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.
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)
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
Metrics ROC Curve, Precision Recall Curve
Evolution of Perceptrons, Hebbs Principle, Cat Experiment
Single layer NN
Backpropagation, Dynamic Programming
Mathematical Take on NN
Link with Linear Regression
Dropout and Activation
Optimizers and Loss Functions
1D and 2D Convolution
Why CNN for Images and speech?
Coding Convolution Layer
Learning Sharpening using single Convolution Layer in Tensor-Flow
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
Image Compression Simple 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
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
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:
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:
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.
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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