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The Complete Deep Learning Course 2021 With 7+ Real Projects

 

The Complete Deep Learning Course 2021 With 7+ Real Projects Learn how to use Google's Deep Learning Framework - TensorFlow with Python! Solve problems with cutting edge techniques! 

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What you'll learn

  • Artificial Neural Networks (ANN)
  • Convolution Neural Network (CNN)
  • Recurrent Neural Network (RNN)
  • Generative adversarial network (GAN)
  • Deep Convolutional Generative adversarial network (DCGAN)
  • Natural Language Processing (NLP)
  • Image Processing
  • Sentiment Analysis
  • Autoencoder
  • Restricted Boltzman Machine
  • Deep Reinforcement Learning - Monte Carlo

Requirements

  • There will be no Prerequisites.
  • Basic knowledge of Python will be good.
  • But everything will be taught from the round up.

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Description

Welcome to the Complete Deep Learning Course 2021 With 7+ Real Projects

This course will guide you through how to use Google's TensorFlow framework to create artificial neural networks for deep learning! This course aims to give you an easy to understand guide to the complexities of Google's TensorFlow framework in a way that is easy to understand. Other courses and tutorials have tended to stay away from pure tensorflow and instead use abstractions that give the user less control. Here we present a course that finally serves as a complete guide to using the TensorFlow framework as intended, while showing you the latest techniques available in deep learning!

This course is designed to balance theory and practical implementation, with complete google colab and Jupiter notebook guides of code and easy to reference slides and notes. We also have plenty of exercises to test your new skills along the way!

This course covers a variety of topics, including

  • Deep Learning.
  • Google Colab
  • Anaconda
  • Jupiter Notebook
  • Activation Function.
  • Keras.
  • Pandas.
  • Seaborn.
  • Feature scaling.
  • Matplotlib.
  • scikit-learn
  • Sigmoid Function.
  • Tanh Function.
  • ReLU Function.
  • Leaky Relu Function.
  • Exponential Linear Unit Function.
  • Swish function.
  • Corpora.
  • NLTK.
  • TensorFlow 2.0
  • Tokenization.
  • Spacy.
  • PoS tagging.
  • NER.
  • Stemming and lemmatization.
  • Semantics and topic modelling.
  • Sentiment analysis techniques.
  • Lexicon-based methods.
  • Rule-based methods.
  • Statistical methods.
  • Machine learning methods.
  • Bernoulli RBMs.
  • Introduction to RBMs (Restricted Boltzman Machine).
  • Introduction to BMs (Boltzman Machine).
  • Learning data representations with RBMs.
  • Multilayer neural networks.
  • Latent vector.
  • Loading data.
  • Analysing data.
  • Training model.
  • Compiling model.
  • Visualizing data and model.
  • Implementing multilayer neural networks
  • Improving the model performance by removing outliers.
  • Building a Keras deep neural network model
  • Neural Network Basics.
  • TensorFlow Basics.
  • Artificial Neural Networks (ANN).
  • Densely Connected Networks.
  • Convolutional Neural Networks (CNN).
  • Recurrent Neural Networks (RNN).
  • AutoEncoders.
  • Generative Adversarial Network (GAN).
  • Deep Convolutional Generative adversarial network (DCGAN).
  • Natural Language Processing (NLP).
  • Image Processing.
  • Sentiment Analysis.
  • Restricted Boltzman Machine.
  • Reinforcement Learning.

There are many Deep Learning Frameworks out there, so why use TensorFlow?

TensorFlow is an open source software library for numerical computation using data flow graphs. Nodes in the graph represent mathematical operations, while the graph edges represent the multidimensional data arrays (tensors) communicated between them. The flexible architecture allows you to deploy computation to one or more CPUs or GPUs in a desktop, server, or mobile device with a single API. TensorFlow was originally developed by researchers and engineers working on the Google Brain Team within Google's Machine Intelligence research organization for the purposes of conducting machine learning and deep neural networks research, but the system is general enough to be applicable in a wide variety of other domains as well.

It is used by major companies all over the world, including Airbnb, Ebay, Dropbox, Snapchat, Twitter, Uber, SAP, Qualcomm, IBM, Intel, and of course, Google!

Moreover, the course is packed with practical exercises that are based on real-life examples. So not only will you learn the theory, but you will also get some hands-on practice building your own models. There are five big projects on healthcare problems and one small project to practice. These projects are listed below:

    Concrete Quality Prediction Using Deep Neural Networks.

  • CIFAR-10.
  • Classifying clothing images.
  • 20 newsgroups.
  • Handwritten Digit.
  • Denoising autoencoders (DAEs).
  • Movie Reviews Sentiment Analysis Using Recurrent Neural Networks.
  • Predicting Stock Price
  • Iris Flower.

Become a machine learning, and deep learning guru today! We'll see you inside the course!

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