Udemy - Deep Learning with Keras

Build a Neural Network and Image Classification Model with Keras

Introduction to Deep Learning and Neural Networks

Understand Deep Learning with Keras

Take a big step towards becoming a Deep Learning / Machine Learning eeer

Keras overview, features, benefits

Keras installation

Keras - Models, Layers and Modules

Keras Models - Sequential Model, Functional API

Keras Layers - Dense Layers, Dropout Layers, Convolution Layers, Pooling Layers

Keras Modules

Keras - Model Compilation, Evaluation and Prediction

Loss, Optimizer, Metrics, Compile the Model

Model Training, Model Evaluation, Model Prediction

Life-Cycle for Neural Network Models in Keras

Define Network, Compile Network, Fit Network, Evaluate Network, Make Predictions

Building your first Neural Network with Keras

Building a Multilayer Perceptron neural network

Building Image Classification Model with Keras

Convolutional Neural Network (CNN) & its layers

Enthusiasm and deteation to make your mark on the world!

Keras is an open-source library of neural network components written in Python. Keras is capable of running atop TensorFlow, Theano, PlaidML and others. The library was developed to be modular and user-friendly. Keras enables fast expentation through a high level, user-friendly, modular and extensible API. Keras can also be run on both CPU and GPU. Keras was developed and is maintained by Francois Chollet and is part of the TensorFlow core, which makes it TensorFlow preferred high-level API.

Comprised of a library of commonly used machine learning components including objectives, activation functions, and optimizers, Keras' open-source platform also offers support for recurrent and convolutional neural networks. Additionally, Keras offers mobile platform development for users intending to implement deep learning models on smartphones, both iOS and Android.

Keras is essentially an API designed for machine learning and deep learning eeers and follows best practices for reducing cognitive load. Keras offers consistent & simple APIs, minimizes the number of user actions required for common use cases, and provides clear & actionable error messages. It also supports extensive documentation and developer guides.

It is made user-friendly, extensible, and modular for facilitating faster expentation with deep neural networks. It not only supports Convolutional Networks and Recurrent Networks individually but also their combination

Why do we need Machine Learning libraries such as Keras

Machine learning uses a variety of math models and calculations to answer specific questions about data. Examples of machine learning in action include detecting spam emails, deteing certain objects using computer vision, recognizing speech, recommending products, and even predicting commodities values years in the future.

The calculations implicit in machine learning and deep learning are very complicated to set up to ensure correct output (answers). A variety of machine learning libraries have emerged to help navigate these complexities. With these options, new folks can start getting into data science easily. Some of the most popular machine learning libraries include:

TensorFlow

Keras

sciKit learn

Theano

Microsoft Cognitive Toolkit (CNTK)

Uplatz provides this comprehensive course on Deep Learning with Keras. This Keras course will help you implement deep learning in Python, preprocess your data, model, build, evaluate and optimize neural networks. The Keras training will teach you how to use Keras, a neural network API written in Python. This Keras course will show how the full implementation is done in code using Keras and Python. You will learn how to organize data for training, build and train an artificial neural network from scratch, build and fine-tune convolutional neural networks (CNNs), implement fine-tuning and transfer learning, deploy models using both front-end and back-end deployment techniques.

Deep Learning with Keras - Course Syllabus

1. Introduction to Deep Learning & Keras

What is deep learning

What is ANN

Introduction to Keras

a) Overview of Keras

b) Features of Keras

c) Benefits of Keras

Keras Installation

2. Keras - Models, Layers and Modules

Keras Models

a) Sequential Model

b) Functional API

Keras Layers

a) Dense Layers

b) Dropout Layers

c) Convolution Layers

d) Pooling Layers

Keras Modules

3. Keras - Model Compilation, Evaluation and Prediction

Loss

Optimizer

Metrics

Compile the model

Model Training

Model Evaluation

Model Prediction

4. Life-Cycle for Neural Network Models in Keras

Define Network

Compile Network

Fit Network

Evaluate Network

Make Predictions

5. Building our first Neural Network with Keras

(Building a Multilayer Perceptron neural network)

Load Data

Define Keras Model

Compile Keras Model

Fit Keras Model

Evaluate Keras Model

Make Predictions

6. Building Image Classification Model with Keras

What is Image Recognition (Classification)

Convolutional Neural Network (CNN) & its layers

Building Image Classification Model (step by step)

Key Features of Keras

Keras is an API designed for humans

Focus on user experience has always been a major part of Keras

Large adoption in the industry

Highly Flexible

It is a multi backend and supports multi-platform, which helps all the encoders come together for coding

Research community present for Keras works amazingly with the production community

Easy to grasp all concepts

It supports fast prototyping

It seamlessly runs on CPU as well as GPU

It provides the freedom to design any architecture, which then later is utilized as an API for the project

It is really very simple to get started with

Easy production of models actually makes Keras special

Easy to learn and use

Deep Learning / Machine Learning Eeers

Machine Learning Researchers - NLP, Python, Deep Learning

Data Scientists and Machine Learning Scientists

Newbies and Bners aspiring for a career in Machine Learning / Data Science / Deep Learning

Head of Eeering and Technical Leads

Anyone who wants to learn Deep Learning and Machine Learning

Computer Vision Researchers

AI Deep Learning Platform Leads

Senior ML and Deep Learning Scientists

Senior Data Consultants & Analytics Professionals

Product Managers

Artificial Intelligence Program Leads




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