• 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
• Numpy
• Pandas
• Tensorflow

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!

Who this course is for:

• Anyone interested in Deep Learning, Machine Learning and Artificial Intelligence
• Students who have at least high school knowledge in math and who want to start learning Machine Learning, Deep Learning, and Artificial Intelligence
• Any intermediate level people who know the basics of machine learning, including the classical algorithms like linear regression or logistic regression, but who want to learn more about it and explore all the different fields of Machine Learning, Deep Learning, Artificial Intelligence.
• Any people who are not that comfortable with coding but who are interested in Machine Learning, Deep Learning, Artificial Intelligence and want to apply it easily on datasets.
• Any students in college who want to start a career in Data Science
• Any data analysts who want to level up in Machine Learning, Deep Learning and Artificial Intelligence.
• Any people who are not satisfied with their job and who want to become a Data Scientist.
• Any people who want to create added value to their business by using powerful Machine Learning, Artificial Intelligence and Deep Learning tools. Any people who want to work in a Car company as a Data Scientist, Machine Learning, Deep Learning and Artificial Intelligence engineer.
• AI experts who want to expand on the field of applications
• Data Scientists who want to take their AI Skills to the next level
• Students in tech-related programs who want to pursue a career in Data Science, Machine Learning, or Artificial Intelligence
• Anyone passionate about Artificial Intelligence