### Instructors

# Deep Learning course with TensorFlow in Bangalore

Deep Learning course with TensorFlow from Sniffer Search provide fundamentals of Deep learning using Neural Network ,Reinforcement Learning and using TensorFlow Framework.

**9 STUDENTS ENROLLED**

Deep Learning with TensorFlow course is best course to start for new comers to learn from basic to advance

The course is aimed to bring the best of Deep learning training in Bangalore .

After Completion of the course ,you will be able to solve:

- Complex problem using neural network
- Build artificial Intelligence (speech to text,NLP solution,Image recognition ,object detection )
- Deep reinforcement algorithm and Recommended System

Google brain team build and implemented Deep neural network inside TensorFlow ,which

you will be hands on after the course completion(Deep Learning using Google TensorFlow)

**Why Sniffer Search**: The Deep Learning with TensorFlow from Sniffer Search training is provided by professional from Top Deep Learning enthusiastic from top product companies in Bangalore and United States

During the training period we provide one to one mentorship so that you can learn at your own speed.

*Become a Deep Learning Engineer in Top Companies where you can utilize your skills on Deep learning using TensorFlow.*

### Course Curriculum

Introduction to Machine learning | |||

Supervised and Unsupervised learning algorithm | 00:00:00 | ||

Machine learning process | |||

Data Cleaning and Exploration | 00:00:00 | ||

Introduction Deep Learning | |||

Define Deep Learning Neural Networks Deep Learning Applications | 00:00:00 | ||

Deep learning type and indepth | |||

there are different type of deep learning | 00:00:00 | ||

Train a Deep neural Network | |||

Train your Network | 00:00:00 | ||

Hyper parameter tuning in Deep Neural Network | |||

Number of Hidden Layers and units | 00:00:00 | ||

Image Recognition and Detection using CNN model | |||

how to use Inception-v3. how to classify images into 1000 classes in Python or C++. We’ll also discuss how to extract higher level features from this model which may be reused for other vision tasks. | 00:00:00 | ||

Convolutional Neural Networks--We will build a relatively small convolutional neural network (CNN) for recognizing images. | |||

Highlights a canonical organization for network architecture, training and evaluation. | 00:00:00 | ||

Provides a template for constructing larger and more sophisticated models. | 00:00:00 | ||

Recurrent Neural Networks--we will show how to train a recurrent neural network on a challenging task of language modeling. The goal of the problem is to fit a probabilistic model which assigns probabilities to sentence | |||

Language Modelling | 00:00:00 | ||

Recurrent Neural Networks Introduction Take a look at this great article for an introduction to recurrent neural networks and LSTMs in particular. Language Modeling In this tutorial we will show how to train a recurrent neural network on a challenging task of language modeling. The goal of the problem is to fit a probabilistic model which assigns probabilities to sentences. It does so by predicting next words in a text given a history of previous words. For this purpose we will use the Penn Tree Bank (PTB) dataset, which is a popular benchmark for measuring the quality of these models, whilst being small and relatively fast to train. Language modeling is key to many interesting problems such as speech recognition, machine translation, or image captioning. It is also fun — take a look here. For the purpose of this tutorial, we will reproduce the results from Zaremba et al., 2014 (pdf), which achieves very good quality on the PTB dataset. Tutorial Files This tutorial references the following files from models/tutorials/rnn/ptb in the TensorFlow models repo: File Purpose ptb_word_lm.py The code to train a language model on the PTB dataset. reader.py The code to read the dataset. Download and Prepare the Data The data required for this tutorial is in the data/ directory of the PTB dataset from Tomas Mikolov’s webpage. The dataset is already preprocessed and contains overall 10000 different words, including the end-of-sentence marker and a special symbol ( |
00:00:00 | ||

Truncated Backpropagation By design, the output of a recurrent neural network (RNN) depends on arbitrarily distant inputs. Unfortunately, this makes backpropagation computation difficult. In order to make the learning process tractable, it is common practice to create an “unrolled” version of the network, which contains a fixed number (num_steps) of LSTM inputs and outputs. The model is then trained on this finite approximation of the RNN. This can be implemented by feeding inputs of length num_steps at a time and performing a backward pass after each such input block. | 00:00:00 | ||

Neural Machine Translation | |||

Sequence-to-sequence (seq2seq) models (Sutskever et al., 2014, Cho et al., 2014) have enjoyed great success in a variety of tasks such as machine translation, speech recognition, and text summarization. This tutorial gives readers a full understanding of seq2seq models and shows how to build a competitive seq2seq model from scratch. We focus on the task of Neural Machine Translation (NMT) which was the very first testbed for seq2seq models with wild success. The included code is lightweight, high-quality, production-ready, and incorporated with the latest research ideas. We achieve this goal by: Using the recent decoder / attention wrapper API, TensorFlow 1.2 data iterator Incorporating our strong expertise in building recurrent and seq2seq models Providing tips and tricks for building the very best NMT models and replicating Google’s NMT (GNMT) system. | 00:00:00 | ||

Recurrent Neural Networks for Drawing Classification--we'll show how to build an RNN-based recognizer for this problem. The model will use a combination of convolutional layers, LSTM layers, and a softmax output layer to classify the drawings | |||

Simple Audio Recognition--You will learn how to build a basic speech recognition network that recognizes ten different words. It's important to know that real speech and audio recognition systems are much more complex, but like MNIST for images, it should give you a basic understanding of the techniques involved. Once you've completed this tutorial, you'll have a model that tries to classify a one second audio clip as either silence, an unknown word, "yes", "no", "up", "down", "left", "right", "on", "off", "stop", or "go". You'll also be able to take this model and run it in an Android application. | |||

TensorFlow Linear Model--In this tutorial, we will use the tf.estimator API in TensorFlow to solve a binary classification problem: Given census data about a person such as age, education, marital status, and occupation (the features), we will try to predict whether or not the person earns more than 50,000 dollars a year (the target label). We will train a logistic regression model, and given an individual's information our model will output a number between 0 and 1, which can be interpreted as the probability that the individual has an annual income of over 50,000 dollars. | |||

TensorFlow Wide & Deep Learning : The course will cover how to use the tf.estimator API to jointly train a wide linear model and a deep feed-forward neural network. This approach combines the strengths of memorization and generalization. It's useful for generic large-scale regression and classification problems with sparse input features (e.g., categorical features with a large number of possible feature values). | |||

Vector Representations of Words--we look at the word2vec model by Mikolov et al. This model is used for learning vector representations of words, called "word embeddings". | |||

You will be learning substantive parts of building a word2vec model in TensorFlow. We start by giving the motivation for why we would want to represent words as vectors. We look at the intuition behind the model and how it is trained (with a splash of math for good measure). We also show a simple implementation of the model in TensorFlow. Finally, we look at ways to make the naive version scale better. | 00:00:00 | ||

Improving Linear Models Using Explicit Kernel Methods | |||

You will learn we demonstrate how combining (explicit) kernel methods with linear models can drastically increase the latters’ quality of predictions without significantly increasing training and inference times. Unlike dual kernel methods, explicit (primal) kernel methods scale well with the size of the training dataset both in terms of training/inference times and in terms of memory requirements | 00:00:00 |

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