Some Current ML & DL Speakers
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2020 SpeakersSample Talk, Workshop, and Training Sessions
Training | Deep Learning | Beginner
Over the past couple of years, PyTorch has been increasing in popularity in the Deep Learning community. What was initially a tool used by Deep Learning researchers has been making headway in industry settings. In this session, we will cover how to create Deep Neural Networks using the PyTorch framework on a variety of examples. The material will range from beginner – understanding what is going on “under the hood”, coding the layers of our networks, and implementing backpropagation – to more advanced material on RNNs, CNNs, LSTMs, & GANs. Attendees will leave with a better understanding of the PyTorch framework. Furthermore, a link to a clean documented GitHub repo with the solutions of the examples covered will be provided…more details
Training | Machine Learning | R-programming | All Levels
Temporal data requires special care to model as it violates several principles of standard machine learning models. R has long had top-of-the-line forecasting tools, though recently new ones have been developed which greatly ease working with time series data. We use the tsibble package for manipulating time series data, feasts for visualization , and fable for building forecasting models such as ETS and ARIMA…more details
Training | Machine Learning | Intermediate
We will present at least 10 examples and suggested solutions of cold-start problems (i.e., that move from a bad initial random guess to a good, perhaps optimal, solution), covering a variety of different algorithms and applications, focused primarily on unsupervised learning, but with some supervised learning examples also. We will also introduce related concepts and their importance, including the objective function, genetic algorithms, backpropagation, gradient descent, and meta-learning. Those concepts represent the true keys that unlock performance in a cold-start challenge. Those are the magic ingredients in most of the examples that we will present. At the end of the workshop, you should be empowered, enabled, and emboldened to tackle similar machine learning challenges problems in other domains. After all, data are data, math is math, and good experience is transferable!..more details
Full-Day Training | Deep Learning | Machine Learning | Beginner-Intermediate
We will use OpenAI gym to try our RL algorithms. OpenAI is a non profit organization that want committed to open source all their research on Artificial Intelligence. To foster innovation OpenAI created a virtual environment, OpenAi gym, where it’s easy to test Reinforcement Learning algorithms.
In particular, we will start with some popular techniques like Multi-Armed Bandit, going thought Markov Decision Processes and Dynamic Programming.
We then will also explore other RL frameworks and more complex concepts like Policy gradients methods and Deep Reinforcement learning, which recently changed the field of Reinforcement Learning. In particular, we will see Actor-Critic models and Proximal Policy Optimizations that allowed OpenAI to beat some of the best Dota players.
We will also provide the necessary Deep Learning concepts for the course...more details
Talk | Machine Learning | Intermediate
Not even a decade ago, machine learning was a profession for an elite few. Nowadays, we don’t have to be math or engineering wizards to implement state-of-the-art predictive models. Advances in computing hardware, and especially the utilization of GPUs for training deep neural networks, make it feasible to develop predictive models that achieve human-level performance in various natural language processing and image recognition challenges. The manifold software layers and APIs that are allowing us to utilize these hardware resources are becoming ever so convenient. In this talk, I will highlight the research and technology advances and trends of the last year(s), concerning GPU-accelerated machine learning and deep learning, and focusing on the most profound hardware and software paradigms that have enabled it…more details
Training | Machine Learning | Intermediate
How can an idea from an 18th-century Presbyterian minister be used to estimate the mass density function of galaxies across the Universe? How can a marketing segmentation algorithm protect astronauts traveling to Mars from certain death? How does a Formula 1 race from then 1950’s inspire one of the greatest data science use cases for the Internet of Things? How can a violation of the triangle inequality theorem in mathematics lead to a cure for cancer? This workshop will answer these questions, and more, by presenting several examples of one of the key aptitudes of successful data science practice, which is adaptability. In particular, I will present several well-known algorithms (including some that we would not even call “algorithms”) that may have been adopted for specific use cases or applied in specific business domains, and then I will show how each one can be adapted to a novel use case that may be less obvious, perhaps producing significantly surprising results in some other domain. Plus, I will suggest some new opportunities that may come from interesting combinations of data and algorithms. The point of these exercises is to demonstrate how data scientists can create even more value, beyond that which is expected, from our data assets and our algorithmic talents…more details
Training | Machine Learning | Open-source | Beginner-Intermediate
The rapid progress in machine learning (ML) and the massive increase in the availability and diversity of data has enabled novel approaches to quantitative investment. It has also increased the demand for the application of data science to develop both discretionary and algorithmic trading strategies.
In this workshop, we will cover popular use cases for ML in the investment industry, and how data science and ML fit into the workflow of developing a trading and investment strategy from the identification and combination of alpha factors to strategy backtesting and asset allocation.
The workshop uses Python and various standard data science and machine learning libraries like pandas, scikit-learn, gensim, spaCy as well as TensorFlow and Keras. The code examples will be presented using jupyter notebooks and are based on my book ‘Machine Learning for Algorithmic Trading’…more details
See all our talks and hands-on workshop and training sessions
See all sessionsSessions on Machine Learning & Deep Learning Track
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Workshop: Deciphering the Black Box: Latest Tools and Techniques for Interpretability
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Talk: Adversarial Attacks on Deep Neural Networks
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Training: Integrating Pandas with Scikit-Learn, an Exciting New Workflow
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Workshop: Machine Learning for Digital Identity
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Talk: Adding Context and Cognition to Modern NLP Techniques
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Training: Good, Fast, Cheap: How to do Data Science with Missing Data
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Workshop: Open Data Hub workshop on OpenShift
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Talk: Practical AI solutions within healthcare and biotechnology
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Training: Apache Spark for Fast Data Science (and Fast Python Integration!) at Scale
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Workshop: Reproducible Data Science Using Orbyter
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Talk: Combining millions of products into one marketplace using computer vision and natural language processing
