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2. TensorFlow Agenda Introduction To TensorFlow Introduction To Deep Learning Fundamentals Of Neural Networks Fundamentals Of Deep Networks Convolutional Neural Networks (CNN) Recurrent Neural Networks (RNN) Restricted Boltzmann Machine(RBM) And Autoencoders

3. What is TensorFlow? ● TensorFlow is a multipurpose open source so2ware library for numerical computation using data flow graphs. It has been designed with deep learning in mind but it is applicable to a much wider range of problems. ● But what does it actually do? TensorFlow provides primitives for defining functions on tensors and automatically computing their derivatives.

4. But what’s a Tensor? Formally, tensors are multilinear maps from vector spaces to the real numbers ( vector space, and dual space) A scalar is a tensor ( ) A vector is a tensor ( ) A matrix is a tensor ( ) Common to have fixed basis, so a tensor can be represented as a multidimensional array of numbers.

5. Introduction To Deep Learning Deep Learning is machine learning technique that learns features and tasks directly from data. Data can be images, text, or sound.

6. What is Artificial Intelligence? “Every aspect of learning or any other features of intelligence can in principle be so precisely described that a machine can be made to simulate it. An attempt will be made to find how to make machine us language, form abstractions and concepts, solve kinds of problems now reserved for humans, and improve themselves.”

7. Why Artificial Intelligence

8. Limitations of Machine Learning There are a few key limitations of machine learning approaches that impact on their usefulness for certain tasks, as well as their ability to function in real-world environments. Machine learning algorithms function very well on tasks related to familiar data from a training set. Limitations tend to surface when the algorithm tries to incorporate new data. As these systems advance, they are quickly becoming better at categorizing familiar data and performing tasks such as image or speech recognition.

9. The Math behind Machine Learning: Linear Algebra ScalarsVectorsMatricesTensorsHyperplanes The Math Behind Machine Learning: Statistics ProbabilityConditional ProbabilitiesPosterior ProbabilityDistributionsSamples vs PopulationResampling MethodsSelection BiasLikelihood

10. Defining Neural Networks Neural networks are a set of algorithms, modeled loosely after the human brain, that are designed to recognize patterns. They interpret sensory data through a kind of machine perception, labeling or clustering raw input. The patterns they recognize are numerical, contained in vectors, into which all real-world data, be it images, sound, text or time series, must be translated. (OR) A neural network is a series of algorithms that attempts to identify underlying relationships in a set of data by using a process that mimics the way the human brain operates. Neural networks have the ability to adapt to changing input so the network produces the best possible result without the need to redesign the output criteria. The concept of neural networks is rapidly increasing in popularity in the area of developing  trading systems.

11. Deep Learning The important innovation in deep learning is a system that learns categories incrementally through it’s hidden layer architecture, defining low-level categories like letters before moving on to higher level categories such as words. In the example of image recognition this means identifying light/dark areas before categorising lines and then shapes to allow face recognition. Each neuron or node in the network represents one aspect of the whole and together they provide a full representation of the image. Each node or hidden layer is given a weight that represents the strength of its relationship with the output and as the model develops the weights are adjusted.

12. LAYER 1: Algorithm first learns to recognise pixels and then edges and shapes LAYER 2 Learns to identify more complex shapes and features like eyes and mouths LAYER 3 Learns which shapes and objects can be used to identify a human face

13. Convolution Neural Network

16. Recurrent Neural Network Model

17. Why Recurrent Neural Network

19. Long-Short Term Memory(LSTM)

20. Output Gate

21. Forget Gate

22. Input Gate

23. LSTM

24. Simplified LSTM

25. Restricted Boltzmann Machine

26. Restricted Boltzmann Machine

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