Inovi Technologies is the Best Artificial Intelligence course gives preparing in the aptitudes required for a profession in AI. You will ace TensorFlow, Machine Learning, and other AI ideas, in addition to the programming dialects expected to plan savvy operators, profound learning calculations and progressed counterfeit neural systems that utilization prescient investigation to explain continuous basic leadership problems.Top AI Training Institute In Noida , Delhi Technical master and an enthusiastic mentor has ability in the field of AI and Machine Learning, Deep Learning and Project Management, he has a demonstrated work record of conveying Technical Training in different innovations and spaces at the head associations.
Inovi Technologies offers the extensive AI (Artificial Intelligence) preparing that will assist you with working on the bleeding edge of man-made consciousness. As a component of the preparation you will ace the different parts of fake neural systems, directed and unsupervised learning, strategic relapse with neural system attitude, double characterization, vectorization, Python for scripting machine learning applications. Visit here for more info: https://www.inovitechnologies.com/
Multi-layered Neural Networks
Prologue to Multi Layer Network, Concept of Deep neural systems, Regularization. Multi-layer perceptron, limit and overfitting, neural system hyperparameters, rationale entryways, the various activation functions in neural systems like Sigmoid, ReLu and Softmax, hyperbolic capacities. Backpropagation, combination, forward engendering, overfitting, hyperparameters.
Preparing Of Neural Networks
The different strategies utilized in preparing of fake neural systems, inclination plunge rule, perceptron learning rule, tuning learning rate, stochastic process, enhancement methods, regularization procedures, relapse methods Lasso L1, Ridge L2, vanishing angles, exchange learning, unsupervised pre-preparing, Xavier introduction, vanishing slopes.
Profound Learning Libraries
How Deep Learning Works, Activation Functions, Illustrate Perceptron, Training a Perceptron, Important Parameters of Perceptron,Multi-layer Perceptron What is Tensorflow, Introduction to TensorFlow open source programming library for planning, building and preparing Deep Learning models, Python Library behind TensorFlow, Tensor Processing Unit (TPU) programmable AI quickening agent by Google,Tensorflow code-fundamentals, Graph Visualization, Constants, Placeholders, Variables, Step by Step – Use-Case Implementation, Keras.
Prologue to Keras API
Keras abnormal state neural system for taking a shot at best of TensorFlow, characterizing complex multi-yield models, making models utilizing Keras, consecutive and practical arrangement, bunch standardization, conveying Keras with TensorBoard, neural system preparing process customization.
TFLearn API for TensorFLow
Actualizing neural systems utilizing TFLearn API, characterizing and making models utilizing TFLearn, sending TensorBoard with TFLearn.
DNN: Deep Neural Networks
Mapping the human personality with Deep Neural Networks, the different building squares of Artificial Neural Networks, the design of DNN, its building hinders, the idea of support learning in DNN, the different parameters, layers, initiation capacities and streamlining calculations in DNN.
CNN: Convolutional Neural Networks
What is a Convolutional Neural Network, understanding the design of CNN, utilize instances of CNN, what is a pooling layer, how to imagine utilizing CNN, how to calibrate a Convolutional Neural Network, what is Transfer Learning and understanding Recurrent Neural Networks,feature maps, Kernel channel, pooling, sending convolutional neural system in TensorFlow
RNN: Recurrent Neural Networks
Introduction to RNN Model, Application utilize instances of RNN, Modeling arrangements, Training RNNs with Backpropagation, Long Short-Term memory (LSTM), Recursive Neural Tensor Network Theory, Recurrent Neural Network Model, fundamental RNN cell, unfurled RNN, preparing of RNN, dynamic RNN, time-arrangement expectations.
GPU in Deep Learning
Prologue to GPUs and how they contrast from CPUs, the significance of GPUs in preparing Deep Learning Networks, the forward pass and in reverse pass preparing system, the GPU constituent with easier center and simultaneous equipment.
Autoencoders and Restricted Boltzmann Machine (RBM)
Prologue to RBM and autoencoders, conveying it for profound neural systems, communitarian separating utilizing RBM, highlights of autoencoders, utilizations of autoencoders.
Our More Course Are:
2. Data Scientist
6. MEAN Stack
7.RPA(Robotic Process Automation)
11. Artificial Intelligence
Mobile No. 8810643463, 9354482334
Phone No. 91-120-4213880
Email- [email protected]
Address. F7 Sector-3 Noida UP 201301 India.