Continuing with the previous story, in this post we are going to go over an example of text preparation of the sentiment analysis of a movie review dataset. The dataset is classified into positive reviews and negative review; all positive reviews are stored in pos directory, and all negative in neg directory. Each review is in a separate file. First, we need to split the data into training data and testing data prior to any data preparation. This means that any knowledge in the test set that could help us better prepare the data is unavailable during the preparation of the data and the training of the model.

With over 25 courses, this set of training covers almost every possible knowledge that could be required to get started with machine learning and put your skills to practical use. There are lectures based on various platforms such as Amazon Web Services, Google Cloud Platform and you can take your pick as per your convenience. Get a basic understanding of artificial intelligence and machine learning concepts with the essential training and take lessons such as NLP with Python to get hands-on with projects. By the end of the classes, you will be well equipped with the skills covered in the videos and ready to take on more challenging specializations.

This is the 1st article of series "Coding Deep Learning for Beginners". You will be able to find here links to all articles, agenda, and general information about an estimated release date of next articles on the bottom. They are also available in my open source portfolio -- MyRoadToAI, along with some mini-projects, presentations, tutorials and links. You can also read the article on Medium. If you read this article I assume you want to learn about one of the most promising technologies -- Deep Learning. Statement "AI is a new electricity" becomes more and more popular lately.

Despite significant advancements in genetics and modern imaging technology, for the vast majority of breast cancer patients, the diagnosis catches them by surprise. For some, it comes too late. Later diagnosis means aggressive treatments, anxiety and uncertain outcomes. Therefore, identifying patients at risk before the disease develops has been a central pillar to breast cancer research and effective early detection programs. A team from MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL) and Massachusetts General Hospital (MGH) has created a new deep learning model that can predict from a mammogram if a patient is likely to develop breast cancer in the future.

Today WekaIO announced Supermicro, a global leader in enterprise computing, storage, networking solutions and green computing technology, is an authorized OEM partner. The Supermicro BigTwin Server featuring the WekaIO File System, WekaFS, is the industry's first and only 2U multi-node system supporting the highest performance processor, memory, storage, and I/O at an incredible 30 percent better thermal capacity with the ability to lower energy consumption in the datacenter. This appliance is an integrated, preconfigured solution that delivers unmatched performance at scale. The BigTwin Server featuring WekaFS offering is a milestone in our relationship with Supermicro," said Barbara Murphy, Vice President of Marketing at WekaIO. "The solution is already in use by many customers with deep learning applications and exceeding expectations for performance and value. By offering this preconfigured solution with Supermicro, we'll be able to simplify the customers' acquisition experience."

Last fall, a bit of nerdy controversy arose around AI chip startup NovuMind when the company announced its first low-power chip for processing neural networks. The company claimed that its patented design could natively process 3-D tensor data far more efficiently than other designs that require pre-processing the 3D tensors into 2D matrices. NovuMind portrayed its advantages in low-resolution and high-resolution environments, while typical (Resnet 50) benchmarks target medium resolution algorithms common in datacenters (where NovuMind has a smaller advantage). Now, recent customer wins and trial deployments may provide the trump card in the debate. Let's take a look at the company, its first product, and some case studies that seem to bear out NovuMind's dramatic performance/watt claims.

Amazon on Wednesday announced Alexa Conversations, a deep learning-based approach for developers to create natural voice experiences on Alexa. The toolset, currently in preview, lets developers build natural skill dialogs with fewer lines of code and less training data, Amazon said. To use Conversations, developers provide API access to their skills' functionality, a sample of dialogs annotated with the prompts that Alexa will say to say to the customer, and the actions they expect the customer to take. Alexa Conversations' AI finishes the job, using the input data to generate dialog flows and variations. "It's way easier to build a complex voice experience with Alexa Conversations due to its underlying deep-learning-based dialog modeling,"said Rohit Prasad, Alexa vice president and head scientist, at Amazon's re:MARS conference in Las Vegas Wednesday.

Amazon on Wednesday announced Alexa Conversations, a deep learning-based approach for developers to create natural voice experiences on Alexa. The toolset, currently in preview, lets developers build natural skill dialogs with fewer lines of code and less training data, Amazon said. To use Conversations, developers provide API access to their skills' functionality, a sample of dialogs annotated with the prompts that Alexa will say to say to the customer, and the actions they expect the customer to take. Alexa Conversations' AI finishes the job, using the input data to generate dialog flows and variations. "It's way easier to build a complex voice experience with Alexa Conversations due to its underlying deep-learning-based dialog modeling,"said Rohit Prasad, Alexa vice president and head scientist, at Amazon's re:MARS conference in Las Vegas Wednesday.

Despite the widespread usage of machine learning throughout organizations, there are some key principles that are commonly missed. In particular: 1) There are at least four main families for supervised learning: logical modeling methods, linear combination methods, case-based reasoning methods, and iterative summarization methods. 2) For many application domains, almost all machine learning methods perform similarly (with some caveats). Deep learning methods, which are the leading technique for computer vision problems, do not maintain an edge over other methods for most problems (and there are reasons why). 3) Neural networks are hard to train and weird stuff often happens when you try to train them. 4) If you don't use an interpretable model, you can make bad mistakes. 5) Explanations can be misleading and you can't trust them. 6) You can pretty much always find an accurate-yet-interpretable model, even for deep neural networks. 7) Special properties such as decision making or robustness must be built in, they don't happen on their own. 8) Causal inference is different than prediction (correlation is not causation). 9) There is a method to the madness of deep neural architectures, but not always. 10) It is a myth that artificial intelligence can do anything.

The Transformer architecture is widely used in natural language processing. Despite its success, the design principle of the Transformer remains elusive. In this paper, we provide a novel perspective towards understanding the architecture: we show that the Transformer can be mathematically interpreted as a numerical Ordinary Differential Equation (ODE) solver for a convection-diffusion equation in a multi-particle dynamic system. In particular, how words in a sentence are abstracted into contexts by passing through the layers of the Transformer can be interpreted as approximating multiple particles' movement in the space using the Lie-Trotter splitting scheme and the Euler's method. Given this ODE's perspective, the rich literature of numerical analysis can be brought to guide us in designing effective structures beyond the Transformer. As an example, we propose to replace the Lie-Trotter splitting scheme by the Strang-Marchuk splitting scheme, a scheme that is more commonly used and with much lower local truncation errors. The Strang-Marchuk splitting scheme suggests that the self-attention and position-wise feed-forward network (FFN) sub-layers should not be treated equally. Instead, in each layer, two position-wise FFN sub-layers should be used, and the self-attention sub-layer is placed in between. This leads to a brand new architecture. Such an FFN-attention-FFN layer is "Macaron-like", and thus we call the network with this new architecture the Macaron Net. Through extensive experiments, we show that the Macaron Net is superior to the Transformer on both supervised and unsupervised learning tasks. The reproducible codes and pretrained models can be found at https://github.com/zhuohan123/macaron-net