Deep Learning with Tensorflow

Course Description

TensorFlow is a leading opensource framework for machine learning that is developed and maintained by Google. TensorFlow is the most popular deep learning library based on Python that provides different types of functionality for implementing deep learning models. TF high-level APIs are based on the Keras API standard for defining and training neural networks.

tf.Keras is popular because it provides a clean and simple interface, also allows much more computationally intensive deep learning models to be defined, fit, and evaluated in just a few lines of code. Using the TensorFlow tool beginners and experts can easily create machine learning models. 

DeepLearning with TensorFlow online training provided by skilled professionals from end to end concepts. TensorFlow deep learning tutorial covers all basic and advanced topics such as regression, classification, create, develop MLP, CNN, RNN models, inspect, diagnose, overfitting reduction, training acceleration, autoencoders. By learning the Tensor flow concepts one can easily apply in deep learning technology applications. 

Course Fee : 264 USD

Deep Learning with Tensorflow Learners from Hachion: 50
Course Schedule

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USD 264

Training Fee: USD 293.3 10% Discount

  • Live interactive online training
  • Daily Assignments and Lab exercises
  • Resume and certification guidance
  • Mock interview and live project assistance
  • Resume marketing and job assistance
Mentoring mode training
  • Live interactive online training
  • Daily Assignments and Lab exercises
  • Resume and certification guidance
  • Mock interview and live project assistance
  • Resume marketing and job assistance
Live online training and internship
  • Live interactive online training
  • Daily Assignments and Lab exercises
  • Resume and certification guidance
  • Mock interview and live project assistance
  • Resume marketing and job assistance

Course Content

  • Introduction to Deep Learning

  • Introduction to Numpy

  • Introduction to Tensorflow and Keras

  • Solution of Equations, row and column Interpretation

  • Vector Space Properties

  • Partial Derivative of Polynomial and Two conditions for Local Minima

  • Physical Interpretation of gradient (Direction of Maximum Change)

  • Matrix-Vector Multiplication

  • EVD and interpretation of Eighen Vectors

  • Linear Independence and Rank of Matrix

  • Orthonormal Matrices, Projection Matrices, Vandemonde Matrix, Markov Matrix, Symmetric, Block Diagonal

  • Intuition behind Linear Regression, classification

  • Grid Search

  • Gradient Descent

  • Training Pipeline

  • Metrics ROC Curve, Precision Recall Curve

  • Calculating Entropy

  • Evolution of Perceptrons, Hebbs Principle, Cat Experiment

  • Single layer NN

  • Tensorflow Code

  • Multilayer NN

  • Backpropagation, Dynamic Programming

  • Mathematical Take on NN

  • Function Approximator

  • Link with Linear Regression

  • Dropout and Activation

  • Optimizers and Loss Functions

  • 1D and 2D Convolution

  • Why CNN for Images and speech?

  • Convolution Layer

  • Coding Convolution Layer

  • Learning Sharpening using single Convolution Layer in Tensor-Flow

  • Convolution

  • Pooling

  • Activation

  • Dropout

  • Batch Normalization

  • Object Classification

  • Creating Batch in Tensorflow and Normalize

  • Training MNIST and CIFAR datasets

  • Understanding a pre-trained Inception Architecture

  • Input Augmentation Techniques for Images

  • Finetuning last layers of CNN Model

  • Selecting appropriate Loss

  • Adding a new class in the Last Layer

  • Making a model Fully Convolutional for Deployment

  • Finetune Imagenet for Cats vs Dog Classification

  • Different types of problem in Objects

  • Difficulties in Object Detection and Localization

  • Fast RCNN

  • Faster RCNN

  • YOLO v1-v3

  • SSD

  • MobileNet

  • Image Compression Simple Autoencoder

  • Denoising Autoencoder

  • Variational Autoencoder and Reparematrization Trick

  • Robust Word Embedding using Variational Autoencoder

  • Evolution of Recurrent Structures

  • LSTM, RNN, GRU, Bi-RNN, Time-Dense

  • Learning a Sine Wave using RNN in Tensorflow

  • Creating Autocomplete for Harry Potter in Tensorflow

  • Generative vs Discrimative Models

  • Theory of GAN

  • Simple Distribution Generator in Tensorflow using MCMC (Markov Chain Monte Carlo)

  • DCGAN,WGANs for Images

  • InfoGANs, CycleGANs and Progressive GANs

  • Creating a GAN for generating Manga Art

  • Model Free Prediction

  • Monte Carlo Prediction and TD Learning

  • Model Free Control with REINFORCE and SARSA Learning

  • Assignment : Implementation of REINFORCE and SARSA Learning in Gridworld

  • Off policy vs On Policy Learning

  • Importance Sampling for Off Policy Learning

  • Q Learning

  • Understanding Deep Learning as Function Approximator

  • Theory of Behavioral Cloning and Deep Q Learning

  • Revisiting Point Collector Example in Unity and

  • Assignment : Training Cartpole Example via Deep Q Learning

  • Face Detection using Yolo-v3

  • Building Autocomplete Feature using RNNs

  • Real-time Depth Prediction and Pose Estimation

  • How is Deep Learning used in Autonomous Driver Assistant systems

  • Tips and Tricks for scaling and easy Deployment of Deep Learning Models

Deep Learning with Tensorflow Training FAQs


TensorFlow is one of the best libraries to implement Deep Learning. TensorFlow is a software library for numerical computation of mathematical expressions, using data flow graphs.

Nodes in the graph represent mathematical operations, while the edges represent the multidimensional data arrays (tensors) that flow between them. It was created by Google and tailored for Machine Learning. In fact, it is being widely used to develop solutions with Deep Learning. Machine learning is one of the fastest-growing and most exciting fields out there, and Deep Learning represents its true bleeding edge. Deep learning is primarily a study of multi-layered neural networks, spanning over a vast range of model architectures. Traditional neural networks relied on shallow nets, composed of one input, one hidden layer, and one output layer.

Deep-learning networks are distinguished from these ordinary neural networks having more hidden layers, or so-called more depth. These kinds of nets are capable of discovering hidden structures within unlabeled and unstructured data (i.e. images, sound, and text), which constitutes the vast majority of data in the world.

Hachion Deep learning with Tensorflow course is designed for all those who want to learn Deep Learning which would include an understanding of Deep Learning methods, Neural Networks, Deep Learning uses Tensorflow, Restricted Boltzmann Machines (RBM) and Autoencoders. The following professionals can go for this course:

  • Developers aspiring to be a 'Data Scientist'
  • Analytics Managers who are leading a team of analysts
  • Business Analysts who want to understand Deep Learning (ML) Techniques
  • Information Architects who want to gain expertise in Predictive Analytics
  • Professionals who want to captivate and analyze Big Data
  • Analysts wanting to understand Data Science methodologies

However, Deep learning is not just focused on one particular industry or skill set, it can be used by anyone to enhance their portfolio.

 Pre-requisites to take this course are:

  • Basic programming knowledge in Python
  • Concept of Arrays
  • Concepts about Machine Learning

We provide 100% job assistance to the Hachion students, once they complete the course. We also provide resume writing, mock interviews, and resume marketing services as part of our job assistance program. 

We offer three modes of training in Deep Learning with TensorFlow online training program.

  • Self Placed
  • Mentorship
  • Instructor-Led

This course is perfectly aligned to the current industry requirements and gives exposure to all latest techniques and tools. The course curriculum is designed by specialists in this field and monitored improved by industry practitioners on continual basis.

According to indeed, the average salary for "deep learning" ranges from approximately $85,738 per year for Research Scientist to $138,252 per year for Machine Learning Engineer

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