Machine Learning Quiz (134 Objective Questions) Start ML Quiz

Deep Learning Quiz (205 Objective Questions) Start DL Quiz

Deep Learning Free eBook Download

Saturday, 24 August 2019

eBook - Deep Learning Objective Type Questions and Answers

This book contains 205 objective type questions and answers covering various basic concepts of deep learning. It contains 19 chapters. Each chapter contains a short description of a concept and objective type questions from that concept. 

You can download this book from here.


Please download this book, study and distribute among your friends and colleagues. I would be more than happy if this book can increase your deep learning knowledge to some extent.

Assumption: This should not be your first book on deep learning as I have not covered deep learning concepts in detail, just given a short description to revise your concepts. So, I assume, you have some basic understanding of deep learning concepts before reading this book.

I am continuously upgrading this book and adding more and more objective type deep learning questions in this book as well as in deep learning quiz. So, stay tuned! You can visit this page once in a week and download the updated copy of this book.

Your opinion about this book matters a lot to me. Please post your comments and suggestions regarding this eBook on this blog post.

Table of Contents














Deep Learning Concepts

This book contains objective questions on following Deep Learning concepts:

1. Perceptrons: Working of a Perceptron, multi-layer Perceptron, advantages and limitations of Perceptrons, implementing logic gates like AND, OR and XOR with Perceptrons etc.

2. Neural Networks: Layers in a neural network, types of neural networks, deep and shallow neural networks, forward and backward propagation in a neural network etc.

3. Weights and Bias: Importance of weights and biases, things to keep in mind while initializing weights and biases, Xavier Weight Initialization technique etc.

4. Activation Functions: Importance of activation functions, Squashing functions, Step (Threshold), Logistic (Sigmoid), Hyperbolic Tangent (Tanh), ReLU (Rectified Linear Unit), Dying and Leaky ReLU, Softmax etc.

5. Batches: Epochs, Batches and Iterations, Batch Normalization etc.

6. Gradient Descent: Batch, Stochastic and Mini Batch Gradient Descent, SGD variants like Momentum, Nesterov Momentum, AdaGrad, AdaDelta, RMSprop and Adam, Local and Global Minima, Vanishing and Exploding Gradients, Learning Rate etc.

7. Loss Functions: categorical_crossentropy, sparse_categorical_crossentropy etc.

8. CNN: Convolutional Neural Network, Filters (Kernels), Stride, Padding, Zero Padding and Valid Padding, Pooling, Max Pooling, Min Pooling, Average Pooling and Sum Pooling, Hyperparameters in CNN, Capsule Neural Network (CapsNets), ConvNets vs CapsNets, Computer vision etc.

9. RNN: Recurrent Neural Network, Feedback loop, Types of RNN like One to One, One to Many, Many to One and Many to Many, Bidirectional RNN, Advantages and disadvantages of RNN, Applications of RNN, Differences between CNN and RNN etc.

10. LSTM: Long Short Term Memory, Gated cells like Forget gate, Input gate and Output gate, Applications of LSTM etc.

11. Regularization: Overfitting and underfitting in a neural network, L1 and L2 Regularization, Dropout, Data Augmentation, Early Stopping etc.

12. Fine-tuning: Transfer Learning, Fine-tuning a model, Steps to fine-tune a model, Advantages of fine-tuning etc.

13. Autoencoders: Components of an autoencoder like encoder, decoder and bottleneck, Latent space representation and reconstruction loss, Types of Autoencoders like Undercomplete autoencoder, Sparse autoencoder, Denoising autoencoder, Convolutional autoencoder, Contractive autoencoders and Deep autoencoders, Hyperparameters in an autoencoder, Applications of an Autoencoder, Autoencoders vs PCA, RBM (Restricted Boltzman Machine) etc.

14. NLP (Natural Language Processing): Tokenization, Stemming, Lemmatization and Vectorization (Count vectorization, N-grams vectorization, Term Frequency - Inverse Document Frequency (TF-IDF)), Document-term matrix, NLTK( Natural Language Toolkit) etc.

15. Frameworks: TesnorFlow, Keras, PyTorch, Theano, CNTK, Caffe, MXNet, DL4J etc.

A note to readers

This book is just a short summary of my online contents on:

1. The Professionals Point
2. Online ML Quiz

Contents of this book are available on my this blog (The Professionals Point) and objective type questions are available in the form of quiz on my website (Online ML Quiz). 

Disclaimer: Contents of this book are the sole property of www.onlinemlquiz.com. Questions should not be reproduced in any form without prior permission and attribution.

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About the Author

I have more than 10 years of experience in IT industry. Linkedin Profile

I am currently messing up with neural networks in deep learning. I am learning Python, TensorFlow and Keras.

Author: I am an author of a book on deep learning.

Quiz: I run an online quiz on machine learning and deep learning.