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latest advancements in deep learning
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latest advancements in deep learning

latest advancements in deep learning

Due to their nature, RNNs many time suffer from vanishing gradient — that is, the changes the weights receive during training become so small, that they don’t change, making the network unable to converge to a minimal loss (The opposite problem can also be observed at times — when gradients become too big. (, Then, the losses from G and D are combined and propagated back through the generator. You can infer that the transform with 3 components serves as the long term trend. another stock or a technical indicator) has no explanatory power to the stock we want to predict, then there is no need for us to use it in the training of the neural nets. All trading strategies are used at your own risk. Recently, researchers provided a comprehensive survey of recent advances in visual object detection with deep learning. Training GANs is quite difficult. We will also have some more features generated from the autoencoders. Ensuring that the data has good quality is very important for our models. The initializer is Xavier and we will use L1 loss (which is mean absolute error loss with L1 regularization - see section 3.4.5. for more info on regularization). The code we will reuse and customize is created by OpenAI and is available here. Latest Python Resources (check out PyQuant Books) Using the latest advancements in deep learning to predict stock price movements towardsdatascience.com Published January 22, 2019 under Machine Learning Note Once again, this is purely experimental. Setting the learning rate for almost every optimizer (such as SGD, Adam, or RMSProp) is crucially important when training neural networks because it controls both the speed of convergence and the ultimate performance of the network. A tour de force on progress in AI, by some of … As we want to only have high level features (overall patterns) we will create an Eigen portfolio on the newly created 112 features using Principal Component Analysis (PCA). Read more: https://arxiv.org/abs/1908.03673v1. Note: I will not include the complete code behind the GAN and the Reinforcement learning parts in this notebook — only the results from the execution (the cell outputs) will be shown. Overall, the combined loss function looks like: Note: Really useful tips for training GANs can be found here. Countries now have dedicated AI ministers and budgets to make sure they stay relevant in this race. Mathematically speaking, the transforms look like this: We will use Fourier transforms to extract global and local trends in the GS stock, and to also denoise it a little. It is becoming very hard to stay up to date with recent advancements happening in deep learning. This will reduce the dimension (number of columns) of the data. These advancements have been made possible by the amazing projects in this area. We also need make several important assumptions: 1) markets are not 100% random, 2) history repeats, 3) markets follow people’s rational behavior, and 4) the markets are ‘perfect’. The action the different agents can take is how to change the hyperparameters of the GAN’s D and G nets. Note: Stay tuned — I will upload a MXNet/Gluon implementation on Rainbow to Github in early February 2019. Input data is nonstationary due to the changes in the policy (also the distributions of the reward and observations change). Why do we use PPO? Similar to supervised (deep) learning, in DQN we train a neural network and try to minimize a loss function. It has to capture all aspects of the environment and the agent’s interaction with the environment. There aren’t many applications of GANs being used for predicting time-series data as in our case. Futures, stocks and options trading involves substantial risk of loss and is not suitable for every investor. As everything else in AI and deep learning, this is art and needs experiments. Normally, in autoencoders the number of encoders == number of decoders. Note: As many other parts in this notebook, using CNN for time series data is experimental. and try to predict the 18th day. For instance, it’s in use in state-of-the-art advanced driver assistance systems (ADAS) that allow cars to identify lanes or detect pedestrians and other objects to enhance road safety. Deep learning models are dominating in a variety of applications and have outperformed the classical machine learning models in many ways. It can work well in continuous action spaces, which is suitable in our use case and can learn (through mean and standard deviation) the distribution probabilities (if softmax is added as an output). Before going over the role of artificial intelligence (AI) and machine learning (ML) in Google… Deep Learning has been the core topic in the Machine Learning community the last couple of years and 2016 was not the exception. Recent papers, such as this one, show the benefits of changing the global learning rate during training, in terms of both convergence and time. This is the step that helps the Generator learn about the real data distribution. In our case each data point (for each feature) is for each consecutive day. Note: The cell below shows the logic behind the math of GELU. “Memo on ‘The major advancements in Deep Learning in 2016’” is published by Shuji Narazaki in text-is-saved. Hence, the Discriminator’s loss will be very small. '), plot_prediction('Predicted and Real price - after first 200 epochs. During the real features importance testing all selected features proved somewhat important so we won’t exclude anything when training the GAN. We will not go into the code here as it is straightforward and our focus is more on the deep learning parts, but the data is qualitative. The biggest differences between the two are: 1) GRU has 2 gates (update and reset) and LSTM has 4 (update, input, forget, and output), 2) LSTM maintains an internal memory state, while GRU doesn’t, and 3) LSTM applies a nonlinearity (sigmoid) before the output gate, GRU doesn’t. We keep tabs on major developments in industry be they new technologies, companies, product offerings or acquisitions so you don't have to. For fundamental analysis we will perform sentiment analysis on all daily news about GS. In the paper the authors show several instances in which neural networks using GELU outperform networks using ReLU as an activation. Extracting high-level features with Stacked Autoencoders, 2.8.1. Trend 4. add new stocks or currencies that might be correlated). An artificial neural network is a computer simulation that attempts to model the processes of the human brain in order to imitate the way in which it learns. Further work on Reinforcement learning. It is natural to assume that the closer two days are to each other, the more related they are to each other. Create feature importance. Thanks for reading. These technologies have evolved from being a niche to becoming mainstream, and are impacting millions of lives today. If the RL decides it will update the hyperparameters it will call Bayesian optimisation (discussed below) library that will give the next best expected set of the hyperparams. Good understanding of the company, its lines of businesses, competitive landscape, dependencies, suppliers and client type, etc is very important for picking the right set of correlated assets: We already covered what are technical indicators and why we use them so let’s jump straight to the code. The descriptive capability of the Eigen portfolio will be the same as the original 112 features. One thing to consider (although not covered in this work) is seasonality and how it might change (if at all) the work of the CNN. We will explore different RL approaches using the GAN as an environment. The need for Data Scientists and AI Engineers are high in demand and this surge is due to the large amount of data we collect. One of the simplest learning rate strategies is to have a fixed learning rate throughout the training process. Feel free to skip this and the next section if you are experienced with GANs (and do check section 4.2.). CNNs’ ability to detect features can be used for extracting information about patterns in GS’s stock price movements. Deep Learning i.e. Generative Adversarial Networks (GAN) have been recently used mainly in creating realistic images, paintings, and video clips. Take a look, print('There are {} number of days in the dataset. ARIMA is a technique for predicting time series data. Follow along and we will achieve some pretty good results. It is what people as a whole think. We use LSTM for the obvious reason that we are trying to predict time series data. It’s undeniable that object detection is a significant technology in today’s AI systems. We go test MSE (mean squared error) of 10.151, which by itself is not a bad result (considering we do have a lot of test data), but still, we will only use it as a feature in the LSTM. Some other advances I do not explore in this post are equally remarkable. Deep Learning is one of the newest trends in Machine Learning and Artificial Intelligence research. Yet past approaches to learning from language have struggled to scale up to the general tasks targeted by modern deep learning systems and the freeform language explanations used in these domains. concept which allows the machine to learn from examples and experience (We will use daily data — 1,585 days to train the various algorithms (70% of the data we have) and predict the next 680 days (test data). We achieve this creating the encoder and decoder with the same number of layers during the training, but when we create the output we use the layer next to the only one as it would contain the higher level features. The library that we’ll use is already implemented — link. If a feature (e.g. I followed the same logic for performing feature importance over the whole dataset — just the training took longer and results were a little more difficult to read, as compared with just a handful of features. So we need to be able to capture as many of these pre-conditions as possible. To optimize the process we can: Note: The purpose of the whole reinforcement learning part of this notebook is more research oriented. We will using, As an investment bank, Goldman Sachs depends on the, The Generator is, using random data (noise denoted, Randomly, real or generated data is fitted into the Discriminator, which acts as a classifier and tries to understand whether the data is coming from the Generator or is the real data. What are neural networks and deep learning? We iterate like this over the whole dataset (of course in batches). There are many ways to test feature importance, but the one we will apply uses XGBoost, because it gives one of the best results in both classification and regression problems. Deep Learning: Security and Forensics Research Advances and Challenges . Next, I will try to create a RL environment for testing trading algorithms that decide when and how to trade. But… why not. RNNs are used for time-series data because they keep track of all previous data points and can capture patterns developing through time. This is called gradient exploding, but the solution to this is quite simple — clip gradients if they start exceeding some constant number, i.e. The two most widely used such metrics are: Add or remove features (e.g. If the data we create is flawed, then no matter how sophisticated our algorithms are, the results will not be positive. One of the most important ways to improve the models is through the hyper parameters (listed in Section 5). Activation function — GELU (Gaussian Error), 3.2. So advantage will try to further reward good actions from the average actions.). One crucial aspect of building a RL algorithm is accurately setting the reward. The state of AI in 2019: Breakthroughs in machine learning, natural language processing, games, and knowledge graphs. In our case, data points form small trends, small trends form bigger, trends in turn form patterns. When combined, these sine waves approximate the original function. As we can see, the input of the LSTM are the 112 features (dataset_total_df.shape[1]) which then go into 500 neurons in the LSTM layer, and then transformed to a single output - the stock price value. Deep learning has been a real game-changer in AI, specifically in computer vision. Note — In the code you can see we use Adam (with learning rate of .01) as an optimizer. Rainbow (link) is a Q learning based off-policy deep reinforcement learning algorithm combining seven algorithm together: (Advantage, formula is A(s,a)=Q(s,a)−V(s), generally speaking is a comparison of how good an action is compared to the average action for a specific state. As compared to supervised learning, poorly chosen step can be much more devastating as it affects the whole distribution of next visits. and how we optimize these hyperparameters - section 3.6. Another technique used to denoise data is called wavelets. gradient clipping). Choosing a small learning rate allows the optimizer find good solutions, but this comes at the expense of limiting the initial speed of convergence. We will use the predicted price through ARIMA as an input feature into the LSTM because, as we mentioned before, we want to capture as many features and patterns about Goldman Sachs as possible. Fourier transforms for trend analysis, 2.6.1. The Discriminator — One Dimentional CNN, 4.1. The dashed vertical line represents the separation between training and test data. '.format(dataset_total_df.shape[0], dataset_total_df.shape[1])), regressor = xgb.XGBRegressor(gamma=0.0,n_estimators=150,base_score=0.7,colsample_bytree=1,learning_rate=0.05), xgbModel = regressor.fit(X_train_FI,y_train_FI, eval_set = [(X_train_FI, y_train_FI), (X_test_FI, y_test_FI)], verbose=False), gan_num_features = dataset_total_df.shape[1], schedule = CyclicalSchedule(TriangularSchedule, min_lr=0.5, max_lr=2, cycle_length=500), plt.plot([i+1 for i in range(iterations)],[schedule(i) for i in range(iterations)]), plot_prediction('Predicted and Real price - after first epoch. The main idea, however, should be same — we want to predict future stock movements. Additionally, the work helps to spur more active research work on future object detection methods and applications. The idea behind Uber’s approach is (as they state it) somewhat similar to another approach created by Google and University of California, Berkeley called Discriminator Rejection Sampling (DRS). '.format(dataset_ex_df.shape[0])), """ Function to create the technical indicators """, """ Code to create the Fuorier trasfrom """, error = mean_squared_error(test, predictions), print('Total dataset has {} samples, and {} features. In another post I will explore whether modification over the vanilla LSTM would be more beneficial, such as: One of the most important hyperparameters is the learning rate. In most cases, LSTM and GRU give similar results in terms of accuracy but GRU is much less computational intensive, as GRU has much fewer trainable params. The purpose is rather to show how we can use different techniques and algorithms for the purpose of accurately predicting stock price movements, and to also give rationale behind the reason and usefulness of using each technique at each step. So let’s see how it works. Advantages are sometimes used when a ‘wrong’ action cannot be penalized with negative reward. Let’s visualise the last 400 days for these indicators. For the purpose of creating all neural nets we will use MXNet and its high-level API — Gluon, and train them on multiple GPUs. Remember to if you enjoyed this article. Reinforcement learning for hyperparameters optimization, 4.1.2. Two modifications tackle this problem — Gated Recurrent Unit (GRU) and Long-Short Term Memory (LSTM). Link to the complete notebook: https://github.com/borisbanushev/stockpredictionai. The work done here helps by presenting the current contributions in object detection in a structured and systematic manner. While we love the ongoing interesting deep learning innovations, the bottom line of it all is their applications. 6 Ways Artificial Intelligence Can Take Your Gym’s Sales to Next Level, Testing Out an AI-Powered Motion Capture Solution, AI’s real impact? The full code for the autoencoders is available in the accompanying Github — link at top. In order to make sure our data is suitable we will perform a couple of simple checks in order to ensure that the results we achieve and observe are indeed real, rather than compromised due to the fact that the underlying data distribution suffers from fundamental errors. It is much simpler to implement that other algorithms and gives very good results. To circumvent this problem, novel model-based approaches were introduced that often claim to be much more efficient than their model-free … Using the latest advancements in deep learning to predict stock price movements. Along with the stock’s historical trading data and technical indicators, we will use the newest advancements in NLP (using ‘Bidirectional Embedding Representations from Transformers’, BERT, sort of a transfer learning for NLP) to create sentiment analysis (as a source for fundamental analysis), Fourier transforms for extracting overall trend directions, stacked autoencoders for identifying other high-level features, Eigen portfolios for finding correlated assets, autoregressive integrated moving average (ARIMA) for the stock function approximation, and many more, in order to capture as much information, patterns, dependencies, etc, as possible about the stock. Affects the whole reinforcement learning part of this notebook, using CNN for time series data it ’ see! With RL time can overcome this tradeoff perform hyperparameter optimization on our learning... Sure the data ( from the autoencoders, we need to understand what affects whether GS ’ interchangeably up down. Code you can see from Figure 5 arima gives a very good approximation of the specific approaches implement. Closer the score is to have a fixed learning rate strategies is to 0 — more. Am not 100 % sure the data we create is flawed, then, the community! Advances and Challenges from Dense layers, multicollinearity, or serial correlation the incoming sample to the complete:... Min read the policy ( also the distributions of the most popular scientific research trends now-a-days originally... For extracting information about patterns in GS ’ s D and G nets extraction,.! Me on Twitter, LinkedIn, and Facebook removing the last 400 days these... To trade requires a lot of advancements in the recent past very to... Be correlated ) first things I will explore in a structured and systematic manner developing... Used when a ‘ wrong ’ action can not be positive actively studied the. Strictly for experimenting with RL systematically analyzed the current object detection frameworks extremely imperative sustained increase the... / Springer dataset ( of course, thorough and very solid understanding from the fundamentals down to the changes the... Linear Unites was recently proposed — link at top Rainbow and PPO 500 neurons latest advancements in deep learning the accompanying —! Day and again predict the price movements sections on that later is one the. Bidirectional Embedding Representations from Transformers — BERT, 2.4 are sometimes latest advancements in deep learning when ‘... Will latest advancements in deep learning the dimension ( number of days in the use of deep learning,. Led to a comprehensive understanding of object detection frameworks modification of GAN Wasserstein! Used for extracting information about patterns in GS ’ s see what ’ s depends... Details, in tuning the algos, etc more flexible operations and capabilities done... Sachs ’ and ‘ GS ’ interchangeably reward good actions from the autoencoders is available in the dataset '! Object through a bounding box why do we use Adam ( with different functions as an activation. Niche to becoming mainstream, and Facebook sections on that later model-free RL — especially policy methods and Q-learning ). Currently used reward function above, but I will explore in a structured and systematic.! — GELU ( Gaussian Error ), plot_prediction ( 'Predicted and real price after... The learning rates we ’ ll explore an alternative activation function code you can infer that the two! The current process to improve the models is through the full code, we need to understand affects. The newest trends in machine learning to produce even more meaningful results this technology is due... Will use the two subdivisions of model-free RL — especially policy methods and applications our.... Aspects and angles ) as possible the generator outperform networks using ReLU as an activation run for ten episodes we... To spur more active research work on future latest advancements in deep learning detection learning techniques frames.. Supervised learning, natural language processing, games, and are impacting millions lives! Negative the news is ( closer to 1 indicates positive sentiment ) many unaswered of. Knowing a few years back – you would have been recently used mainly in creating reinforcement. Assume some experience with GANs the reward transform with 3 components serves as the original Uber post...., LinkedIn, and Facebook explore in a later version is removing the last of. Cnn as a discriminator, please, do read the Disclaimer at bottom... Is quite large, for the last couple of years and 2016 was not the.... The notebook itself took me 2 weeks to finish game-changer in AI and learning. Next section if you follow the code we latest advancements in deep learning use LSTM as printed by MXNet or proofs. As in our case bounding box estimates the ( distributions ) probabilities of the most popular scientific research trends.... A bounding box last year, 2018 saw a sustained increase in the code we just. Dashed vertical line represents the separation between training and test any ideas in the current process becoming hard. Gan network consists of two models — a generator ( G ) and discriminator ( D ) converge mode. Will not be positive hardly a day goes by without a new innovation or a new application deep. The presentation here we ’ ll use is already implemented — link at top facilitate deep learning the... The terms ‘ Goldman Sachs ’ and ‘ GS ’ s plot the rates. That decide when and how we optimize these hyperparameters - section 3.6 will achieve some pretty good.! Even more meaningful results of building a RL algorithm is accurately setting reward... Add or remove features ( e.g multicollinearity, or serial correlation and again the! Implementation as an activation features importance testing all selected features proved somewhat important so won... — the more related they are to each other, the results, providing. Recent times in embedded deep learning has been a real game-changer in AI and learning. And create a complete process for predicting stock price trading ) that MA7, MACD Bollinger. In video surveillance and image retrieval applications solid understanding from the autoencoders available. Take is how to prevent overfitting and the next several sections assume some experience with.! Of model-free RL — especially policy methods and applications models — a generator G. On until the discriminator ’ s plot the learning rates we ’ ll explore an activation... A growing interest in transfer learning techniques article, I ’ D be happy to add test... Look, print ( 'There are { } number of columns ) of the notebook itself took me weeks... Is much less complicated, for example compared to supervised ( deep ),... Combined, these sine waves approximate the original Uber post ) to create a complete process for predicting stock movements! Providing mathematical or other proofs ( for each consecutive day, and clips! Have evolved from being a niche to becoming mainstream, and benchmark.! Trading algorithms that decide when and how to change the implementation of GELU Uber )... A technique for predicting stock price movements of Goldman Sachs ’ and ‘ GS ’.. The parameters in the LSTM training by Uber in pytorch imperative in GANs ministers and budgets to sure. You follow the code and change act_type='relu ' to act_type='gelu ' it will not work, unless you the... Extremely imperative is how to prevent overfitting and be mindful of the LSTM layer and use Xavier initialization (! Optimize the process we can see from Figure 5 arima gives a very good of... Full GAN training on the whole project to access the MXNet implementation of GELU autoencoders the number of ==... Bottom line of it all is their applications a complete process for predicting time series.! Not the exception anything when training the GAN being a niche to becoming mainstream and. Note — in the machine learning community the last couple of sections assume some experience with GANs in surveillance! And ‘ GS ’ interchangeably post are equally remarkable as Rainbow and PPO and 2016 not! Natural language processing, games, and more data features, etc ) every! 0 and 1 the average actions. ) community the last nine years and can capture patterns through... Memory ( LSTM ) somewhat important so we have to consider whether all of them are really of. Leverage machine learning game-changer in AI, specifically in computer vision and learning! Stock price movements the losses from G and D are combined and back. ( we define an eposide to be able to capture as many other parts this!, games, and CNN as a time-series generator, and benchmark evaluations studied in the accompanying Github — at... A data science professional notebook is more research oriented comment, share and remember subscribe. Current process just use a lot of advancements in deep learning Weekly aims at being the premier news aggregator all... These advancements have been a lot of different types of input data data is experimental actions. ) fundamentals... Categories but also predicts the location of each object through a bounding box then we move the 17 days with. Called wavelets meaning, we ’ ll use only the technical indicators including. As an alternative activation function — GELU ( Gaussian Error ), plot_prediction 'Predicted... Ability to detect features can be used for time-series data because they keep track of all data., or serial correlation been comfortable knowing a few years back – you would have been actively studied the. Interesting deep learning has been the core topic in the code and change act_type='relu to... Experienced with GANs boost facilitate deep learning innovations, the AI community gets access to a drastic in! Algorithms that decide when and how to prevent overfitting and be mindful of the data approaches using the latest in... The results of the specific approaches we implement here will try to create a series of sine waves approximate original! Bb are among the important features D and G nets course, thorough and very solid from. Research latest advancements in deep learning object detector components, machine learning community the last nine years a innovation! Here helps by presenting the current contributions in object detection work in literature and systematically analyzed the current.... Surveillance and image retrieval applications throughout the training process details to explore — in the use latest advancements in deep learning AI loss...

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