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4.8 (51 reviews)
( 732 Students )
15 Hours


Deep Learning with Keras

You will learn how to build Neural Networks and Image Classification Models using Keras & more.
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Deep Learning essentially means training an Artificial Neural Network (ANN) with a huge amount of data. In deep learning, the network learns by itself and thus requires humongous data for learning.

Keras is high-level neural networks API  that runs on top of TensorFlow an end to end open source machine learning platform.  Using Keras, easily define complex ANN architectures to experiment on your big data.

This course, Deep Learning with Keras, will get up to speed with both the theory and practice of using keras to implement deep neural networks.

Software engineers who are curious about data science and about the Deep Learning Buzz and data scientists who are familiar with Machine learning and want to develop a strong foundational knowledge of deep learning.


Advantages of Keras

Simplicity – Keras is very easy and simple. It is a user-friendly API with easy to learn and code feature. It is very simple to start with Deep Learning using Keras.

Backend Support - It runs off the top of TensorFlow, Theano, and Microsoft CNTK. These are some libraries that Keras use for backend support. 

Great community and Caliber Documentation - Keras has a large supportive community. It provides code on an open-platform. This community allows the researchers to publish their code and experimentation details for the public. This community never fails to respond to the queries of its users.


Architecture of Keras

Keras API can be divided into three main categories – Model, Layer, Core Modules.  Keras Model is composition of Keras Layers and represents ANN layers like input, hidden layer, output layers, convolution layer, pooling layer, etc.,

Keras model and layer access Keras modules for activation function, loss function, regularization function, etc., Using Keras model, Keras Layer, and Keras modules, any ANN algorithm (CNN, RNN, etc.,) can be represented in a simple and efficient manner.


This Deep Learning with Keras course by Uplatz is a complete and end-to-end course covering all topics of Deep Learning with Kerasin detail. You will also learn how to build Neural Networks and Image Classification Models using Keras.

Course/Topic - Deep Learning with Keras - all lectures

  • In this first session we will be learning about introductory topics of Deep Learning. We will see about what is Deep learning and what is artificial neural network. Furthermore, in the introduction to deep learning with Keras we will see the overview, features and benefits of Keras. Lastly we will learn about the Keras installation.

    • 37:38
  • In this video tutorial we will learn about Keras as a neural based library. In this you will see the stepwise installation of keras, starting by creating a virtual environment and then activating the virtual environment.

    • 53:30
  • This video will cover the topics under Keras such as Models, Layers and Modules. In Keras Models we will learn about sequential model and functional API. In Keras layers we will learn about Dense Layer, Dropout Layer, Convolution Layer and Pooling Layer.

    • 34:38
  • This video session is a sequel to Keras – Models, Layers and Modules. In this we will learn in depth about the sequential model and the different layers in Keras.

    • 39:52
  • In this session the tutor explains about the layers in sequential model and how the model looks like and its functions. We will see how to build a sequential model successfully.

    • 23:49
  • This video talks about some methods on how to access the models. Further we will see about model.layers, model.input and model.output and understanding summarizing the model.

    • 38:26
  • This session will explain the Keras layers in detail. The tutor talks about the basic concepts of each layer - Input layer, output layer and hidden layer. Further explanation is on Dense layer and its operation. Secondly we will learn about Dropout layers and lastly about Convolution layer.

    • 26:45
  • In this video we cover the last part of this topic that is Modules. We will learn about the Modules provided by Keras such as Backend Module and Initializers Module.

    • 36:56
  • This video course will explain about the Model Compilation, evaluation and Predictions in Keras. After watching this chapter, you will be able to understand the concepts of Loss, Optimizer, Metrics and Compiling the Module. We will also learn about Model Training, Model evaluation and Model Prediction.

    • 28:26
  • This video is a sequel to the above video. In this video we will learn about the Optimizer concept in depth. In this session the tutor talks about what is Optimizer and Stochastic gradient descent (SGD) and Adam Optimizer.

    • 36:58
  • This video is a sequel to the above video. In this video we will learn about the Loss concept in depth. Further we will see how the compilation of a model, its training and evaluation.

    • 25:07
  • This video talks about the Model Training and its functions and batch size. Here we will see how to create data and code for single input model. Lastly we see about Model Prediction and why are predictions important.

    • 59:02
  • This is a video tutorial on Life-Cycle for Neural Network Models in Keras. We will look at the 5 steps in the life cycle of neural network i.e. Define Network, compile Network, Fit Network, Evaluate Network and making Predictions.

    • 35:31
  • This video is a sequel to the above video on 5 steps of Life cycle of Neural Network. In this we continue with the step 3 i.e. Fit Network and so on.

    • 27:16
  • This tutorial explains in detail how to build our first Neural Network with Keras. Further we will learn about developing and evaluating deep learning models.

    • 32:35
  • In this session tutor defines the Kera Model using sequential model. In the end we will see evaluating Keras Models.

    • 39:46
  • In this session you will discover how to create your first deep learning neural network model in Python using Keras.

    • 44:30
  • In this tutorial we will learn about building Image Classification Model with Keras. We will start with understanding what is image recognition (classification). Further we will learn about convolution neural network (CNN) and its layers. And Lastly we will see the stepwise process for building image classification model.

    • 35:17
  • In this session we will learn Image classification model with examples to clear out the concepts in detail. The tutor talks about step 2 i.e. importing libraries and splitting dataset. Step 3 – Building the CNN. Step 4 – full connection. Step 5 – Compile the model.

    • 40:57
  • This video is a sequel to the building image classification model tutorial. This video shows the step 6 i.e. Data Augmentation. Step 7 – setting train and test directories. Step 8 – Training our network.

    • 42:50
  • This is the last part of building image classification model tutorial. In this session we will learn the concluding steps i.e. Make prediction (how to load an image with keras).

    • 37:46
Objectives Back to Top

You will learn about exciting applications of deep learning and why it is really rewarding to learn how to leverage deep learning skills.

You will learn about neural networks and how they learn and update their weights and biases.

You will learn about the vanishing gradient problem.

You will learn about building a regression model using the Keras library.

You will learn about building a classification model using the Keras library.

You will learn about supervised deep learning models, such as convolutional neural networks and recurrent neural networks, and how to build a convolutional neural network using the Keras library.

You will learn about unsupervised learning models such as auto encoders.

Syllabus Back to Top

Deep Learning with Keras - Course Syllabus


Introduction to Deep Learning & Keras

1) What is deep learning?

2) What is ANN?

3) Introduction to Keras
·       Overview of Keras
·       Features of Keras
·       Benefits of Keras

4) Keras Installation

Keras - Models, Layers and Modules

1) Keras Models
·       Sequential Model
·       Functional API

2) Keras Layers
·       Core Layers
·       Convolution Layers
·       Pooling Layers
·       Recurrent Layers

3) Modules

Keras - Model Compilation, Evaluation and Prediction

1) Loss

2) Optimizer

3) Metrics

4) Compile the model

5) Model Training

6) Model Evaluation

7) Model Prediction

Life-Cycle for Neural Network Models in Keras

1) Define Network

2) Compile Network

3) Fit Network

4) Evaluate Network

5) Make Predictions

Developing a Deep Learning model with Keras

Building our first neural network in keras

Certification Back to Top

This Deep Learning course with Keras certification training is developed by industry leaders and aligned with the latest best practices. You’ll master deep learning concepts and models using Keras and TensorFlow frameworks and implement deep learning algorithms, preparing you for a career as Deep Learning Engineer. Upon successful completion of the Deep Learning course with KERAS training, you will be awarded an industry-recognized course completion certificate from Uplatz which has lifelong validity.

This Course is intended for Individuals wanting to understand a deeper level of deep learning using Keras. The course provides you a comprehensive introduction to deep learning, you will also be trained on neural networks and optimization techniques. Earning Uplatz  Deep Learning with Keras Certification can help candidate differentiate in today's competitive job market, broaden their employment opportunities by displaying their advanced skills, and result in higher earning potential.

Career & Jobs Back to Top

The demand for AI skills is so high that companies must also turn to contractors and freelancers to fill the skills gaps in Machine Learning and other AI segments. According to a recent report from freelancer platform Upwork, AI and related fields were prominent with natural language processing the second fastest-growing skill, neural networks fifth, and machine learning sixteenth.

Job Titles:

Machine Learning Senior Engineer

Data Scientist

Computer Vision Engineer


Interview Questions Back to Top

Q.1Top of Form

Explain the examples of data processing in Keras.

Some of the examples include: Firstly, neural networks don't process raw data, like text files, encoded JPEG image files, or CSV files. They process vectorized & standardized representations. Secondly, text files need to be read into string tensors, then split into words. Finally, the words need to be indexed and turned into integer tensors. Thirdly, images need to be read and decoded into integer tensors, then converted to floating points and normalized to small values (usually between 0 and 1). Lastly, CSV data needs to be parsed, with numerical features converted to floating-point tensors and categorical features indexed and converted to integer tensors. Then each feature typically needs to be normalized to zero-mean and unit variance.

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Q.2Name the types of inputs in the Keras model.

Keras models accept three types of inputs: Firstly, NumPy arrays, just like Scikit-Learn and many other Python-based libraries. This is a good option if your data fits in memory. Secondly, TensorFlow Dataset objects. This is a high-performance option that is more suitable for datasets that do not fit in memory and that are streamed from a disk or from a distributed filesystem. Lastly, Python generators that yield batches of data (such as custom subclasses of the keras.utils.Sequence class).

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Q.3What is Long Short Term Memory (LSTM)? Explain its process.

LSTM’s have a Nature of Remembering information for long periods of time is their Default behavior. The LSTM had a three-step Process:

1. Forget Gate This gate Decides which information is to be omitted from the cell in that particular timestamp. It is decided by the sigmoid function. However, it looks at the previous state(ht-1) and the content input(Xt) and outputs a number between 0(omit this)and 1(keep this)for each number in the cell state Ct−1.

2. Update Gate/input gate Decides how much of this unit is added to the current state. In this, the Sigmoid function decides which values to let through 0,1. and tanh function gives weightage to the values which are passed deciding their level of importance ranging from-1 to 1.

3. Output Gate Decides which part of the current cell makes it to the output. In this, the Sigmoid function decides which values to let through 0,1. and tanh function gives weightage to the values which are passed deciding their level of importance ranging from-1 to 1 and multiplied with an output of Sigmoid.

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Q.4Explain the term regularization.

Regularization is a method that makes slight modifications to the learning algorithm such that the model generalizes better. This in turn improves the model’s performance on the unseen data as well.

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Q.5Name some of the regularization techniques.

The techniques are as follows:

1. L2 and L1 Regularization

2. Dropout

3. Early Stopping

4. Data Augmentation

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Q.6Explain the L2 and L1 Regularization techniques.

L2 and L1 are the most common types of regularization. Regularization works on the premise that smaller weights lead to simpler models which result helps in avoiding overfitting. So to obtain a smaller weight matrix, these techniques add a ‘regularization term’ along with the loss to obtain the cost function. Here, Cost function = Loss + Regularization term However, the difference between L1 and L2 regularization techniques lies in the nature of this regularization term. In general, the addition of this regularization term causes the values of the weight matrices to reduce, leading to simpler models.

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Q.7What do you understand about Dropout and early stopping techniques?

Dropout means that during the training, randomly selected neurons are turned off or ‘dropped’ out. It means that they are temporarily obstructed from influencing or activating the downward neuron in a forward pass, and none of the weights updates is applied on the backward pass. Whereas Early Stopping is a kind of cross-validation strategy where one part of the training set is used as a validation set, and the performance of the model is gauged against this set. So if the performance on this validation set gets worse, the training on the model is immediately stopped. However, the main idea behind this technique is that while fitting a neural network on training data, consecutively, the model is evaluated on the unseen data or the validation set after each iteration. So if the performance on this validation set is decreasing or remaining the same for certain iterations, then the process of model training is stopped.

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Q.8What is Convolutional Neural Network?

A Convolutional Neural Network (ConvNet/CNN) is a Deep Learning algorithm that can take in an input image, assign importance to various aspects/objects in the image and be able to differentiate one from the other. The pre-processing required in a ConvNet is much lower as compared to other classification algorithms. While in primitive methods filters are hand-engineered, with enough training, ConvNets have the ability to learn these filters/characteristics.

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Q.9Explain the process of training a CNN.

The process for training a CNN for classifying images consists of the following steps −

1. Data Preparation In this step, we center-crop the images and resize them so that all images for training and testing would be of the same size. This is usually done by running a small Python script on the image data.

2. Model Definition In this step, we define a CNN architecture. The configuration is stored in .pb (protobuf) file.

3. Solver Definition In this, we define the solver configuration file. The solver does the model optimization.

4. Model Training In this, we use the built-in Caffe utility to train the model. The training may take a considerable amount of time and CPU usage. After the training is completed, Caffe stores the model in a file, which can, later on, be used on test data and final deployment for predictions.

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Q.10What do you know about Data preprocessing with Keras?

Once your data is in the form of string/int/float NumpPy arrays, or a Dataset object (or Python generator) that yields batches of string/int/float tensors, it is time to preprocess the data. This can mean: Firstly, Tokenization of string data, followed by token indexing. Secondly, Feature normalization. Thirdly, Rescaling the data to small values. In general, input values to a neural network should be close to zero -- typically we expect either data with zero-mean and unit-variance, or data in the [0, 1] range.

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Q.11What do you understand about callbacks?

Callbacks are an important feature of Keras that is configured in fit(). Callbacks are objects that get called by the model at different points during training like: Firstly, at the beginning and end of each batch Secondly, at the beginning and end of each epoch However, callbacks are a way to make model trainable entirely scriptable. This can be used for periodically saving your model.

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Q.12Explain the process of debugging your model with eager execution.

If you write custom training steps or custom layers, you will need to debug them. The debugging experience refers to an integral part of a framework and with Keras, the debugging workflow is designed with the user in mind. However, by default, Keras models are compiled to highly optimized computation graphs that deliver fast execution times. That means that the Python code you write is not the code you are actually executing. This introduces a layer of indirection that can make debugging hard. Further, it is better to perform debugging in a step-by-step manner. You want to be able to sprinkle your code with a print() statement to see what your data looks like after every operation, you want to be able to use pdb. You can achieve this by running your model eagerly. With eager execution, the Python code you write is the code that gets executed. Simply pass run_eagerly=True to compile():

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Q.13Explain the role of multiple GPUs in Keras.

Keras has built-in industry-strength support for multi-GPU training and distributed multi-worker training, via the tf.distribute API. However, if you have multiple GPUs on your machine, you can train your model on all of them by: Firstly, creating a tf.distribute.MirroredStrategy object. Secondly, creating and compiling your model inside the strategy's scope. Lastly, calling fit() and evaluate() on a dataset as usual.

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