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Keras: The Python Deep Learning library

You can now iterate on your training data in batches: Alternatively, you can feed batches to your model manually: Evaluate your performance in one line: Or generate predictions on new data: Building a question answering system, an image classification model, a Neural Turing Machine, or any other model is just as fast.

For a more in-depth tutorial about Keras, you can check out: In the examples folder of the repository, you will find more advanced models: question-answering with memory networks, text generation with stacked LSTMs, etc.

There are two ways to install Keras: If you are using a virtualenv, you may want to avoid using sudo: First, clone Keras using git: Then, cd to the Keras folder and run the install command: By default, Keras will use TensorFlow as its tensor manipulation library.

It is a reference to a literary image from ancient Greek and Latin literature, first found in the Odyssey, where dream spirits (Oneiroi, singular Oneiros) are divided between those who deceive men with false visions, who arrive to Earth through a gate of ivory, and those who announce a future that will come to pass, who arrive through a gate of horn.

Thoughts after taking the Deeplearning.ai courses

DL practitioners and ML engineers typically spend most days working at an abstract Keras or TensorFlow level.

In classic Ng style, the course is delivered through a carefully chosen curriculum, neatly timed videos and precisely positioned information nuggets.

Andrew picks up from where his classic ML course left off and introduces the idea of neural networks using a single neuron(logistic regression) and slowly adding complexity — more neurons and layers.

By the end of the 4 weeks(course 1), a student is introduced to all the core ideas required to build a dense neural network such as cost/loss functions, learning iteratively using gradient descent and vectorized parallel python(numpy) implementations.

Andrew patiently explains the requisite math and programming concepts in a carefully planned order and a well regulated pace suitable for learners who could be rusty in math/coding.

If your math is rusty, there is no need to worry — Andrew explains all the required calculus and provides derivatives at every occasion so that you can focus on building the network and concentrate on implementing your ideas in code.

Then he slowly explains more details about how the car works — why rotating the wheel makes the car turn, why pressing the brake pedal makes you slow down and stop etc.

He keeps getting deeper into the inner workings of the car and by the end of the course, you know how the internal combustion engine works, how the fuel tank is designed etc.

Once you are comfortable creating deep neural networks, it makes sense to take this new deeplearning.ai course specialization which fills up any gaps in your understanding of the underlying details and concepts.

Andrew stresses on the engineering aspects of deep learning and provides plenty of practical tips to save time and money — the third course in the DL specialization felt incredibly useful for my role as an architect leading engineering teams.

How to build your own AlphaZero AI using Python and Keras

Each squares is allocated a number from 0 to 41, as follows: The game.py file gives the logic behind moving from one game state to another, given a chosen action.

For example, given the empty board and action 38, the takeAction method return a new game state, with the starting player’s piece at the bottom of the centre column.

You can replace the game.py file with any game file that conforms to the same API and the algorithm will in principal, learn strategy through self play, based on the rules you have given it.

It loads the game rules and then iterates through the main loop of the algorithm, which consist of three stages: There are two agents involved in this loop, the best_player and the current_player.

To view individual convolutional filters and densely connected layers in the neural network, run the following inside the the run.ipynb notebook: This contains the Node, Edge and MCTS classes, that constitute a Monte Carlo Search Tree.

To play against your creation, run the following code (it’s also in the run.ipynb notebook) When you run the algorithm, all model and memory files are saved in the run folder, in the root directory.

Then, enter the run number, model version number and memory version number into the initialise.py file, corresponding to the location of the relevant files in the run_archive folder.

Equally from the logger.tourney file, you can see the probabilities attached to each move, during the evaluation phase: Training over a couple of days produces the following chart of loss against mini-batch iteration number: The top line is the error in the policy head (the cross entropy of the MCTS move probabilities, against the output from the neural network).

The bottom line is the error in the value head (the mean squared error between the actual game value and the neural network predict of the value).

To show how this results in stronger and stronger play, I ran a league between 17 players, ranging from the 1st iteration of the neural network, up to the 49th.

Observe the difference between the first version of the algorithm and say, the 30th version: 1st neural network version 30th neural network version This is a good strategy as many lines require the centre column — claiming this early ensures your opponent cannot take advantage of this.

Keras or PyTorch as your first deep learning framework

Whether you want to start applying it to your business, base your next side project on it, or simply gain marketable skills –

picking the right deep learning framework to learn is the essential first step towards reaching your goal.

[Edit: Recently, TensorFlow introduced Eager Execution, enabling the execution of any Python code and making the model training more intuitive for beginners (especially when used with tf.keras API).] While you may find some Theano tutorials, it is no longer in active development.

Keras is a higher-level framework wrapping commonly used deep learning layers and operations into neat, lego-sized building blocks, abstracting the deep learning complexities away from the precious eyes of a data scientist.

PyTorch offers a comparatively lower-level environment for experimentation, giving the user more freedom to write custom layers and look under the hood of numerical optimization tasks.

Keras is indeed more readable and concise, allowing you to build your first end-to-end deep learning models faster, while skipping the implementational details.

Working with PyTorch may offer you more food for thought regarding the core deep learning concepts, like backpropagation, and the rest of the training process.

For instance, in the Dstl Satellite Imagery Feature Detection Kaggle competition, the 3 best teams used Keras in their solutions, while our deepsense.ai team (4th place) used a combination of PyTorch and (to a lesser extend) Keras.

Unique mentions of deep learning frameworks in arxiv papers (full text) over time, based on 43K ML papers over last 6 years.

Andrej Karpathy (@karpathy) 10 marca 2018 While both frameworks have satisfactory documentation, PyTorch enjoys stronger community support –

their discussion board is a great place to visit to if you get stuck (you will get stuck) and the documentation or StackOverflow don’t provide you with the answers you need.

Anecdotally, we found well-annotated beginner level deep learning courses on a given network architecture easier to come across for Keras than for PyTorch, making the former somewhat more accessible for beginners.

The readability of code and the unparalleled ease of experimentation Keras offers may make it the more widely covered by deep learning enthusiasts, tutors and hardcore Kaggle winners.

For examples of great Keras resources and deep learning courses, see “Starting deep learning hands-on: image classification on CIFAR-10“ by Piotr Migdał and “Deep Learning with Python” –

For PyTorch resources, we recommend the official tutorials, which offer a slightly more challenging, comprehensive approach to learning the inner-workings of neural networks.

PyTorch saves models in Pickles, which are Python-based and not portable, whereas Keras takes advantages of a safer approach with JSON + H5 files (though saving with custom layers in Keras is generally more difficult).

Exporting PyTorch models is more taxing due to its Python code, and currently the widely recommended approach is to start by translating your PyTorch model to Caffe2 using ONNX.

play framework: to quickly build, train, and evaluate a model, without spending much time on mathematical implementation details.

Once you master the basics in one environment, you can apply them elsewhere and hit the ground running as you transition to new deep learning libraries.

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