solution https://stackoverflow.com/questions/32019556/matplotlib-crashing-tkinter-application/34109240

    opened by lhc1990 2
  • doubt and update

    doubt and update

    Hi,

    why in the mnist_4.2_batchnorm_convolutional.py file the biases are defined as constants? They are defined like this: B5 = tf.Variable(tf.constant(0.1, tf.float32, [10])) instead of the usual tf.Variable(tf.ones([10])/10).

    And also, I refactored all your mnist training to tensorflow estimator format, you can find it here: https://github.com/ricoms/tensorflow-without-a-phd If you want I can create a pull request, although I deleted some of your files for visualization. For visualization, I am using a google Colaboratory ipynb that creates a tensorboard temporary url.

    Another point is: my mnist_4.2_batchnorm_convolutional.py did not reach 99,5% accuracy. It's still below the mnist_3.2 results:

    screenshot from 2018-10-10 08-33-22

    And I wonder if that is related to my first question, or my use of tf.layers.batch_normalization instead of your hardcoded function.

    I intend to use it for a workshop at my college if it's ok with you. I will reference your work, of course. @martin-gorner and team, thanks for all the great material and tutorial videos! I'm a fan.

    opened by ricoms 2
  • Would like to also try other Atari games like Spaceinvaders

    Would like to also try other Atari games like Spaceinvaders

    Thank you for your great video's, insights, codes, etc Much appreciated and inspiring. After Pong want to try other Atari games like Spaceinvaders, Pacman, etc and explore the boundaries of the policy gradient approach. Unfortunately got error when I simply replaced inner_env = gym.make('Pong-v0') to inner_env = gym.make('SpaceInvaders-v0'). Saw the code was quite resilient to capture any Atari game and the training also started but gave an error in "writing summary". Any pointers what might have caused it?

    `args: {'n_epoch': 6000, 'batch_size': 10000, 'output_dir': '/tmp/pong_output', 'job_dir': '/tmp/pong_output', 'restore': False, 'render': False, 'save_checkpoint_steps': 1, 'learning_rate': 0.005, 'decay': 0.99, 'gamma': 0.99, 'laziness': 0.01, 'hidden_dim': 200, 'max_to_keep': 6000}

    epoch 1 Rollout phase D:/OneDrive/Robotics/AINN/pong_google_cloud/tensorflow-without-a-phd/tensorflow-rl-spaceinvaders/trainer/task.py:217: RuntimeWarning: invalid value encountered in true_divide prwd /= np.std(prwd) Train phase rollout reward: -4.323713773987632 Writing summary Traceback (most recent call last):

    File "", line 1, in runfile('D:/OneDrive/Robotics/AINN/pong_google_cloud/tensorflow-without-a-phd/tensorflow-rl-spaceinvaders/trainer/task.py', wdir='D:/OneDrive/Robotics/AINN/pong_google_cloud/tensorflow-without-a-phd/tensorflow-rl-spaceinvaders/trainer')

    File "D:\anaconda\lib\site-packages\spyder_kernels\customize\spydercustomize.py", line 668, in runfile execfile(filename, namespace)

    File "D:\anaconda\lib\site-packages\spyder_kernels\customize\spydercustomize.py", line 108, in execfile exec(compile(f.read(), filename, 'exec'), namespace)

    File "D:/OneDrive/Robotics/AINN/pong_google_cloud/tensorflow-without-a-phd/tensorflow-rl-spaceinvaders/trainer/task.py", line 316, in main(args)

    File "D:/OneDrive/Robotics/AINN/pong_google_cloud/tensorflow-without-a-phd/tensorflow-rl-spaceinvaders/trainer/task.py", line 249, in main summary = sess.run(merged, feed_dict=feed_dict)

    File "D:\anaconda\lib\site-packages\tensorflow\python\client\session.py", line 877, in run run_metadata_ptr)

    File "D:\anaconda\lib\site-packages\tensorflow\python\client\session.py", line 1100, in _run feed_dict_tensor, options, run_metadata)

    File "D:\anaconda\lib\site-packages\tensorflow\python\client\session.py", line 1272, in _do_run run_metadata)

    File "D:\anaconda\lib\site-packages\tensorflow\python\client\session.py", line 1291, in _do_call raise type(e)(node_def, op, message)

    InvalidArgumentError: Nan in summary histogram for: summaries/train/model/dense_2/kernel [[Node: summaries/train/model/dense_2/kernel = HistogramSummary[T=DT_FLOAT, _device="/job:localhost/replica:0/task:0/device:CPU:0"](summaries/train/model/dense_2/kernel/tag, summaries/train/model/dense_2/kernel/ReadVariableOp)]]

    Caused by op 'summaries/train/model/dense_2/kernel', defined at: File "D:\anaconda\lib\runpy.py", line 193, in _run_module_as_main "main", mod_spec) File "D:\anaconda\lib\runpy.py", line 85, in run_code exec(code, run_globals) File "D:\anaconda\lib\site-packages\spyder_kernels\console_main.py", line 11, in start.main() File "D:\anaconda\lib\site-packages\spyder_kernels\console\start.py", line 296, in main kernel.start() File "D:\anaconda\lib\site-packages\ipykernel\kernelapp.py", line 486, in start self.io_loop.start() File "D:\anaconda\lib\site-packages\tornado\platform\asyncio.py", line 127, in start self.asyncio_loop.run_forever() File "D:\anaconda\lib\asyncio\base_events.py", line 422, in run_forever self._run_once() File "D:\anaconda\lib\asyncio\base_events.py", line 1434, in _run_once handle._run() File "D:\anaconda\lib\asyncio\events.py", line 145, in _run self._callback(*self._args) File "D:\anaconda\lib\site-packages\tornado\platform\asyncio.py", line 117, in _handle_events handler_func(fileobj, events) File "D:\anaconda\lib\site-packages\tornado\stack_context.py", line 276, in null_wrapper return fn(*args, **kwargs) File "D:\anaconda\lib\site-packages\zmq\eventloop\zmqstream.py", line 450, in _handle_events self._handle_recv() File "D:\anaconda\lib\site-packages\zmq\eventloop\zmqstream.py", line 480, in _handle_recv self._run_callback(callback, msg) File "D:\anaconda\lib\site-packages\zmq\eventloop\zmqstream.py", line 432, in _run_callback callback(*args, **kwargs) File "D:\anaconda\lib\site-packages\tornado\stack_context.py", line 276, in null_wrapper return fn(*args, **kwargs) File "D:\anaconda\lib\site-packages\ipykernel\kernelbase.py", line 283, in dispatcher return self.dispatch_shell(stream, msg) File "D:\anaconda\lib\site-packages\ipykernel\kernelbase.py", line 233, in dispatch_shell handler(stream, idents, msg) File "D:\anaconda\lib\site-packages\ipykernel\kernelbase.py", line 399, in execute_request user_expressions, allow_stdin) File "D:\anaconda\lib\site-packages\ipykernel\ipkernel.py", line 208, in do_execute res = shell.run_cell(code, store_history=store_history, silent=silent) File "D:\anaconda\lib\site-packages\ipykernel\zmqshell.py", line 537, in run_cell return super(ZMQInteractiveShell, self).run_cell(*args, **kwargs) File "D:\anaconda\lib\site-packages\IPython\core\interactiveshell.py", line 2662, in run_cell raw_cell, store_history, silent, shell_futures) File "D:\anaconda\lib\site-packages\IPython\core\interactiveshell.py", line 2785, in _run_cell interactivity=interactivity, compiler=compiler, result=result) File "D:\anaconda\lib\site-packages\IPython\core\interactiveshell.py", line 2907, in run_ast_nodes if self.run_code(code, result): File "D:\anaconda\lib\site-packages\IPython\core\interactiveshell.py", line 2961, in run_code exec(code_obj, self.user_global_ns, self.user_ns) File "", line 1, in runfile('D:/OneDrive/Robotics/AINN/pong_google_cloud/tensorflow-without-a-phd/tensorflow-rl-spaceinvaders/trainer/task.py', wdir='D:/OneDrive/Robotics/AINN/pong_google_cloud/tensorflow-without-a-phd/tensorflow-rl-spaceinvaders/trainer') File "D:\anaconda\lib\site-packages\spyder_kernels\customize\spydercustomize.py", line 668, in runfile execfile(filename, namespace) File "D:\anaconda\lib\site-packages\spyder_kernels\customize\spydercustomize.py", line 108, in execfile exec(compile(f.read(), filename, 'exec'), namespace) File "D:/OneDrive/Robotics/AINN/pong_google_cloud/tensorflow-without-a-phd/tensorflow-rl-spaceinvaders/trainer/task.py", line 316, in main(args) File "D:/OneDrive/Robotics/AINN/pong_google_cloud/tensorflow-without-a-phd/tensorflow-rl-spaceinvaders/trainer/task.py", line 148, in main tf.summary.histogram(var.op.name, var) File "D:\anaconda\lib\site-packages\tensorflow\python\summary\summary.py", line 187, in histogram tag=tag, values=values, name=scope) File "D:\anaconda\lib\site-packages\tensorflow\python\ops\gen_logging_ops.py", line 282, in histogram_summary "HistogramSummary", tag=tag, values=values, name=name) File "D:\anaconda\lib\site-packages\tensorflow\python\framework\op_def_library.py", line 787, in _apply_op_helper op_def=op_def) File "D:\anaconda\lib\site-packages\tensorflow\python\util\deprecation.py", line 454, in new_func return func(*args, **kwargs) File "D:\anaconda\lib\site-packages\tensorflow\python\framework\ops.py", line 3155, in create_op op_def=op_def) File "D:\anaconda\lib\site-packages\tensorflow\python\framework\ops.py", line 1717, in init self._traceback = tf_stack.extract_stack()

    InvalidArgumentError (see above for traceback): Nan in summary histogram for: summaries/train/model/dense_2/kernel [[Node: summaries/train/model/dense_2/kernel = HistogramSummary[T=DT_FLOAT, _device="/job:localhost/replica:0/task:0/device:CPU:0"](summaries/train/model/dense_2/kernel/tag, summaries/train/model/dense_2/kernel/ReadVariableOp)]]`

    It is running on Anaconda on a Windows machine. Thank you.

    opened by Charelvanhoof 2
  • UI Authentication error

    UI Authentication error

    Authorization Error Error 400: redirect_uri_mismatch

    You can't sign in to this app because it doesn't comply with Google's OAuth 2.0 policy.

    If you're the app developer, register the JavaScript origin in the Google Cloud Console.

    Any guidance on how to do this?

    opened by crispindev 0
  • TPU-optimized pipeline

    TPU-optimized pipeline

    Hi! I compared Keras fit time for dataset and experimental_distribute_dataset from your great notebook using latest TF version. It turned out that distributed dataset adds no speedup. Are you sure that your distributed input pipeline is well optimized for TPU? Why don't you use other optimizations like these:

    def input_fn(batch_size):
    """> 2000 images/sec"""
    files = tf.data.Dataset.list_files(FLAGS.data_dir)

    def tftecord_dataset(filename):
        buffer_size = 8 * 1024 * 1024   # 8 MiB per file
        return tf.data.TFRecordDataset(filename, buffer_size=buffer_size)

    dataset = files.apply(tf.contrib.data.parallel_interleave(
        tftecord_dataset, cycle_length=32, sloppy=True))
    dataset = dataset.apply(tf.contrib.data.shuffle_and_repeat(10000, NUM_EPOCHS))
    dataset = dataset.apply(tf.contrib.data.map_and_batch(
        parser_fn, batch_size, num_parallel_calls=4))
    return dataset.prefetch(4)

if FLAGS.use_tpu:
    # When using TPU, wrap the optimizer with CrossShardOptimizer which
    # handles synchronizarion details between different TPU cores.
    optimizer = tpu_optimizer.CrossShardOptimizer(optimizer)
    opened by qo4on 0
  • --job-dir error

    --job-dir error

    Hi When I tried training in cloud using this link https://github.com/GoogleCloudPlatform/tensorflow-without-a-phd/tree/master/tensorflow-mnist-tutorial/mlengine

    I got error Error when submitting the job (gcloud.ml-engine.jobs.submit.training) argument --job-dir: Must be of form gs://bucket/object

    I specified the bucket storage here but still got an error. gcloud ml-engine jobs submit training jobXXX --job-dir gs:///jobs/jobXXX --project --config config.yaml --module-name trainer.task --package-path trainer --runtime-version 1.4

    Kindly help..thanks

    opened by jocelynbaduria 0
  • AttributeError: module 'tensorflow' has no attribute 'set_random_seed'

    AttributeError: module 'tensorflow' has no attribute 'set_random_seed'

    Hi Martin,

    I have the following issue while compiling the code. please let me know how to resolve this.

    when I run the python command

    python mnist_1.0_softmax.py I'm getting this error AttributeError: module 'tensorflow' has no attribute 'set_random_seed'

    opened by ManishEnishetty 2
  • Fixing decay call at plot_learning_rate method

    Fixing decay call at plot_learning_rate method

    Method plot_learning_rate was not using the lr_func parameter and was using the global definition of lr_decay instead. This PR fixes it by using the correct method parameter.

    opened by gerson23 0
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Vowpal Wabbit is a machine learning system which pushes the frontier of machine learning with techniques such as online, hashing, allreduce, reductions, learning2search, active, and interactive learning.

This is the Vowpal Wabbit fast online learning code. Why Vowpal Wabbit? Vowpal Wabbit is a machine learning system which pushes the frontier of machin

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In the case of your data having only 1 channel while want to use timm models

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null 2 Nov 26, 2021
Compute execution plan: A DAG representation of work that you want to get done. Individual nodes of the DAG could be simple python or shell tasks or complex deeply nested parallel branches or embedded DAGs themselves.

Hello from magnus Magnus provides four capabilities for data teams: Compute execution plan: A DAG representation of work that you want to get done. In

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A very simple tool for situations where optimization with onnx-simplifier would exceed the Protocol Buffers upper file size limit of 2GB, or simply to separate onnx files to any size you want.

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Reinforcement-learning - Repository of the class assignment questions for the course on reinforcement learning

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A Python Automated Machine Learning tool that optimizes machine learning pipelines using genetic programming.

Master status: Development status: Package information: TPOT stands for Tree-based Pipeline Optimization Tool. Consider TPOT your Data Science Assista

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Scripts of Machine Learning Algorithms from Scratch. Implementations of machine learning models and algorithms using nothing but NumPy with a focus on accessibility. Aims to cover everything from basic to advance.

Algo-ScriptML Python implementations of some of the fundamental Machine Learning models and algorithms from scratch. The goal of this project is not t

Algo Phantoms 81 Nov 26, 2022