avinashsen707.github.io/post_39_45.html at master · avinashsen707/avinashsen707.github.io · GitHub

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
<!DOCTYPE html>
<html lang="en">

  <head>

    <meta charset="utf-8">
    <meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no">
    <meta name="description" content="">
    <meta name="author" content="">

    <title>Avinash Sen - Master Research Project Blog</title>

    <!-- Bootstrap core CSS -->
    <link href="vendor/bootstrap/css/bootstrap.min.css" rel="stylesheet">

    <!-- Custom fonts for this template -->
    <link href="vendor/font-awesome/css/font-awesome.min.css" rel="stylesheet" type="text/css">
    <link href='https://fonts.googleapis.com/css?family=Lora:400,700,400italic,700italic' rel='stylesheet' type='text/css'>
    <link href='https://fonts.googleapis.com/css?family=Open+Sans:300italic,400italic,600italic,700italic,800italic,400,300,600,700,800' rel='stylesheet' type='text/css'>

    <!-- Custom styles for this template -->
    <link href="css/clean-blog.min.css" rel="stylesheet">

  </head>

  <body>

    <!-- Navigation -->
    <nav class="navbar navbar-expand-lg navbar-light fixed-top" id="mainNav">
      <div class="container">
        <a class="navbar-brand" href="about.html">Avinash Sen</a>
        <button class="navbar-toggler navbar-toggler-right" type="button" data-toggle="collapse" data-target="#navbarResponsive" aria-controls="navbarResponsive" aria-expanded="false" aria-label="Toggle navigation">
          Menu
          <i class="fa fa-bars"></i>
        </button>
        <div class="collapse navbar-collapse" id="navbarResponsive">
          <ul class="navbar-nav ml-auto">
            <li class="nav-item">
              <a class="nav-link" href="index.html">Home</a>
            </li>
            <li class="nav-item">
              <a class="nav-link" href="about.html">About</a>
            </li>
            <li class="nav-item">
              <a class="nav-link" href="contact.html">Contact</a>
            </li>
          </ul>
        </div>
      </div>
    </nav>

    <!-- Page Header -->
    <header class="masthead" style="background-image: url('img/week_39_41.png')">
      <div class="overlay"></div>
      <div class="container">
        <div class="row">
          <div class="col-lg-8 col-md-10 mx-auto">
            <div class="post-heading">
              <h1>Week 33-34</h1>
              <h2 class="subheading">Online courses for Deep Learning.</h2>
              <span class="meta">Posted by
                <a href="about.html">Avinash Sen</a>
                on May 10, 2020</span>
            </div>
          </div>
        </div>
      </div>
    </header>

    <!-- Post Content -->
    <article>
      <div class="container">
        <div class="row">
          <div class="col-lg-8 col-md-10 mx-auto">
              <p>
                <p><b><font size="6">I. Deep Learning : Specialisation</font></b></p>
                Deep Learning is transforming multiple industries. This five-course specialization  helped me to understand Deep Learning fundamentals,
                apply them, and build a career in AI.
              </p>
              <center>
                <p><img class="img-fluid" src="img/deepspec.png" alt=""></p>
                <p><a href="https://www.deeplearning.ai/deep-learning-specialization/">Open in deeplearning.ai</a></p>
              </center>

              <p>
                <p><b><font size="5">1. Neural Networks and Deep Learning :</b></font></p>
                In this course, i  learned the foundations of deep learning. When i finished this class : -
                - Understood the major technology trends driving Deep Learning - Be able to build, train and apply fully connected deep neural networks.
                Knew how to implement efficient (vectorized) neural networks - Understood the key parameters in a neural network's architecture.
                This course also taught me how Deep Learning actually works, rather than presenting only a cursory or surface-level description.
                So after completing it, i am able to apply deep learning to a my own applications. This is the first course of the Deep Learning Specialization.
              </p>
              <center>
                <p><img class="img-fluid" src="img/cdl1.png" alt=""></p>
              </center>

              <p>
                <p><b><font size="5">2. Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization :</b></font></p>
                This is the second course of the Deep Learning Specialization. I  understood what drives performance in the deep learning process, and abled to more systematically get good results.
                Understood industry best-practices for building deep learning applications. - abled to effectively use the common neural network "tricks",
                including initialization, L2 and dropout regularization, Batch normalization, gradient checking, - abled to implement and apply a variety of optimization algorithms,
                such as mini-batch gradient descent, Momentum, RMSprop and Adam, and check for their convergence. - Understood new best-practices for the deep learning era of
                how to set up train/dev/test sets and analyze bias/variance - abled to implement a neural network in TensorFlow.
              </p>
              <center>
                <p><img class="img-fluid" src="img/cdl2.png" alt=""></p>
              </center>

              <p>
                <p><b><font size="5">3. Structuring Machine Learning Projects :</b></font></p>
                This is the third course in the Deep Learning Specialization. you will: - Understood how to diagnose errors in a machine learning system.
                - abled to prioritize the most promising directions for reducing error - Understood complex ML settings, such as mismatched training/test sets, and comparing to and/or surpassing human-level performance.
                - Studied how to apply end-to-end learning, transfer learning, and multi-task learning. This is a standalone course, and we can take this so long as you have basic machine learning knowledge.
              </p>
              <center>
                <p><img class="img-fluid" src="img/cdl3.png" alt=""></p>
              </center>

              <p>
                <p><b><font size="5">4. Convolutional Neural Networks :</b></font></p>
                This is the fourth course of the Deep Learning Specialization.-This course will taught me how to build convolutional neural networks and apply it to image data.
                - Understood how to build a convolutional neural network, including recent variations such as residual networks.
                - Knew how to apply convolutional networks to visual detection and recognition tasks.
                - Knew to use neural style transfer to generate art. - abled to apply these algorithms to a variety of image, video, and other 2D or 3D data.
              </p>
              <center>
                <p><img class="img-fluid" src="img/cdl4.png" alt=""></p>
              </center>

              <p>
                <p><b><font size="5">5. Sequence Models :</b></font></p>
                This is the fifth and last course of the Deep Learning Specialization. This course will teach you how to build models for natural language, audio, and other sequence data.
                - Understood how to build and train Recurrent Neural Networks (RNNs), and commonly-used variants such as GRUs and LSTMs. - abled to apply sequence models to natural language problems, including text synthesis.
                - abled to apply sequence models to audio applications, including speech recognition and music synthesis. This is the fifth and final course of the Deep Learning Specialization.
              </p>
              <center>
                <p><img class="img-fluid" src="img/cdl5.png" alt=""></p>
              </center>

              <center>
                <p><b><font size="5">Specialisation Certificate</b></font></p>
                <p><img class="img-fluid" src="img/cdl6.png" alt=""></p>
              </center>


              <p>
                <p><b><font size="6">II. TensorFlow in Practice</font></b></p>
                This Specialization taught me best practices for using TensorFlow, a popular open-source framework for machine learning.
              </p>
              <center>
                <p><img class="img-fluid" src="img/tfpractice.png" alt=""></p>
                <p><a href="https://www.deeplearning.ai/tensorflow-in-practice/">Open in deeplearning.ai</a></p>
              </center>
              <p>
                <p><b><font size="5">1. Introduction to TensorFlow for Artificial Intelligence, Machine Learning, and Deep Learning :</b></font></p>
                 This First course helps to use TensorFlow to implement those principles so that we can start building and applying scalable models to real-world problems.
              </p>
              <center>
                <p><img class="img-fluid" src="img/ctf1.png" alt=""></p>
              </center>

              <p>
                <p><b><font size="5">2. Convolutional Neural Networks in TensorFlow :</b></font></p>
                This second course teached me advanced techniques to improve the computer vision model you built in Course 1. This will explore how to work with real-world images in different shapes and sizes,
                visualize the journey of an image through convolutions to understand how a computer “sees” information, plot loss and accuracy, and explore strategies to prevent overfitting,
                including augmentation and dropouts. Finally, taught the transfer learning and how learned features can be extracted from models.
              </p>
              <center>
                <p><img class="img-fluid" src="img/ctf2.png" alt=""></p>
              </center>

              <p>
                <p><b><font size="5">3. Natural Language Processing in TensorFlow :</b></font></p>
                In this third course, I learned how to apply neural networks to solve natural language processing problems using TensorFlow. we’ll learn how to process and represent text through tokenization
                so that it’s recognizable by a neural network. Then introduced to new types of neural networks, including RNNs, GRUs and LSTMs, and how we can train them to understand the meaning of text.
                Finally, l learned how to train LSTMs on existing text to create original context.
              </p>
              <center>
                <p><img class="img-fluid" src="img/ctf3.png" alt=""></p>
              </center>

              <p>
                <p><b><font size="5">4. Sequences, Time Series and Prediction :</b></font></p>
                In this fourth course, i learned how to build time series models in TensorFlow. Firstly, implemented best practices to prepare time series data.
                After that studied how RNNs and 1D ConvNets can be used for prediction.
              </p>
              <center>
                <p><img class="img-fluid" src="img/ctf4.png" alt=""></p>
              </center>

              <center>
                <p><b><font size="5"></b>Specialisation Certificate</font></p>
                <p><img class="img-fluid" src="img/ctf5.png" alt=""></p>
              </center>

              <p>
                <p><b><font size="6">III. TensorFlow: Data and Deployment</font></b></p>
                Bringing a machine learning model into the real world involves a lot more than just modeling. This Specialization
                taught me how to navigate various deployment scenarios and use data more effectively to train your model.
              </p>
              <center>
                <p><img class="img-fluid" src="img/tfdeployement.png" alt=""></p>
                <p><a href="https://www.deeplearning.ai/tensorflow-data-and-deployment/">Open in deeplearning.ai</a></p>
              </center>
              <p>
                <p><b><font size="5">1. Browser-based Models with TensorFlow.js</b></font></p>
                This first course showed me how you can train and run machine learning models in any browser using TensorFlow.js.
                l learned techniques for handling data in the browser, and at the end you’ll build a computer vision project that recognizes and classifies objects from a webcam.
              </p>
              <center>
                <p><img class="img-fluid" src="img/ctd1.png" alt=""></p>
              </center>
              <p>
                <p><b><font size="5">2. Device-based Models with TensorFlow Lite</b></font></p>
                This second course teaches you how to run your machine learning models in mobile applications. l learned how to prepare models for a lower-powered,
                battery-operated devices, then execute models on both Android and iOS platforms. Finally,
                l explored how to deploy on embedded systems using TensorFlow on Raspberry Pi and microcontrollers.
              </p>
              <center>
                <p><img class="img-fluid" src="img/ctd2.png" alt=""></p>
              </center>
              <p>
                <p><b><font size="5">3. Data Pipelines with TensorFlow Data Services</b></font></p>
                In this third course, l used a suite of tools in TensorFlow to more effectively leverage data and train your model.
                l learned how to leverage built-in datasets with just a few lines of code, used APIs to control how to split the data,
                and process all types of unstructured data.
              </p>
              <center>
                <p><img class="img-fluid" src="img/ctd3.png" alt=""></p>
              </center>
              <p>
                <p><b><font size="5">4. Advanced Deployment Scenarios with TensorFlow</b></font></p>
                In this final course, l explored four different scenarios encounter when deploying models. l been introduced to TensorFlow Serving,
                a technology that lets you do inference over the web. l moved on to TensorFlow Hub, a repository of models that i can use for transfer learning.
                Then l used TensorBoard to evaluate and understand how my models work, as well as share mine model metadata with others. Finally,
                l explored federated learning and how i can retrain deployed models with user data while maintaining data privacy.
              </p>
              <center>
                <p><img class="img-fluid" src="img/ctd4.png" alt=""></p>
              </center>
              <center>
                <p><b><font size="5"></b>Specialisation Certificate</font></p>
                <p><img class="img-fluid" src="img/ctd5.png" alt=""></p>
              </center>


              <!-- Pager -->
              <div class="clearfix">
                <a class="btn btn-primary float-left" href="post_35_38.html">&larr;Previous Week </a>
                <a class="btn btn-primary float-right" href="post_46_48.html">Next Week &rarr;</a>
              </div>

          </div>
        </div>
      </div>
    </article>

    <hr>

    <!-- Footer -->
    <footer>
      <div class="container">
        <div class="row">
          <div class="col-lg-8 col-md-10 mx-auto">
            <ul class="list-inline text-center">
              <!-- <li class="list-inline-item">
                <a href="#">
                  <span class="fa-stack fa-lg">
                    <i class="fa fa-circle fa-stack-2x"></i>
                    <i class="fa fa-twitter fa-stack-1x fa-inverse"></i>
                  </span>
                </a>
              </li> -->
              <li class="list-inline-item">
                <a href="https://www.facebook.com/avinashsen707">
                  <span class="fa-stack fa-lg">
                    <i class="fa fa-circle fa-stack-2x"></i>
                    <i class="fa fa-facebook fa-stack-1x fa-inverse"></i>
                  </span>
                </a>
              </li>
              <li class="list-inline-item">
                <a href="https://github.com/avinashsen707">
                  <span class="fa-stack fa-lg">
                    <i class="fa fa-circle fa-stack-2x"></i>
                    <i class="fa fa-github fa-stack-1x fa-inverse"></i>
                  </span>
                </a>
              </li>
            </ul>
            <p class="copyright text-muted">Copyright 2020 &copy; avinashsen707.github.io</p>
          </div>
        </div>
      </div>
    </footer>

    <!-- Bootstrap core JavaScript -->
    <script src="vendor/jquery/jquery.min.js"></script>
    <script src="vendor/bootstrap/js/bootstrap.bundle.min.js"></script>

    <!-- Custom scripts for this template -->
    <script src="js/clean-blog.min.js"></script>

  </body>

</html>